Clinical Molecular Diagnostics 9789811610363, 9789811610370

Table of contents :
Foreword
Coordinators
Preface
Acknowledgments
Contents
About the Editors and Contributors
About the Editors
Editorial Board
Contributors
Part I: Principles of Clinical Molecular Diagnostics
1: Molecules of Disease and Their Detection Methods
1.1 Overview
1.2 Molecular Mechanism of Diseases
1.3 Nucleic Acid Detection Methods
1.3.1 Nucleic Acid Amplification Technology
1.3.2 Sequencing Technology
1.3.3 Nucleic Acid Hybridization Technology
1.3.4 Chip Technology
1.3.5 Biosensing Technology
1.4 Protein Detection Methods
1.4.1 Spectrum Technology
1.4.2 Protein Chip Technology
1.4.3 Labeled Immunoassay
1.4.4 Mass Spectrometric Technique
1.5 Future Trends
References
2: Assay Performance Evaluation
2.1 Precision
2.1.1 Terminology and Definitions
2.1.2 Overview of the Precision Evaluation Process (Fig. 2.1)
2.1.3 Features of the EP5-A2 Program
2.1.4 EP5-A2 Experimental Protocol and Requirements
2.1.4.1 Experimental Preparation
Experimental Sample
2.1.4.2 Experimental Method
2.1.4.3 Quality Control
2.1.5 Data Collection, Processing, and Statistical Analysis
2.1.5.1 Experimental Data Record
2.1.5.2 Outlier Test
2.1.5.3 Repeatability Estimate
2.2 Accuracy
2.2.1 Definitions
2.2.2 Features of the EP9-A2 Program
2.2.3 EP9-A2 Experimental Protocol and Requirements
2.2.3.1 Experimental Preparation
Sample Preparation
Comparison Method Selection
2.2.3.2 Experimental Method
2.2.3.3 Quality Control
2.2.4 Simple Accuracy Evaluation Plan
2.2.4.1 Comparison of Patient Sample Results to Those of Another Procedure
2.2.4.2 Method of Setting Reference Materials
Sources of Reference Materials
Procedure for Demonstration of Accuracy with Reference Materials
2.3 Sensitivity
2.3.1 Definitions
2.3.2 Discussion of Several Common Terms
2.3.3 Lower Limit of Linear Range (LLR), Biological Limit of Detection (BLD), and Functional Sensitivity (FS)
2.3.3.1 Lower Limit of Linear Range (LLR)
2.3.3.2 Biological Limit of Detection (BLD)
2.3.3.3 Functional Sensitivity (FS)
2.3.3.4 Experimental Precautions
Blank Sample
Detection Limit Sample
Time Required for the Experiment
2.3.4 Limits of Blank, Limits of Detection, and Limits of Quantitation
2.3.4.1 Overview
2.3.4.2 General Method for Determining the Limits of Blank (LoB)
2.4 Analytical Measurement Range
2.4.1 Definitions
2.4.2 EP6-A Protocol and Requirements
2.4.2.1 Experimental Requirements
Device Familiarization Period
Duration of the Experiment
Specimen of the Experiment
Number of Samples
Matrix Effects
Selection of Materials Used to Supplement Samples
Analyte Range
Sample Preparation and Value Assignment
2.4.2.2 Analytical Sequence
2.4.2.3 Preliminary Data Check
Outlier Inspection
Determination of the Linear Range
Degree of Nonlinearity
Considerations for Random Error
2.5 Variation Factors of Pre-analysis
2.5.1 Collection, Transport, and Preservation of Nucleic Acid Test Specimens
2.5.1.1 Preparation of Specimen Collection Site
2.5.1.2 Type and Collection of Specimens
2.5.1.3 Sampling and Transport Containers
2.5.1.4 Anti-pollution in Specimen Collection
2.5.1.5 Evaluation of Sampling Quality
References
3: Establishment of Biological Reference Interval
3.1 Biological Reference
3.1.1 Definitions and Terms
3.1.2 Clarifications
3.2 Establishment of Biological Reference Interval
3.2.1 Protocol Outline for Obtaining Reference Values and Establishing Reference Intervals
3.2.1.1 New Analyte or Analytical Method
3.2.1.2 Previously Measured Analyte
3.2.2 Selection of Reference Individuals
3.2.2.1 Exclusion Criteria
3.2.2.2 Partitioning Criteria
3.2.2.3 Selection of Reference Individuals
3.2.2.4 Sample Questionnaire
3.2.3 Pre-analytical and Analytical Considerations
3.2.4 Analysis of Reference Values
3.2.4.1 Minimum Number of Reference Values
3.2.4.2 Treatment of Outlying Observations
3.2.4.3 Partitioning of Reference Values
3.3 Verification of Biological Reference Interval
3.4 Description of Biological Reference Interval
3.4.1 Laboratory Presentation
3.4.2 Manufacturer Presentation
References
4: Ethics: Informed Consent, Patient Privacy
4.1 Overview
4.2 Informed Consent
4.2.1 Challenges
4.2.2 Regulations and Recommendations
4.2.3 What Should Be Included in the Informed Consent?
4.3 Patient Privacy and Confidentiality
4.3.1 Challenges
4.3.2 Regulations and Recommendations
4.3.3 How to Protect Patient Privacy?
4.4 Conclusion
References
5: Bioinformatics
5.1 Overview
5.1.1 Concept and Background
5.1.2 Research Categories
5.1.3 Common International Bioinformatics Centers
5.1.4 Common Bioinformatics Database
5.2 Biological Sequence Analysis
5.2.1 Sequence Analysis
5.2.2 Multiple Sequence Analysis
5.2.3 Molecular Polygenetic Tree
5.2.4 Comparative Genomics
5.3 Transcriptomics Data Analysis
5.3.1 Gene Expression Profile Analysis
5.3.2 Functional Enrichment Analysis
5.3.3 Timing Analysis
5.3.4 Gene Co-expression Network Analysis
5.3.5 Analysis of Transcriptional Regulation
5.4 Protein Structure Analysis
5.4.1 Protein Structure Prediction
5.4.2 Protein-Protein Interaction
5.4.3 Protein Function Prediction
5.5 Bioinformatics and Precision Medicine
5.5.1 Bioinformatics and Precision Medical Diagnosis
5.5.1.1 Genetic Testing
5.5.1.2 Detection of Pathogenic Microorganisms
5.5.2 Bioinformatics and Precision Medical Prevention and Treatment
5.5.3 Bioinformatics and the Future of Precision Medicine
References
6: Report and Consultation
6.1 Overview
6.2 Genetic Variation and Description
6.2.1 The Level of Sequence Variation
6.2.2 Content of the Variant Description
6.2.3 Types of Variant Sequences
6.2.4 Expression of Variant Types Specific Abbreviations Are Used to Describe Different Types of Sequence Variations
6.2.5 Reference Sequence
6.3 Naming Rules
6.3.1 Specific Rules for DNA Levels
6.3.2 Detailed Rules for RNA Levels
6.3.3 Detailed Rules of Protein Levels
6.3.3.1 Amino Acid Coding
6.3.3.2 Silent Changes
6.3.3.3 Substitutions, Missense Changes
6.3.3.4 Amino Acid Deletion
6.3.3.5 Frameshift Mutation
6.3.3.6 Amino Acid Insertion, Repeat
6.3.4 Gene Pharmacology Genotype Terminology
6.3.5 Other
6.4 Application of Gene Mutation in Disease Diagnosis
6.4.1 Genetic Variation and Genetic Diagnosis
6.4.2 Application of Genetic Variation Detection in Disease Diagnosis
6.4.2.1 Molecular Diagnosis of Mendelian Genetic Disease
6.4.2.2 Prenatal Diagnosis and Prenatal and Postnatal Care
6.4.2.3 Molecular Genetic Testing of Complex Diseases
6.4.2.4 Diagnosis and Treatment of Tumors
6.4.2.5 Detection of Pathogenic Microorganisms
6.5 What Is Involved Before the Clinical Report
6.5.1 Content Covered by the Clinical Report
6.5.1.1 The Test Report Should Include the Following
6.5.1.2 DNA Sequencing Reports and Explanations Should Include
6.5.1.3 Reports and Interpretations of Whole-Exon or Whole-Genome Next-Generation Sequencing Should also Include the Following
6.5.2 Clinical Interpretation of the Data
6.5.3 Clinical Molecular Diagnostic Testing Process and Precautions
6.5.3.1 Clinical Molecular Diagnostic Testing Process
6.5.3.2 Precautions for Clinical Reports
6.6 Reporting Model and Case Analysis
6.6.1 Tumor Molecular Diagnosis Report
6.6.1.1 Introduction to Non-small Cell Lung Cancer
6.6.1.2 Methods and Test Items for Clinical Molecular Diagnosis of Non-small Cell Lung Cancer
Source of NSCLC Target Molecule Detection Project
Detection Method of NSCLC Target Molecule
For Example
6.6.2 Molecular Diagnosis Report of Infectious Diseases
6.6.2.1 Introduction to Hepatitis B
6.6.2.2 Method and Test Item for Clinical Molecular Diagnosis of Hepatitis B Virus
Project Source
Detection Method
For Example
6.6.3 Noninvasive Prenatal Screening Results Report
6.6.3.1 Introduction to Noninvasive Prenatal Screening
6.6.3.2 Noninvasive Prenatal Screening Method and Test Project for Clinical Molecular Diagnosis
6.6.3.3 High Throughput Sequencing
For Example
6.6.4 Genetic Disease Diagnosis Report
6.6.4.1 Clinical Molecular Diagnosis of G6PD Deficiency
Introduction to G6PD Deficiency
Methods and Test Items for Clinical Molecular Diagnosis of G6PD Deficiency
Source of G6PD Deficiency Molecular Testing Project
For Example
References
7: Factors Associated with Variation
7.1 The Concept
7.2 Sampling
7.2.1 Specimen Collection
7.2.1.1 Time of Collection
7.2.1.2 Method of Collection
Body Position and Part in Blood Collection
Cuff
Infusion
7.2.1.3 Volume of Specimen
7.2.1.4 Precautions for Sample Collection
Collect Representative Specimens
Proper Use of Anticoagulant
Avoid Specimen Hemolysis and Container Contamination
Avoid Collecting Blood During Infusion
7.2.2 Sample Delivery and Preservation
7.2.2.1 Sample Delivery
Delivery Principle
Specially Assigned People
Receiving
7.2.2.2 Specimen Preservation
7.3 Biological Factors
7.3.1 Age
7.3.1.1 Neonatal
7.3.1.2 Children
7.3.1.3 The Elderly
7.3.2 Gender
7.3.3 Season
7.3.4 Altitude
7.3.5 Biological Cycle
7.3.6 Pregnancy
7.3.7 Lifestyle
7.4 Drugs
7.4.1 Interference of Drugs on Routine Inspection in Clinic
7.4.1.1 The Impact on Routine Blood Tests Results
7.4.1.2 The Impact on Routine Urine Test Results
7.4.1.3 The Impact on Routine Stool Test Results
7.4.2 Interference of Drugs on Clinical Biochemical Tests
7.4.2.1 Drugs Affecting Enzyme Assay
7.4.2.2 Drugs Affecting Blood Sugar Assay
7.4.2.3 Drugs Affecting Protein Assay
7.4.2.4 Drugs Affecting Lipid Assay
7.4.2.5 Drugs Affecting Electrolyte Assay
References
8: Quality Control and Quality Assurance
8.1 Overview
8.2 Laboratory Setting and Contamination Control
8.2.1 Laboratory Setting
8.2.2 Facilities
8.2.3 Laboratory Practices
8.2.4 Chemical and Enzymatic Controls
8.3 Internal Quality Control
8.3.1 Quantitative Tests
8.3.2 Qualitative Tests
8.4 Quality Assessments
8.4.1 Quality Indicators
8.4.2 External Quality Assessment
8.4.3 Auditing Program
8.5 Quality Control Material
8.6 Verification and Validation
8.7 The Process of Detection and Quality Control
8.7.1 Pre-analytic Phase
8.7.2 Analytic Phase
8.7.3 Post-analytical Phase
8.8 Personnel
8.9 Quality Control of Laboratory Equipment
8.10 Reagents and Consumables Quality Control
8.11 Quality Improvement
8.12 Document Control
8.13 Conclusion
References
9: Precision Medicine
9.1 Evidence-Based Laboratory Medicine
9.2 Translational Medicine of Molecular Diagnostic Tests
9.3 Molecular Diagnostic Tests in Personalized Medicine
9.4 The Application of Pharmacogenomics
9.5 Companion Diagnostics
References
Part II: Molecular Biomarkers and Signals from Diseased Functional Organ
10: Molecules in Body Systems
10.1 Overview
10.2 Four Major Families of Small Organic Molecules
10.2.1 Sugars Provide an Energy Source for Cells and Are the Subunits of Polysaccharides
10.2.2 Fatty Acids Are Precursors for Phospholipids and Other Membrane Components
10.2.3 Amino Acids Are the Subunits of Proteins
10.2.4 Nucleotides Are the Subunits of DNA and RNA
10.3 The Chemistry of Cells Is Dominated by Macromolecules with Remarkable Properties
10.3.1 Carbohydrates
10.3.2 Lipids
10.3.3 Nucleic Acids
10.3.4 Proteins
References
11: Molecules in Signal Pathways
11.1 Overview
11.2 Functions
11.2.1 The Change of Signal Transduction Molecules Is the Basis of Signal Transduction
11.2.1.1 Signal Transduction Molecules Constitute the Network of Signal Transduction Pathways
11.2.1.2 The Change in the Conformation and Activity of Signal Transduction Molecules Is the Basis of Signal Transduction
11.2.1.3 Changes of Intracellular Signal Transduction Molecule Content and Translocation Are Important Ways of Signal Transduction Regulation
11.2.2 Signal Transduction Molecules Are Important Targets for Drug Action
11.3 Signal Pathways
11.3.1 Apoptosis
11.3.1.1 The BCL-2 Family
11.3.1.2 Caspases
11.3.1.3 Inhibitors of Apoptotic Proteins
11.3.2 PI3K/AKT/mTOR Signal Pathway
11.3.2.1 Phosphatidylinositide 3 Kinase
11.3.2.2 Akt
11.3.2.3 Mammalian Target of Rapamycin
11.3.3 Wnt Signal Pathway
11.3.3.1 Wnt
11.3.3.2 Adenomatous Polyposis Coli
11.3.3.3 β-Catenin
11.3.3.4 c-Myc
11.3.4 Mitogen-Activated Protein Kinase Signal Pathway
11.3.4.1 Extracellular Signal-Regulated Kinase 1/2
11.3.4.2 c-Jun Terminal Kinases
11.3.4.3 p38
11.3.5 JAK/STAT Signal Pathway
11.3.5.1 JAKs
11.3.5.2 STATs
11.3.6 Angiogenesis
11.3.7 Notch Signal Pathway
11.4 Important Molecules
11.4.1 PD-1/PD-L1
11.4.2 HIF-1
11.4.3 p53
11.4.4 Tumor Specific Protein 70
References
12: Endocrine and Metabolism
12.1 Overview
12.2 Insulin
12.2.1 Sources and Characteristic
12.2.2 Methods
12.2.3 Reference Interval for Healthy Persons
12.2.4 Clinical Significance
12.2.4.1 The Application in the Diagnosis and Treatment of Diabetes
12.2.4.2 The Application in the Hypoglycemia Syndrome
12.2.4.3 The Application in the Diagnosis of Insulin Beta Cell Tumor
12.2.4.4 The Application of Other Diseases in the Diagnosis and Treatment
12.3 C-Peptide
12.3.1 Source and Characteristics
12.3.2 Methods
12.3.3 Reference Interval for Healthy Persons
12.3.4 Clinical Significance
12.4 Glycosylated Hemoglobin (HbA1c)
12.4.1 Sources and Characteristics
12.4.2 Methods
12.4.3 Reference Interval for Healthy Persons
12.4.4 Clinical Significance
12.4.5 Conclusions and Prospects
12.5 IGFBP-2
12.5.1 Sources and Characteristics
12.5.2 Methods
12.5.3 Reference Interval for Healthy Persons
12.5.4 Clinical Significance
12.5.5 Conclusions and Prospects
12.6 Thyroid Markers
12.6.1 The Function of Thyroid
12.6.2 Laboratory Evaluation of Thyroid
12.6.2.1 Tests That Asses the Hypothalamic-Pituitary-Thyroid Axis
T3 and T4
TSH and TRH
FT4 and FT3
12.6.2.2 Tests for Evaluation of Thyroid Autoimmunity
TG-Ab and TPO-Ab
TR-Ab
12.6.3 Conclusions and Prospects
12.7 Adrenal Gland Markers
12.7.1 Structure of Adrenal Gland
12.7.2 Function of Adrenal Gland
12.7.3 Laboratory Examination of Adrenal Gland
12.7.3.1 Tests That Assess the Level of Adrenal Medullary Hormone
12.7.3.2 Tests That Asses the Level of Adrenocortical Hormone
Examinations of Glucocorticoids and Their Metabolites in Blood, Urine, and Saliva
Examination of ATCH and N-POMC in Plasma
Dynamic Functional Test
Dexamethasone Inhibition Test
12.7.3.3 Genetic Testing
12.7.4 Conclusion
12.8 The Relevant Progress
References
13: Immune System
13.1 Overview
13.1.1 Autoimmune Diseases
13.1.2 Hypersensitive Diseases
13.1.2.1 Markers of Type I Hypersensitivity Diseases
13.1.2.2 Markers of Type II Hypersensitivity Diseases
13.1.2.3 Markers of Type III Hypersensitivity Diseases
13.1.2.4 Markers of Type IV Hypersensitivity Diseases
13.2 Tissue-specific Autoantibodies
13.2.1 Anti-intrinsic Factor Antibody (AIFA)
13.2.2 Anti-parietal Cell Antibody
13.2.3 Anti-smooth Muscle Antibody
13.2.4 Anti-liver/Kidney Microsome Antibody
13.2.5 Anti-pituitary Antibody
13.2.6 Anti-thyroid Antibody
13.2.7 Anti-glutamic Acid Decarboxylase Antibody
13.2.8 Anti-islet Cell Antibody
13.2.9 Anti-insulin Antibody
13.2.10 Anti-adrenocortical Antibody
13.2.11 Anti-glomerular Basement Membrane Antibody
13.3 Systemic Autoantibodies
13.3.1 Anti-mitochondrial Antibody
13.3.2 Human Anti-globulin Antibody
13.3.3 Anti-cold Agglutinin Antibody
13.3.4 Anti-nuclear Antibody
13.3.5 Anti-extractable Nuclear Antibody
13.3.5.1 Anti-Smith Antibody
13.3.5.2 Anti-SSA/Ro Antibody and Anti-SSB/La Antibody
13.3.5.3 Anti-RNP Antibody
13.3.5.4 Anti-topoisomerase Antibody (ATA)/Anti-Scl-70 Antibody
13.3.6 Anti-cardiolipin Antibody
13.3.7 Anti-DNA Antibody
13.3.7.1 Anti-dsDNA Antibody
13.3.7.2 Anti-ssDNA Antibody
13.3.8 Anti-histone Antibody
13.3.9 Anti-centromere Antibody
13.3.10 Anti-p62 Antibody (AP62A)/Anti-sp100 Antibody/Anti-glycoprotein-210 Antibody (AGPA, Anti-gp210, Anti-nup210, Anti-np210)
13.3.11 Rheumatoid Factor
13.3.12 Anti-citrullinated Peptide Antibody (Anti-CCP Antibody)
13.3.13 Anti-glucose-6-Phosphate Isomerase (Anti-G6PI)
13.3.14 Antineutrophil Cytoplasmic Antibody
13.3.15 Rh Antibody
13.3.16 Anti-erythrocyte Antibody
13.3.17 Anti-HLA Antibody
13.3.18 Prospects for Autoantibodies
13.4 Immunoglobulin E
13.5 Relevant Activated Cell Markers
13.5.1 Mast Cells
13.5.2 Eosinophil Activation Marker
13.5.3 The Activation Markers of Basophil and Neutrophil
13.5.4 T Lymphocyte Immunophenotype
13.6 Complement System
13.7 Circulation Immune Complex
13.8 Other Markers
13.8.1 Cytokines
13.8.2 Proteins and Enzymes
13.9 The Relevant Progress
References
14: Lipoproteins
14.1 Overview
14.1.1 Basic Concepts
14.1.2 Functions and Metabolic Pathways of Various Lipoproteins
14.1.2.1 Chylomicrons Transport Exogenous Triglycerides and Cholesterol
14.1.2.2 Very Low-Density Lipoproteins Transport Endogenous Triglycerides
14.1.2.3 Low-Density Lipoprotein Transports Endogenous Cholesterol
14.1.2.4 High-Density Lipoprotein Transports Cholesterol in Reverse Fashion
14.1.3 Lipoprotein Metabolic Disturbances
14.1.3.1 Hyperlipoproteinemia
Primary Hyperlipoproteinemia
Hyperlipoproteinemia
Secondary Hyperlipoproteinemia
Clinical Manifestations of Hyperlipoproteinemia
Diagnostic Criteria for Hyperlipoproteinemia
14.1.3.2 Hypolipoproteinemia
14.1.3.3 Lipoprotein Metabolic Disturbance and Atherosis
Factors Leading to Atherosis
Lipoprotein Remnants
Modified LDL
Type B LDL
LP(a)
14.2 Total Cholesterol
14.2.1 Sources and Characteristics
14.2.2 Detection Methods and Reference Values of Healthy People
14.2.2.1 Detection Methods
14.2.2.2 Reference Values of Healthy Individuals
14.2.2.3 Clinical Significance
14.2.2.4 Related Progress
14.3 Triglycerides
14.3.1 Sources and Characteristics
14.3.2 Detection Methods and Reference Values of Healthy Individuals
14.3.2.1 Detection Methods
14.3.2.2 Reference Values of Healthy Individuals
14.3.2.3 Clinical Significance
14.3.2.4 Related Progress
14.4 Free Fatty Acids
14.4.1 Sources and Characteristics
14.4.2 Detection Methods and Reference Values of Healthy Individuals
14.4.2.1 Detection Methods
14.4.2.2 Reference Values of Healthy Individuals
14.4.2.3 Clinical Significance
14.5 Phospholipids
14.5.1 Sources and Characteristics
14.5.2 Detection Methods and Reference Values of Healthy People
14.5.2.1 Detection Methods
14.5.2.2 Reference Values of Healthy Individuals
14.5.2.3 Clinical Significance
14.6 Lipoprotein
14.6.1 High-Density Lipoprotein
14.6.1.1 Sources and Characteristics
14.6.1.2 Detection Methods and Reference Values of Healthy Individuals
Detection Methods
14.6.1.3 Reference Values of Healthy Individuals
14.6.1.4 Clinical Significance
14.6.1.5 Related Progress
14.6.2 Low-Density Lipoprotein
14.6.2.1 Sources and Characteristics
14.6.2.2 Detection Methods and Reference Values of Healthy Individuals
Detection Methods
Reference Values of Healthy Individuals
14.6.2.3 Clinical Significance
14.6.2.4 Related Progress
14.6.3 Small and Dense Low-Density Lipoprotein
14.6.3.1 Sources and Characteristics
14.6.3.2 Detection Methods and Reference Values of Healthy Individuals
Detection Methods
Reference Values of Healthy Individuals
14.6.3.3 Clinical Significance
14.6.3.4 Related Progress
14.6.4 Lipoprotein (a)
14.6.4.1 Sources and Characteristics
14.6.4.2 Detection Methods and Reference Values of Healthy Individuals
Detection Methods
Reference Values of Healthy Individuals
14.6.4.3 Clinical Significance
14.6.4.4 Related Progress
References
15: Transport and Carrier Proteins
15.1 Overview
15.2 Apolipoprotein AI (AI, ApoAI)
15.2.1 Sources and Characteristics
15.2.2 Detection Methods and Reference Values of Healthy Individuals
15.2.2.1 Detection Methods
15.2.2.2 Reference Values of Healthy Individuals
15.2.3 Clinical Significance
15.2.4 Related Progression
15.3 Apolipoprotein B
15.3.1 Sources and Characteristics
15.3.2 Detection Methods and Reference Values of Healthy Individuals
15.3.2.1 Detection Methods
15.3.2.2 Reference Values of Healthy Individuals
15.3.3 Clinical Significance
15.3.4 Related Progress
15.4 Apolipoprotein B/Apolipoprotein AI Ratio (ApoB/ApoAI Ratio)
15.4.1 Sources and Characteristics
15.4.2 Detection Methods and Reference Values of Healthy Individuals
15.4.2.1 Detection Methods
15.4.2.2 Reference Values of Healthy Individuals
15.4.3 Clinical Significance
15.4.4 Related Progress
15.5 Apolipoprotein CII and CIII
15.5.1 Sources and Characteristics
15.5.2 Detection Methods and Reference Values of Healthy Individuals
15.5.2.1 Detection Method
15.5.2.2 Reference Values of Healthy Individuals
15.5.3 Clinical Significance
15.5.4 Related Progress
15.6 Apolipoprotein E
15.6.1 Sources and Characteristics
15.6.2 Detection Methods and Reference Values of Healthy Individuals
15.6.2.1 Detection Methods
15.6.2.2 Reference Values of Healthy Individuals
15.6.3 Clinical Significance
15.6.4 Related Progress
15.7 Apolipoprotein H
15.7.1 Sources and Characteristics
15.7.2 Detection Methods
15.7.2.1 Detection Methods
15.7.2.2 Reference Value of Healthy Individuals
15.7.3 Clinical Significance
15.7.4 Research Progress
15.8 Apolipoprotein M
15.8.1 Sources and Characteristics
15.8.2 Detection Methods and Reference Values of Healthy Individuals
15.8.2.1 Detection Methods
15.8.2.2 Reference Values of Healthy Individuals
15.8.3 Clinical Significance
15.8.4 Research Progress
References
16: Coagulation and Fibrinolysis
16.1 Overview
16.2 Fibrinogen
16.2.1 Sources and Characteristics
16.2.2 Detection Method and Reference Range
16.2.3 Clinical Significance
16.3 D-Dimer
16.3.1 Sources and Characteristics
16.3.2 Detection Method
16.3.3 Healthy Person Reference Range
16.3.4 Clinical Significance
16.4 Fibrinogen/Fibrin Degradation Product
16.4.1 Sources and Characteristics
16.4.2 Detection Method
16.4.3 Healthy Person Reference Range
16.4.4 Clinical Significance
16.5 Protein C
16.5.1 Sources and Characteristics
16.5.2 Detection Method
16.5.3 Healthy Person Reference Range
16.5.4 Clinical Significance
16.6 Protein S
16.6.1 Sources and Characteristics
16.6.2 Detection Method
16.6.3 Healthy Person Reference Range
16.6.4 Clinical Significance
16.7 Antithrombin
16.7.1 Sources and Characteristics
16.7.2 Detection Method
16.7.3 Healthy Person Reference Range
16.7.4 Clinical Significance
16.8 Plasminogen
16.8.1 Sources and Characteristics
16.8.2 Detection Method
16.8.3 Healthy Person Reference Range
16.8.4 Clinical Significance
16.9 α2-Plasmin Inhibitor
16.9.1 Sources and Characteristics
16.9.2 Detection Method
16.9.3 Healthy Person Reference Range
16.9.4 Clinical Significance
16.10 Von Willebrand Factor
16.10.1 Sources and Characteristics
16.10.2 Detection Method
16.10.3 Healthy Person Reference Range
16.10.4 Clinical Significance
16.11 Thrombomodulin
16.11.1 Sources and Characteristics
16.11.2 Detection Method and Healthy Person Reference Range
16.11.3 Clinical Significance
16.12 Plasminogen Activator Inhibitor Type 1
16.12.1 Sources and Characteristics
16.12.2 Detection Method
16.12.3 Healthy Person Reference Range
16.12.4 Clinical Significance
16.13 Tissue Factor Pathway Inhibitor
16.13.1 Sources and Characteristics
16.13.2 Detection Method
16.13.3 Healthy Person Reference Range
16.13.4 Clinical Significance
16.14 Laboratory Tests for Clotting Factor Deficiency Screening
16.14.1 Prothrombin Time
16.14.1.1 Sources and Characteristics
16.14.1.2 Detection Method
16.14.1.3 Healthy Person Reference Range
16.14.1.4 Clinical Significance
16.14.2 Activated Partial Thromboplastin Time
16.14.2.1 Sources and Characteristics
16.14.2.2 Detection Method
16.14.2.3 Healthy Person Reference Range
16.14.2.4 Clinical Significance
16.14.3 Thrombin Time
16.14.3.1 Sources and Characteristics
16.14.3.2 Detection Method
16.14.3.3 Healthy Person Reference Range
16.14.3.4 Clinical Significance
16.14.4 Coagulation Factor Assay
16.14.4.1 Sources and Characteristics
16.14.4.2 Detection Method
16.14.4.3 Healthy Person Reference Range
16.14.4.4 Clinical Significance
16.15 Molecular Diagnosis of Inherited Bleeding Disorders
16.15.1 Molecular Diagnosis of Hemophilia A and Hemophilia B
16.15.2 Molecular Diagnosis of Von Willebrand Disease
16.15.3 Molecular Diagnosis of Rare Bleeding Disorders
16.16 Related Progress
References
17: Cardiovascular System
17.1 Overview
17.2 Myocardial Damage Markers
17.2.1 Cardiac Troponin (cTn)
17.2.2 Myoglobin (Mb)
17.2.3 Creatine Kinase Isoenzyme MB (CK-MB)
17.2.4 Heart Type-Fatty Acid Binding Protein (H-FABP)
17.2.5 Copeptin
17.3 Myocardial Ischemia Markers
17.3.1 Ischemic Modified Albumin (IMA)
17.4 Heart Function Markers
17.4.1 Brain Natriuretic Peptide (BNP) or NT-proBNP
17.4.2 Soluble Suppression of Tumorigenicity-2 (sST2)
17.4.3 Adrenomedullin (ADM)
17.5 Inflammatory Markers
17.5.1 C-Reactive Protein (CRP)
17.5.2 Interleukin-6 (IL-6)
17.6 Markers That Predict Heart-Risk Events
17.6.1 Homocysteine (Hcy)
17.6.2 Lipoprotein-Associated Phospholipase A2 (Lp-PLA2)
17.6.3 Myeloperoxidase (MPO)
17.7 Other Markers
17.7.1 MicroRNAs
17.7.2 KCNQ1
17.7.3 SCN5A
17.7.4 RYR2
17.7.5 KCNE1
17.7.6 KCNH2
References
18: Pregnancy
18.1 Overview
18.2 Human Chorionic Gonadotropin (hCG)
18.2.1 Resource and Characteristics
18.2.2 Detection Methods
18.2.3 Reference Range
18.2.4 Clinical Application
18.2.4.1 Diagnosis and Monitor Normal/Abnormal Pregnancy
18.2.4.2 Diagnosis of Gestational Trophoblastic Disease (GTD)
18.2.4.3 Marker for Down’s Syndrome Screening
18.3 Alpha-Fetoprotein (AFP)
18.3.1 Resource and Characteristics
18.3.2 Detection Methods
18.3.3 Reference Range
18.3.4 Clinical Applications
18.3.4.1 Prenatal Diagnosis
18.3.4.2 Diagnosis and Monitoring of Hepatocellular Carcinoma and Other Germ Cell Tumors
18.4 Pregnancy-Associated Plasma Protein-A (PAPP-A)
18.4.1 Resource and Characteristics
18.4.2 Detection Methods
18.4.3 Reference Range
18.4.4 Clinical Applications
18.5 Inhibin
18.5.1 Resource and Characteristics
18.5.2 Detection Methods
18.5.3 Reference Range
18.5.4 Clinical Application
18.5.4.1 A Marker of Trophoblast Viability and Placental Dysfunction
18.5.4.2 Prenatal Serum Screening for Down’s Syndrome
18.5.4.3 Prenatal Serum Screening for Other Chromosome Abnormalities
18.5.4.4 A Marker to Evaluate the Development of Hypertensive Disorders of Pregnancy
18.6 Unconjugated Estriol
18.6.1 Resource and Characteristics
18.6.2 Detection Method
18.6.3 Reference Range
18.6.4 Clinical Application
18.6.4.1 Prenatal Serum Screening for Down’s Syndrome
18.6.4.2 Monitor Placental Function and Fetal Development
18.7 Cell-Free DNA (cfDNA)
18.7.1 Resource and Characteristics
18.7.2 Detection Method
18.7.3 Clinical Application
18.7.3.1 Trisomy 21 (Down’s Syndrome)
18.7.3.2 Trisomy 18 (Edwards Syndrome)
18.7.3.3 Trisomy 13
References
19: Urine
19.1 Overview
19.2 Urea
19.2.1 Sources and Characteristics
19.2.2 Detection Methods
19.2.3 Normal Reference Interval
19.2.4 Clinical Significance
19.3 Creatinine
19.3.1 Sources and Characteristics
19.3.2 Detection Methods
19.3.3 Normal Reference Interval
19.3.4 Clinical Significance
19.4 Urinary Microalbumin
19.4.1 Sources and Characteristics
19.4.2 Detection Methods
19.4.3 Normal Reference Interval
19.4.4 Clinical Significance
19.5 Cystatin C
19.5.1 Sources and Characteristics
19.5.2 Detection Methods
19.5.3 Normal Reference Interval
19.5.4 Clinical Significance
19.6 Transferrin
19.6.1 Sources and Characteristics
19.6.2 Detection Methods
19.6.3 Normal Reference Interval
19.6.4 Clinical Significance
19.7 Uric Acid
19.7.1 Sources and Characteristics
19.7.2 Detection Methods
19.7.3 Normal Reference Interval
19.7.4 Clinical Significance
19.8 α1-Microglobulin
19.8.1 Sources and Characteristics
19.8.2 Detection Methods
19.8.3 Normal Reference Interval
19.8.4 Clinical Significance
19.9 β2-Microglobulin
19.9.1 Sources and Characteristics
19.9.2 Detection Methods
19.9.3 Normal Reference Interval
19.9.4 Clinical Significance
19.10 Neutrophil Gelatinase-Associated Lipocalin
19.10.1 Sources and Characteristics
19.10.2 Detection Methods
19.10.3 Normal Reference Interval
19.10.4 Clinical Significance
19.11 N-Acetyl-β-d Glucosamine Glycosaminase
19.11.1 Sources and Characteristics
19.11.2 Detection Methods
19.11.3 Normal Reference Interval
19.11.4 Clinical Significance
19.12 Retinol Binding Protein
19.12.1 Sources and Characteristics
19.12.2 Detection Methods
19.12.3 Normal Reference Interval
19.12.4 Clinical Significance
References
20: Bone
20.1 Overview
20.2 BAP
20.2.1 Sources and Characteristics
20.2.2 Methods
20.2.3 Reference Interval for Healthy Persons
20.2.4 Clinical Significance
20.2.5 Conclusions and Prospects
20.3 N-MID Osteocalcin
20.3.1 Sources and Characteristics
20.3.2 Methods
20.3.3 Reference Interval for Healthy Persons
20.3.4 Clinical Significance
20.3.5 Conclusions and Prospects
20.4 Total PINP
20.4.1 Sources and Characteristics
20.4.2 Methods
20.4.3 Reference Interval for Healthy Persons
20.4.4 Clinical Significance
20.4.5 Conclusions and Prospects
20.5 PINP
20.5.1 Sources and Characteristics
20.5.2 Methods
20.5.3 Reference Interval for Healthy Persons
20.5.4 Clinical Significance
20.5.5 Conclusions and Prospects
20.6 PICP
20.6.1 Sources and Characteristics
20.6.2 Methods
20.6.3 Reference Interval for Healthy Persons
20.6.4 Clinical Significance
20.6.5 Conclusions and Prospects
20.7 β-Crosslaps
20.7.1 Sources and Characteristics
20.7.2 Methods
20.7.3 Reference Interval for Healthy Persons
20.7.4 Clinical Significance
20.8 Conclusions and Prospects
References
21: Cancer
21.1 Overview
21.1.1 The Ideal Biomarker for Cancer
21.1.2 Classification of Tumor Biomarkers
21.1.2.1 Classification by Biochemical Properties
21.1.2.2 Classification by Sources
21.1.2.3 Classification by Oncogenesis and Development of Tumors
21.2 Tumor Markers with Different Biochemical Properties
21.2.1 Oncofetal Protein
21.2.1.1 AFP
21.2.1.2 AFP-L3
21.2.1.3 CEA
21.2.2 Protein
21.2.2.1 TPA and TPS
21.2.2.2 CYFRA21-1
21.2.2.3 PIVKA-II
21.2.3 Enzymes
21.2.3.1 PSA
21.2.3.2 NSE
21.2.3.3 LDH
21.2.4 Sugar Esters or Glycoproteins
21.2.4.1 CA125
21.2.4.2 CA15–3
21.2.4.3 CA19–9
21.2.5 Hormone
21.2.5.1 HCG
21.2.5.2 CA
21.3 Multistage Biomarkers of Tumorigenesis and Development
21.3.1 Molecular Biomarkers for Early Diagnosis
21.3.1.1 SPLUNC1
21.3.1.2 APC
21.3.1.3 RASSF1A
21.3.1.4 EBERs
21.3.1.5 DAPK
21.3.1.6 NES1
21.3.1.7 DCC
21.3.2 Molecular Biomarkers for Invasion and Metastasis
21.3.2.1 Adhesion Molecules
E-Cadherins
Integrins
CD44
21.3.2.2 Proteolytic Enzymes and Their Inhibitors
MMP
uPA
Cathepsin
21.3.2.3 Tumor Microangiogenesis-Related Molecules
Promoting Microangiogenesis Factors
VEGF
MVD
Inhibitory Microangiogenesis Factors
Angiostatin and Endostatin
21.3.2.4 Autocrine Motility Factor
AMF
Gp78
21.3.3 Molecular Biomarkers for Prognosis
21.3.3.1 Survivin
21.3.3.2 Cyclin D1
21.3.3.3 P53
21.3.3.4 TSLC1
21.3.3.5 BAG-1
21.3.3.6 Bmi-1
21.4 Molecular Biomarkers for Precision Medicine
21.4.1 Breast Cancer
21.4.1.1 HER2
21.4.1.2 ER and PR
21.4.1.3 BRCA1/BRCA2
21.4.2 Lung Cancer
21.4.2.1 EGFR
21.4.2.2 ALK
21.4.2.3 ROS-1
21.4.3 Colorectal Cancer
21.4.3.1 CIN
21.4.3.2 MSI
21.4.3.3 CIMP
21.5 Novel Molecular Biomarkers of Tumor
21.5.1 Tumor Specific Protein 70 (SP70)
21.5.1.1 Sources and Characteristics
21.5.1.2 Clinical Detection Methods
21.5.1.3 Clinical Application
21.5.2 CTC
21.5.3 ctDNA
21.5.4 DNA Methylation in Peripheral Blood
21.5.5 Circulating miRNAs
21.5.6 LncRNA
21.5.7 CircRNA
21.5.8 Exosome
21.5.9 Endosome
21.6 Future Prospects for Cancer Biomarkers
References
22: Translation Research of Novel Biomarker
22.1 Overview
22.2 Discovery
22.2.1 Characteristics of Molecular Biomarkers
22.2.2 Biomarker Classification and Samples
22.2.3 Existing Detection Techniques for Clinical Application
22.2.4 Biomarker Discovery Approaches
22.2.4.1 Strategy of Biomarker Discovery
Research Subjects
Omics Technologies
22.2.4.2 Biomarker Function Identification
Role Transformation of P53
Current Novel Tumor Biomarkers
Pro-Gastrin-Releasing Peptide (ProGRP)
Protein Induced by Vitamin K Absence/Antagonist-II (PIVKA-II)
22.2.5 Artificial Intelligence and Big Data
22.2.6 Monoclonal Antibody Library Technique
22.2.6.1 Usage of Monoclonal Antibody Library
Research on Diagnostic Tumor Biomarker
Cancer Target Therapy
Advantages of the Monoclonal Antibody Library
22.2.7 Transformation Determinants
22.3 Qualification and Verification
22.3.1 Samples from Clinical Cohort
22.3.2 Platforms for Qualification/Verification
22.3.2.1 Enzyme-Linked Immunosorbent Assay (ELISA)
22.3.2.2 Mass Spectrometry
22.3.2.3 Selection of Assays
22.4 Assay Optimization
22.4.1 Development of Clinical Diagnostic Monoclonal Antibodies
22.4.2 Preanalytical Variation
22.4.2.1 Sample Collection and Processing
22.4.2.2 Physiological Factors
22.4.3 Analytical Evaluation
22.4.3.1 Indicators of Accuracy
22.4.3.2 Indicators of Precision
22.4.3.3 Analytical Measurement Range (AMR)
22.4.4 Reference Intervals
22.4.5 Cut-off Value
22.5 Clinical Evaluation
22.6 Progress
References
Part III: Advanced Molecular Diagnostic Techniques
23: Next-Generation Sequencing (NGS)
23.1 Overview
23.2 Basic Principle
23.2.1 Library Construction Principle and Characteristics
23.2.1.1 Construction of DNA Library
23.2.1.2 Construction of RNA Library
23.2.2 High-Throughput Sequencing Principles and Features
23.2.2.1 Illumina Sequencing
23.2.2.2 Ion Torrent Semiconductor Sequencing
23.2.2.3 Complete Genomics Sequencing Platform
23.2.2.4 Single-Molecule Sequencing
23.3 Technology Development
23.3.1 Summary of Technology Development
23.3.2 First-Generation Sequencing Technology
23.3.2.1 Maxam-Gilbert Chemical Degradation Sequencing
23.3.2.2 Deoxygenation Chain Termination Sequencing
23.3.2.3 DNA Automated Sequencing
23.3.3 Next-Generation Sequencing
23.3.3.1 Pyrosequencing
23.3.3.2 454/Roche Sequencing System
23.3.3.3 Ion Torrent/Life Technologies Sequencing System
23.3.3.4 Solexa/Illumina Sequencing System
23.3.3.5 SOLiD/Life Technologies Sequencing System
23.3.3.6 Complete Genomics/BGI Sequencing System
23.3.4 Third Generation of Sequencing Technology
23.3.4.1 Pacific Bioscience SMRT Sequencing Technology
23.3.4.2 Oxford Nanopore Technologies Nanopore Sequencing Technology
23.3.5 Development and Prospect of Sequencing Technology
23.4 Clinical Application
23.4.1 Application of NGS in Noninvasive Prenatal Screening for Chromosome Aneuploidy
23.4.2 Application of NGS in Preimplantation Genetic Screening
23.4.3 Application of NGS in the Detection of Single-Gene Genetic Diseases
23.4.4 Application in the Detection of Tumors
23.4.5 Application of NGS in Tumor-Targeted Therapy
23.4.6 Application of NGS in the Detection of Pathogenic Microorganisms
23.4.7 Application of NGS in Epigenetic Detection
23.4.8 Application of NGS in Sequencing of Immune Repertoire (IR)
23.4.9 Application of NGS in Transcriptomics
References
24: Digital PCR
24.1 Overview
24.2 Basic Principle
24.2.1 Statistics and Quantitative Principles of Digital PCR
24.2.1.1 Confidence Interval in Absolute Quantification
24.2.2 Experimental Principle of Digital PCR
24.2.3 Technology Development
24.2.3.1 The Germination of Digital PCR
24.2.3.2 Proposal of Digital PCR Concept
24.2.3.3 BEAMing Digital PCR
24.2.3.4 Microfluidic Digital PCR
24.2.4 Clinical Application
24.2.4.1 Application Characteristics of Digital PCR
24.2.4.2 Application of Digital PCR on Genomics
24.2.4.3 Application of Digital PCR on Translational Medicine and Precision Medicine
Individualized Medicine and Liquid Biopsy Technique
Application of Digital PCR on Drug Resistance Surveillance
Prenatal Examination
24.2.4.4 Application of Digital PCR on Food Safety
24.2.4.5 Application of Digital PCR on Environmental Microorganisms
24.2.4.6 Other Applications on Digital PCR
Quality Control of Sequencing Library and Verification of Sequencing Result
Gene Editing
24.2.4.7 The Trend of Development of Digital PCR
References
25: Biosensor
25.1 Overview
25.1.1 Characteristics
25.1.1.1 Low Cost and High Speed
25.1.1.2 High Precision and Stability
25.1.1.3 High Specificities
25.1.1.4 Wide Range of Applications
25.1.1.5 Simple Operation and Automatic Analysis
25.1.2 Classifications
25.2 Basic Principle
25.2.1 Principles of Electrochemical Biosensor
25.2.1.1 Electrochemical Immunosensor
25.2.1.2 Electrochemical Enzyme Electrode Sensor
25.2.1.3 Electrochemical DNA Sensor
25.2.1.4 Electrochemical Cell Sensor
25.2.1.5 Cell Receptor-Based Cell Sensor
25.2.1.6 Cellular Lesion-Based Cell Sensor
25.2.1.7 Cell Sensor Integrating Optical Measurement Technology and Electronic Measurement Technology
25.2.2 Principles of Optical Biosensor
25.2.2.1 Passive Optical Sensor
25.2.2.2 Photoinduced Optical Biosensor
25.2.2.3 Electro-optical Biosensor
25.2.3 Principles of Piezoelectric Biosensor
25.3 Technology Development
25.3.1 Wearable Biosensor
25.3.2 Molecular Biosensor and Imaging
25.3.3 Biological Function Simulation Sensor
25.3.3.1 Odor Sensor
25.3.3.2 Taste Sensor
25.3.4 Biosensor Chips
25.4 Clinical Application
References
26: Microfluidic Chip
26.1 Overview
26.2 Basic Principle
26.2.1 Separation
26.2.2 Detection
26.3 Technology Development
26.3.1 Silica and Glass
26.3.2 Polymers
26.3.3 Hydrogel
26.3.4 Paper
26.3.5 Droplet
26.4 Clinical Application
26.4.1 Capillary Electrophoresis
26.4.2 Detection of Nucleic Acid
26.4.2.1 Polymerase Chain Reaction
26.4.2.2 Detection of Gene Mutation
26.4.2.3 Genotyping Detection
26.4.2.4 DNA Sequencing
26.4.3 Amino Acid Analysis
26.4.4 Peptide and Protein Analysis
26.4.4.1 Sample Manipulation and Detection
26.4.4.2 Protein-Ligand Interaction Analysis
26.4.4.3 Mass Spectrometry-Based Proteomics
26.4.5 Immunoassay
26.4.6 Cell Culture and Analysis
26.4.6.1 Cell Manipulation
26.4.6.2 Cell Culture and Cell-to-Cell Interaction Analysis
26.4.6.3 Single-Cell Analysis
26.4.7 Drug Assay and Screening
26.4.7.1 Drug Delivery System
26.4.7.2 Drug Screening
26.4.8 Enzymatic Analysis
26.4.9 Mono-molecular Detection
References
27: Liquid Biopsy
27.1 Overview
27.2 Basic Principle
27.2.1 Circulating Tumor DNA
27.2.1.1 Sample Collection and DNA Extraction
27.2.1.2 ctDNA Detection
PCR-Based Gene Analysis
Sequencing-Based Gene Mutation Analysis
Whole Genome Sequencing Analysis
27.2.2 Circulating Tumor Cells
27.2.2.1 CTC Detection Technologies Without Enrichment
27.2.2.2 CTC Detection Technologies with Enrichment
Molecular Marker-Based Cell Enrichment
Negative Enrichment
Positive Enrichment
Physical Property and Non-affinity-Based Microfluidic Cell Enrichment
27.2.3 Exosomes
27.2.3.1 Isolation
27.2.3.2 Analysis of Exosomes
27.3 Technology Development
27.3.1 Circulating Tumor DNA
27.3.1.1 PCR-Based Techniques
27.3.1.2 Sequencing Techniques
NGS in Liquid Biopsy
27.3.2 Circulating Tumor Cells
27.3.2.1 CellSearch System
27.3.2.2 MagSweeper
27.3.2.3 ISET
27.3.2.4 MEMS
27.3.2.5 FMSA
27.3.2.6 Vortex
27.3.2.7 SP70-Targeted Tumor Cell Enrichment
27.3.2.8 SP70-Targeted Flow Cytometry
27.3.3 Exosomes
27.3.3.1 Isolation of Exosomes
Ultracentrifugation Techniques
Size-Based Techniques
Immunoaffinity Capture-Based Techniques
Precipitation
Microfluidic-Based Isolation Techniques
27.3.3.2 Analysis of Exosomes
Physical Analysis
Chemical, Biochemical, and Compositional Analysis
27.4 Clinical Application
27.4.1 Circulating Tumor DNA
27.4.2 CTCs
27.4.3 Exosome
27.4.4 Comparison Between CTCs, ctDNA, and Exosome Tests in Cancer Detection
27.4.5 Challenges and Outlook
References
28: Molecular-Targeted Imaging
28.1 Overview
28.2 Basic Principle
28.2.1 Probes
28.2.2 Molecular-Targeted Imaging Strategy
28.2.3 Molecular-Targeted Imaging Type
28.3 Technology Development
28.3.1 PET/CT
28.3.2 SPECT
28.3.3 PET/MR
28.3.4 CT
28.3.5 Ultrasound
28.3.6 Optical Imaging
28.4 Clinical Application
28.4.1 Cancer
28.4.2 Cardiovascular Diseases
28.4.3 Central Nervous System Diseases
28.4.4 Autoimmune Diseases
28.4.5 Drug Development
References
29: Fluorescence In Situ Hybridization
29.1 Overview
29.2 Basic Principle
29.2.1 Preparation of the Slides
29.2.2 Preparation of the Probes
29.2.3 Fluorescence In Situ Hybridization
29.2.4 FISH Quality Control
29.3 Technology Development
29.3.1 Immuno-FISH
29.3.2 Multiplex-FISH (M-FISH), Spectral Karyotyping (SKY) and RxFISH
29.3.3 RNA-FISH
29.3.4 Three-Dimensional FISH (3D-FISH)
29.3.5 Fiber-FISH
29.3.6 CGH (Comparative Genomic Hybridization)
29.3.7 Array-CGH
29.3.8 Flow-FISH
29.4 Clinical Application
29.4.1 Prenatal Diagnosis
29.4.2 Hematopoietic Diseases and Lymphoma Related Tests
29.4.3 Application of FISH in Pathogen Detection
29.4.4 FISH for Detection of Circulating Tumor Cells (CTC)
29.4.5 Application of FISH in Solid Oncology
References
30: Circulating DNA Quantification
30.1 Overview
30.2 Basic Principle
30.2.1 Sample Selection of Circulating DNA Testing
30.2.2 Isolation of Circulating DNA
30.2.3 Analysis Platform of Circulating DNA
30.2.3.1 Massive Parallel Sequencing
30.2.3.2 Quantitative Polymerase Chain Reaction, PCR
30.3 Technology Development
30.3.1 Duplex Real-Time PCR for Total Plasma DNA Quantitation
30.3.2 Candidate Gene Approach
30.3.2.1 Allele-Specific PCR (AS PCR)
30.3.2.2 Competitive Allele-Specific TaqMan PCR (CAST PCR)
30.3.2.3 Coamplification at Lower Denaturation Temperature PCR (COLD-PCR)
30.3.2.4 Peptide Nucleic Acid-Locked Nucleic Acid PCR (PNA-LNA PCR)
30.3.2.5 Droplet Digital PCR (ddPCR)
30.3.2.6 Microfluidic Digital PCR
30.3.2.7 “Beads, Emulsion, Amplification, Magnetics Digital PCR” (BEAMing)
30.3.3 Whole-Genome Sequencing Methods
30.3.4 Targeted Deep Sequencing
30.3.4.1 Tagged AMplicon Deep Sequencing (TAM-Seq)
30.3.4.2 Safe-Sequencing System (Safe-SeqS)
30.3.4.3 Duplex Sequencing
30.3.4.4 Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq)
30.4 Clinical Application
30.4.1 Prognostic Value
30.4.2 Diagnosis Value
30.4.3 Detecting Resistance Mechanisms
30.4.4 Detecting Minimal Residual Disease
30.4.5 Noninvasive Prenatal Testing (NIPT)
30.4.6 Injury Assessment
References
31: DNA Methylation Detection Techniques
31.1 Overview
31.2 Basic Principle
31.2.1 DNA Enrichment Methods
31.2.2 ChIP-Seq
31.2.3 Mapping Open Chromatin
31.2.4 Chromosome Conformation Capture
31.3 Technology Development
31.3.1 Genome-Wide Methylation Analysis
31.3.1.1 High-performance Liquid Chromatography
31.3.1.2 High-performance Capillary Electrophoresis
31.3.2 Methylation Detection of Specific Sites
31.3.2.1 Bisulfite Sequencing (BSP)
31.3.2.2 Methylation Specificity PCR
31.3.2.3 Methylight
31.3.2.4 High-resolution Melting (HRM)
31.3.2.5 Combined Bisulfite Restriction Analysis (COBRA)
31.3.2.6 Pyrosequencing
31.3.2.7 Analysis of DNA Methylation Map Based on Chip
31.3.2.8 Single Molecule Real-Time (SMRT) Sequencing Technology
31.3.2.9 Flight Mass Spectrometry
31.4 Clinical Application
References
Part IV: Disease Presentation and Clinical Laboratory Procedures
32: Immunological Disorders
32.1 Allergic Disease
32.1.1 Overview
32.1.2 Pathogenesis
32.1.2.1 Immune Cells in Allergy
32.1.2.2 Inflammatory Molecules in Allergy
32.1.3 Atopic Dermatitis
32.1.3.1 Overview
32.1.3.2 Clinical Appearance
32.1.3.3 Laboratory Diagnosis
Serum Total IgE
Detection of Eosinophils in Peripheral Blood
Serum Eosinophil Cationic Protein (ECP)
Allergen Skin Patch Test or Prick Test
Basophil Activation Test (BAT)
32.1.4 Allergic Asthma
32.1.4.1 Overview
32.1.4.2 Clinical Appearance
32.1.4.3 Laboratory Diagnosis
Allergic Skin Prick Test and Serum IgE Test
Induced Sputum Test
Fractional Exhaled Nitric Oxide (FeNO) Measurement
BAT
Gene Detection
32.1.5 Allergic Rhinitis
32.1.5.1 Overview
32.1.5.2 Clinical Appearance
32.1.5.3 Laboratory Diagnosis
Supportive Methods for the Diagnosis of AR Include Skin Prick Test (SPT) and Serum Specific IgE
Inhalation Allergen Screening Test (Phadiatop)
ECP Detection
32.1.6 Food Allergy
32.1.6.1 Overview
32.1.6.2 Clinical Appearance
IgE-Mediated Food Allergy
Non-lgE-Mediated Food Allergy
Mixed IgE and Non-IgE-Related Food Allergies
Laboratory Diagnosis
Component Resolution Diagnosis (CRD)
Gene Detection
32.1.7 Drug Allergy
32.1.7.1 Overview
32.1.7.2 Clinical Appearance
32.1.7.3 Laboratory Diagnosis
General Laboratory Examination
Gene Detection
32.1.8 Typical Medical Case
32.2 Autoimmune Diseases
32.2.1 Overview
32.2.1.1 Classification of Autoimmune Diseases
32.2.1.2 Common Characteristics of Autoimmune Diseases
32.2.1.3 Pathogenesis of Autoimmune Diseases (Fig. 32.2)
32.2.1.4 The Appearance of Autoantigens
32.2.1.5 Abnormal Immune Regulation
32.2.1.6 Abnormal Expression of Fas/FasL
32.2.1.7 Genetic Factors
32.2.2 The Mechanism of Immune Injury in Autoimmune Diseases (Fig. 32.3)
32.2.2.1 Type II Hypersensitivity
32.2.2.2 Type III Hypersensitivity
32.2.2.3 Type IV Hypersensitivity
32.2.3 Common Autoimmune Diseases
32.2.3.1 Autoimmune Hemolytic Anemia (AIHA)
Case 1, Primary WAIHA
32.2.3.2 Immune Thrombocytopenic Purpura (ITP)
32.2.3.3 Systemic Lupus Erythematosus (SLE)
Experimental Diagnosis of SLE
Case 2, SLE
32.2.3.4 Rheumatoid Arthritis (RA)
Case 3, RA
32.2.3.5 Sjögren’s Syndrome (SS)
32.2.3.6 Myasthenia Gravis (MG)
32.2.3.7 Pulmonary Hemorrhagic Nephritis Syndrome
32.2.4 Major Immunological Detection of Autoimmune Diseases
32.2.4.1 Detection of Autoantibodies and Its Clinical Significance
Systemic Lupus Erythematosus (SLE)
Rheumatoid Arthritis (RA): Detection of Autoantibodies to RA
Autoimmune Hemolytic Anemia
Sjögren’s Syndrome (SS)
32.2.4.2 Clinical Significance of Immunoglobulin and Complement Examination
32.2.4.3 Lymphocyte Detection
32.2.4.4 Detection of Cytokines
32.2.4.5 Application Principles of Immunoassay for Autoimmune Diseases
32.3 Acquired Immune Deficiency Syndrome
32.3.1 Overview
32.3.1.1 HIV Morphology
32.3.1.2 HIV Gene Structure
32.3.1.3 Virus Characteristics
32.3.1.4 Route of Transmission of AIDS
32.3.2 Clinical Appearance
32.3.2.1 General Symptoms
32.3.2.2 Respiratory Symptoms
32.3.2.3 Digestive Symptoms
32.3.2.4 Nervous System Symptoms
32.3.2.5 Skin and Mucous Membrane Damage
32.3.2.6 Tumor
32.3.3 Detection Method
32.3.3.1 Antibody Detection
Screening Test
Confirmatory Reagent
32.3.3.2 Antigen Detection
32.3.3.3 Nucleic Acid Detection
Qualitative Detection
Quantitative Detection
32.3.3.4 CD4+ and CD8+ T Lymphocyte Detection
32.3.3.5 Virus Isolation and Culture
32.3.3.6 HIV-1 Genotype Resistance Detection
32.3.4 Therapy
32.3.4.1 General Treatment
32.3.4.2 Antiviral Therapy
32.3.4.3 Drug Resistance
References
33: Liver Diseases
33.1 Viral Hepatitis
33.1.1 Overview
33.1.2 Hepatitis A
33.1.2.1 Overview
33.1.2.2 Clinical Appearance
33.1.2.3 Laboratory Diagnosis
Immunological Detection
Molecular Biological Detection
Nucleic Acid Molecular Hybridization
RT-PCR
33.1.2.4 Management
33.1.2.5 Typical Medical Case
33.1.3 Hepatitis B
33.1.3.1 Overview
33.1.3.2 Clinical Appearance
33.1.3.3 Laboratory Diagnosis
Immunological Detection
HBsAg
Anti-HBs Antibodies
HBeAg
Anti-HBe Antibodies
HBcAg
Anti-HBc Antibodies
Molecular Biological Detection
Quantitative Analysis of HBV DNA
Quantitative Analysis of HBV RNA
HBV Typing
Analysis of Drug Resistance of HBV
33.1.3.4 Management
33.1.3.5 Typical Medical Case
33.1.4 Hepatitis C
33.1.4.1 Overview
33.1.4.2 Clinical Appearance
33.1.4.3 Laboratory Diagnosis
Immunological Detection
Screening Test
Confirmatory
Molecular Biological Detection
Detection of HCV Nucleic Acid
Genotyping of HCV
33.1.4.4 Management
33.1.4.5 Typical Medical Case
Results with Interpretation Guideline
Final Diagnosis
33.1.5 Hepatitis D
33.1.5.1 Overview
33.1.5.2 Clinical Appearance
Co-Infection
Superinfection
33.1.5.3 Laboratory Diagnosis
Immunological Detection
Molecular Biological Detection
33.1.5.4 Management
33.1.6 Hepatitis E
33.1.6.1 Overview
33.1.6.2 Clinical Appearance
33.1.6.3 Laboratory Diagnosis
33.1.6.4 Management
33.1.6.5 Typical Medical Case
Final Diagnosis
33.2 Autoimmune Hepatitis
33.2.1 Overview
33.2.2 Clinical Appearance
33.2.3 Laboratory Diagnosis
33.2.3.1 Serum Biochemical Indicators
33.2.3.2 Autoantibody
ANA
Anti-Smooth Muscle Antibodies (ASMA)
Anti-Liver/Kidney Microsomal Antibodies (ALKM)
Anti-Liver Cytosol Type 1 (LC-1) Antibodies
Antibodies Against Soluble Liver Antigens/Antibodies Against Liver Pancreas (SLA/LP)
33.2.3.3 Serum Immunoglobulin
33.2.3.4 Histopathology
33.2.3.5 Conclusion
33.3 Hepatocellular Carcinoma
33.3.1 Overview
33.3.2 Surveillance and Diagnosis
33.3.2.1 Surveillance
33.3.2.2 Diagnosis
33.3.3 Biomarkers
33.3.3.1 Alpha-Fetoprotein (AFP)
33.3.3.2 Glypican-3 (GPC3)
33.3.3.3 Golgi Protein-73 (GP73)
33.3.3.4 Alpha-l-Fucosidase (AFU)
33.3.3.5 Protein Induced by Vitamin K Absence/Antagonist-II (PIVKA-II)
33.3.3.6 Squamous Cell Carcinoma Antigen (SCCA)
33.3.3.7 MicroRNAs
33.3.3.8 Other Biomarkers
Tumor Specific Protein 70 (SP70)
Long Non-coding RNAs (LncRNAs)
Circular RNAs (CircRNAs)
Contents in Exosome
33.3.4 Genetic Variations
33.3.5 Treatment
33.3.6 Typical Medical Case
33.4 Wilson Disease
33.4.1 Clinical Appearance
33.4.1.1 Clinical Manifestations
33.4.1.2 Neurological and Psychiatric Manifestations
33.4.1.3 Ophthalmic Manifestations
33.4.2 Diagnosis
33.4.2.1 Liver Function Tests
33.4.2.2 Biochemical Investigations
Ceruloplasmin
Total Serum Copper
Urinary Copper Excretion
33.4.2.3 Liver Biopsy and Liver Copper Content
33.4.2.4 Imaging
33.4.2.5 Genetic Testing
33.4.3 Correlation Between Phenotype and Genotype
33.4.4 Genetic Counselling
33.4.5 Prenatal Diagnosis and Preimplantation Genetic Diagnosis
33.4.6 Typical Clinical Case
References
34: Pancreatic Diseases
34.1 Pancreatic Ductal Adenocarcinoma
34.1.1 Overview
34.1.2 Clinical Appearance
34.1.3 Laboratory Diagnosis
34.1.3.1 Mucin Tumor Markers
CA19-9
CA50
CA242
DU-PAN-2
CAM17.1/Wheat-Germ Agglutinin (WGA)
34.1.3.2 Other Markers
Tumor-Specific Protein (SP70)
Carcinoembryonic Antigen
Membrane Antigens
PAM4
MicroRNAs
34.1.3.3 Proteomics
34.1.3.4 Genetic Testing
34.1.4 Management
34.1.5 Treatment
34.1.6 Typical Medical Case
34.2 Chronic Pancreatitis
34.2.1 Overview
34.2.2 Clinical Appearance
34.2.3 Laboratory Diagnosis
34.2.3.1 Exocrine Pancreatic Enzymes or Hormones
34.2.3.2 Human Pancreatic Polypeptide (PP)
34.2.3.3 Pancreatic Function Tests
Indirect Pancreatic Function Tests
Direct Pancreatic Function Tests
34.2.3.4 Genetic Testing
34.2.4 Management
34.2.5 Treatment
34.2.6 Typical Medical Case
34.3 Insulinoma
34.3.1 Overview
34.3.2 Clinical Appearance
34.3.3 Laboratory Diagnosis
34.3.3.1 C-Peptide Inhibition Test with Hog Insulin
34.3.3.2 Intravenous Secretin Test for Insulinoma
34.3.3.3 The 72-h Fast Test
34.3.3.4 Genetic Testing
34.3.4 Management
34.3.5 Treatment
34.4 Intraductal Papillary Mucinous Neoplasms
34.4.1 Overview
34.4.2 Clinical Appearance
34.4.3 Laboratory Diagnosis
34.4.3.1 Mucin Tumor Markers and Oncofetal Antigens
CEA
CA19-9
34.4.3.2 Other Tumor Markers
CF protein
S100 Protein
mAb Das-1
MicroRNAs
34.4.3.3 Genetic Testing
34.4.4 Management
34.4.5 Typical Medical Case
References
35: Digestive Tract Disease
35.1 Gastritis
35.1.1 Classification
35.1.1.1 Based on the Course of Disease
35.1.1.2 Based on Etiology
Helicobacter pylori Gastritis
Chemical Gastritis
Autoimmune Gastritis
35.1.2 Clinical Manifestation
35.1.3 Laboratory Diagnosis
35.1.3.1 Endoscopy
35.1.3.2 Histology
35.1.3.3 Helicobacter pylori
35.1.4 Biomarker
35.1.4.1 Pepsinogens
35.1.4.2 Gastrin 17
35.1.4.3 HOX Transcript Antisense RNA
35.1.5 Typical Medical Case
35.2 Diarrhea
35.2.1 Overview
35.2.2 Acute Diarrhea
35.2.2.1 Infections
35.2.2.2 Poisoning
35.2.2.3 Medications
35.2.2.4 Other Diseases
35.2.3 Chronic Diarrhea
35.2.4 Laboratory Examination
35.2.4.1 Stool Tests
Fecal Bacterial Culture
Stool Routine
Detection of Virus, Virus Antigen, and Virus Nucleic Acid in Feces
35.2.4.2 Blood Tests
35.2.4.3 Serological Tests
35.2.4.4 Others Tests
35.2.5 Biomarkers
35.2.5.1 Calprotectin
35.2.5.2 Lactoferrin
35.2.5.3 Anti-Saccharomyces cerevisiae Antibodies (ASCA)
35.2.5.4 Antineutrophil Cytoplasmic Antibody (ANCAs)
35.2.5.5 C-Reactive Protein (CRP)
35.2.5.6 Anti-Vinculin and Anti-Cytolethal Distending Toxin B Antibodies (CdtB)
35.2.5.7 Angiotensin-Converting Enzyme 2 (ACE 2)
35.2.6 Conclusion
35.2.7 Typical Medical Case
35.3 Crohn’s Disease
35.3.1 Overview
35.3.2 Clinical Manifestation
35.3.3 Laboratory Diagnosis
35.3.3.1 CRP and ESR
35.3.3.2 Hematologic Tests
35.3.3.3 Fecal Calprotectin and Other Fecal Marker
35.3.3.4 Other Serological Markers
35.3.3.5 Microbiology
35.3.3.6 Genetic Testing
35.3.3.7 Endoscopy
35.3.3.8 Imaging
35.3.4 Conclusions
35.3.5 Typical Medical Case
35.4 Esophageal Cancer
35.4.1 Overview
35.4.2 Epidemiology
35.4.3 Etiology
35.4.3.1 Squamous Cell Carcinoma
35.4.3.2 Adenocarcinoma
35.4.4 Screening
35.4.5 Clinical Diagnosis
35.4.6 Molecular Diagnosis
35.4.6.1 Genetic and Epigenetic Alterations Detection and Its Implications
35.4.6.2 Molecular Diagnosis of Micrometastasis and CTCs
35.4.6.3 miRNA-Based Molecular Diagnosis
35.4.6.4 Genetic Polymorphism
35.4.6.5 Novel Molecular Markers
ADAMTS16
Tumor Specific Protein (SP70)
35.4.7 Therapy
35.4.7.1 Ablation
35.4.7.2 Cryotherapy
35.4.7.3 Endoscopic Mucosal Resection
35.4.7.4 Esophagectomy
35.4.8 Treatment Modalities Used in Locally Advanced Esophageal Cancer
35.4.8.1 Radiation Therapy
35.4.8.2 Chemotherapy
35.4.8.3 Chemoradiation Alone
35.4.8.4 Chemoradiation and Surgery
35.4.8.5 Surveillance
35.4.8.6 Palliative Options for Esophageal Carcinoma
35.4.9 Molecules of Companion Diagnosis
35.4.9.1 Targeting the EGFR Signaling Pathway
35.4.9.2 Targeting the HER2 Signaling Pathway
35.4.9.3 Angiogenesis Inhibitors
35.4.9.4 Others
35.4.10 Typical Medical Case
35.5 Gastric Cancer
35.5.1 Overview
35.5.2 Clinical Appearance
35.5.3 Risk Factors for Gastric Cancer
35.5.4 Historical Classification of GC
35.5.5 Laboratory Diagnosis
35.5.5.1 Principles of Biomarker Testing by NCCN Guidelines for GC
35.5.5.2 Traditional Biomarkers of GC
35.5.5.3 Molecular Biomarkers of GC
Tumor Specific Protein 70 (SP70)
HER2/neu
MSI
PD-L1
Circulating Nucleic Acids
The Other Genetic Susceptibility Genes
35.5.6 Typical Medical Case
35.5.7 Conclusion
35.6 Colorectal Cancer
35.6.1 Overview
35.6.2 Epidemiology
35.6.3 Aetiology
35.6.3.1 Environmental Factors
35.6.3.2 Genetic Factors
35.6.3.3 Other Risk Factors
Colorectal Polyps (Adenomatous Polyps)
Inflammatory Bowel Disease
Cholecystectomy
35.6.4 Screening
35.6.4.1 Screening Object
35.6.4.2 Screening Test
Questionnaire Survey Based on High-Risk Factors
Fecal Occult Blood Test (FOBT)
Fecal DNA Testing
Endoscopy
35.6.5 Clinical Diagnosis
35.6.5.1 Clinical Manifestation
Changes in Bowel Habits and Fecal Traits
Abdominal Pain
Abdominal Mass
35.6.5.2 Digital Rectal Examination
35.6.5.3 Imaging Examination
35.6.5.4 Histopathology
35.6.6 Molecular Diagnosis
35.6.6.1 Broad-Spectrum Tumor Serum Markers
CEA
CA19-9
CA242 and CA50
35.6.6.2 Tumor Specific Protein (SP70)
35.6.6.3 Liquid Biopsy
CTCs
CfDNA
35.6.6.4 Genetic Biomarkers
Microsatellite Instability (MSI)
KRAS and BRAF Mutations
SMAD4 Mutation
35.6.6.5 Proteomics
P53
CDX2
MutT-Related Proteins
C-Met
35.6.6.6 Epigenetics
MicroRNA (miRNA)
Methylation
CpG island Methylator Phenotype (CIMP)
ZNF331
LINE-1
35.6.7 Therapy
35.6.8 Typical Medical Case
References
36: Kidney Diseases
36.1 Polycystic Kidney Disease
36.1.1 Overview
36.1.2 Diagnosis
36.1.3 Genetic Testing Algorithm
36.1.4 Correlation Between Phenotype and Genotype
36.1.5 Genetic Counselling
36.1.6 Prenatal Diagnosis and Preimplantation Genetic Diagnosis
36.1.7 Typical Clinical Case
36.2 Nephropathy
36.2.1 Overview
36.2.2 Classification
36.2.2.1 Glomerular Diseases
Acute Glomerulonephritis
Signs and Symptoms
Causes and Pathogenesis
Molecular Markers
Serum Glomerular Filtration Markers
Urinary Glomerular Cell Injury Markers
Treatment
Chronic Glomerulonephritis
Signs and Symptoms
Causes and Pathogenesis
Molecular Markers
Treatment
Nephrotic Syndrome
Signs and Symptoms
Causes
Pathogenesis
Complications
Laboratory Diagnosis
Treatment
IgA Nephropathy
Signs and Symptoms
Causes
Pathogenesis
Laboratory Diagnosis
Treatment
36.2.2.2 Tubular Injury
Signs and Symptoms
Causes and Pathogenesis
Molecular Markers
36.3 Kidney Cancer
36.3.1 Overview
36.3.1.1 Summary of Renal Tumors
36.3.1.2 The Classification of Malignant Kidney Diseases
36.3.2 Renal Cell Carcinoma
36.3.2.1 Serological Markers of Renal Cell Carcinoma
Traditional Biomarkers for Renal Cell Carcinoma
Novel Molecular Markers for Renal Cell Carcinoma
Transforming Growth Factor β (TGF-β)
Carbonic Anhydrase IX
PTEN
B7-H1
Insulin-Like Growth Factor II mRNA Binding Protein 3 (IMP3)
Other Laboratory Diagnostic Testings
36.3.2.2 Typical Case
36.3.3 Renal Pelvis Carcinoma
36.3.3.1 Biomarkers of Renal Pelvic Carcinoma
Traditional Biomarkers for Renal Pelvic Carcinoma
β2 Microglobulin
Neuro-Specific Enolase
Novel Molecular Biomarkers for Renal Pelvic Carcinoma
Oncogene Marker
Tamm–Horsfall Protein Antibody
36.3.3.2 Typical Case
36.4 Hantavirus Hemorrhagic Fever with Renal Syndrome
36.4.1 Overview
36.4.2 Pathogenesis
36.4.2.1 Pathogenesis
Direct Action of the Virus
Immunity
The Role of Various Cytokines and Mediators
36.4.2.2 Pathology and Physiology
Shock
Bleeding
Acute Renal Failure
36.4.3 Course of Disease
36.4.3.1 Fever Period
36.4.3.2 Hypotension Shock Period
36.4.3.3 Oliguria
36.4.3.4 Polyuria
36.4.3.5 Recovery Period
36.4.4 Diagnosis
36.4.4.1 Blood Routine
36.4.4.2 Urine Routine
36.4.4.3 Biochemical Tests
36.4.4.4 Special Inspections
36.4.4.5 Other Auxiliary Inspections
36.4.4.6 Differential Diagnosis
36.4.5 Treatment
36.4.6 Typical Medical Case
References
37: Cardiovascular Disease
37.1 Acute Myocardial Infarction
37.1.1 Overview
37.1.2 Clinical Appearance
37.1.3 Laboratory Diagnosis
37.1.4 Management
37.1.5 Conclusion
37.2 Heart Failure
37.2.1 Overview
37.2.2 Clinical Appearance
37.2.3 Laboratory Diagnosis
37.2.4 Management
37.2.5 Conclusion
37.3 Myocarditis
37.3.1 Overview
37.3.2 Clinical Appearance
37.3.3 Laboratory Diagnosis
37.3.3.1 Endomyocardial Biopsy
37.3.3.2 Cardiac Biomarkers
37.3.3.3 Inflammation Biomarkers
37.3.3.4 Pathogenetic Diagnosis
37.3.4 Management
37.3.5 Conclusion
37.4 Congenital Heart Disease (CHD)
37.4.1 Overview
37.4.2 Clinical Appearance
37.4.2.1 Excessive Sweating
37.4.2.2 Poor Feeding
37.4.2.3 Acyanotic and Cyanotic Congenital Heart Diseases
37.4.2.4 Heart Murmurs
37.4.3 Laboratory Diagnosis
37.4.3.1 Karyotyping
37.4.3.2 Array CGH
37.4.3.3 Whole-Exome Sequencing
37.4.3.4 Whole-Genome Sequencing
37.4.4 Management
37.4.5 Conclusion
37.5 Arrhythmia
37.5.1 Overview
37.5.2 Clinical Appearance
37.5.3 Laboratory Diagnosis
37.5.3.1 LQTS
37.5.3.2 SQTS
37.5.3.3 Brugada Syndrome
37.5.3.4 Catecholaminergic Polymorphic Ventricular Tachycardia
37.5.4 Management
37.5.5 Conclusion
References
38: Lung Disease
38.1 Lung Cancer
38.1.1 Overview
38.1.2 Etiology and Pathogenesis
38.1.3 Clinical Appearance
38.1.4 Routine Diagnosis (Fig. 38.3)
38.1.4.1 Imaging Tests
Chest X-Ray
CT Scan
Magnetic Resonance Imaging
38.1.4.2 Endoscopic and Histopathological Examination
Sputum Cytology
Bronchoscopy
Mediastinoscopy
Needle Biopsy
38.1.5 Molecular Diagnosis
38.1.5.1 Routine Biomarkers
38.1.5.2 Companion Diagnostic Biomarker (Fig. 38.5) (Table 38.1)
EGFR
ALK and ROS1
KRAS
BRAF
MET
HER2
PD-1/PD-L1
Others
38.1.5.3 Other Biomarkers
Tumor Specific Protein 70
Auxiliary Diagnosis
Therapy Efficacy Monitoring
Predict Prognosis
DNA Methylation
Potential Biomarkers
38.1.5.4 Liquid Biopsy
38.1.6 Treatments and Therapies
38.1.7 Conclusion
38.1.8 Typical Medical Case
38.2 Cystic Fibrosis
38.2.1 Overview
38.2.2 Clinical Appearance
38.2.2.1 Respiratory System
38.2.2.2 Digestive System
38.2.2.3 Other
38.2.3 Laboratory Diagnosis
38.2.4 Conclusion
38.3 Pneumonia
38.3.1 Overview
38.3.2 Clinical Appearance
38.3.3 Laboratory Diagnosis
38.3.3.1 Pathogenic Diagnosis
38.3.3.2 Key Points for the Laboratory Diagnosis of Respiratory Infections
38.3.4 Management
38.3.5 Conclusion
38.4 Tuberculosis
38.4.1 Overview
38.4.2 Clinical Appearance
38.4.3 Laboratory Diagnosis
38.4.4 Typical Medical Case
38.4.5 Management
38.4.6 Conclusion
38.5 Influenza
38.5.1 Overview
38.5.2 Pathogenesis
38.5.2.1 Transmission
38.5.2.2 Pathology and physiology
38.5.3 Epidemiology
38.5.3.1 Seasonal Variations
38.5.3.2 Epidemic and Pandemic Spread
38.5.4 Course of the Disease
38.5.5 Diagnosis
38.5.5.1 Virus Isolation and Identification
38.5.5.2 Serological Testing and Typing Methods
38.5.5.3 Quick Diagnosis
Loop-Mediated Isothermal Amplification
Gene Chip Technology
Pyrosequencing Technology
Nuclear Acid Sequence-Based Amplification
38.5.6 Prevention and Treatment
38.5.6.1 Prevention
Influenza Vaccination
Improve Your Own Immunity
Other
38.5.6.2 Treatment
NA Inhibitors
M2 Inhibitors
38.5.7 Typical Medical Case
38.6 COVID-19
38.6.1 Overview
38.6.2 Pathogenesis
38.6.2.1 Transmission
38.6.2.2 Pathology and Physiology
38.6.3 Epidemic and Pandemic Spread
38.6.4 Course of the Disease
38.6.4.1 Clinical Classification
38.6.5 Diagnosis
38.6.5.1 Blood Routine and Biochemical Indicators
38.6.5.2 Serological Detection of 2019-nCoV
38.6.5.3 Molecular Diagnostic Methods
Specimen Collection and Storage
Biosafety Considerations
Detection of 2019-nCoV by Real-Time RT-PCR
Assay Control Addition
Interpretation of Results
Detection of 2019-nCoV by Genetic Sequencing
38.6.5.4 Chest CT for COVID-19
38.6.5.5 Clinically Diagnosis
38.6.6 Prevention and Treatment
38.6.6.1 Prevention
Diagnosis and Isolation
Improve Your Immunity
Others
38.6.6.2 Treatment
38.6.7 Typical Medical Case
References
39: Blood Disorders
39.1 Blood Cancer
39.1.1 Overview
39.1.2 Myeloid Neoplasms
39.1.2.1 Myelodysplastic Syndromes
39.1.2.2 Acute Myeloid Leukemia
39.1.2.3 Chronic Myeloid Leukemia
39.1.2.4 Myeloproliferative Neoplasms
39.1.2.5 Myelodysplastic/Myeloproliferative Neoplasms
39.1.3 Lymphoid Neoplasms
39.1.3.1 Lymphoid Leukemia
39.1.3.2 Lymphoma
39.1.3.3 Myeloma
39.1.4 Conclusion
39.2 Coagulation Disorders
39.2.1 Hemophilia
39.2.1.1 Introduction
Hemophilia A
Mutations in the F8 Gene
Hemophilia B
Mutations in the F9 Gene
39.2.1.2 Clinical Manifestation
39.2.1.3 Laboratory Diagnosis
Hemophilia A
Hemophilia B
39.2.1.4 Management
39.2.1.5 Conclusions
39.2.2 Von Willebrand Disease
39.2.2.1 Introduction
Molecular Basis of Disease
Type 1 Von Willebrand Disease
Type 3 Von Willebrand Disease
Type 2 Von Willebrand Disease
Type 2A Von Willebrand Disease
Type 2B Von Willebrand Disease
Type 2M Von Willebrand Disease
Type 2N (Normandy) Von Willebrand Disease
39.2.2.2 Clinical Manifestation
39.2.2.3 Laboratory Diagnosis
39.2.2.4 Management
39.2.2.5 Conclusions
39.2.3 Hereditary Thrombocytopenia
39.2.4 Typical Medical Case
39.3 Hematologic Disorders
39.3.1 Hemoglobinopathies
39.3.1.1 Sickle Cell Disease
Overview
Clinical Manifestation
Laboratory Diagnosis
Management
Conclusions
39.3.2 Thalassemia
39.3.2.1 Overview
α-Thalassemia
β-Thalassemia
39.3.2.2 Clinical Manifestation
39.3.2.3 Laboratory Diagnosis
39.3.2.4 Management
39.3.2.5 Conclusions
39.3.3 Typical Medical Case
References
40: Endocrine and Metabolic Diseases
40.1 Diabetes
40.1.1 Overview
40.1.2 Classification and Clinical Appearance
40.1.2.1 Categories of Increased Risk for Diabetes (Prediabetes)
40.1.2.2 Type 1 Diabetes Mellitus
40.1.2.3 Type 2 Diabetes Mellitus
40.1.2.4 Gestational Diabetes Mellitus
40.1.2.5 Monogenic Diabetes Syndromes
40.1.3 Diagnosis
40.1.3.1 Prediabetes
40.1.3.2 Diagnostic Criteria for Diabetes Mellitus
40.1.3.3 Gestational Diabetes Mellitus
40.1.3.4 Monogenic Diabetes
40.1.4 Laboratory Diagnosis
40.1.4.1 Blood Glucose
40.1.4.2 Urinary Glucose
40.1.4.3 Glucose Tolerance Test
40.1.4.4 Glycated Hemoglobin (HbA1c)
40.1.4.5 Ketone Body
40.1.4.6 Lactic Acid and Pyruvic Acid
40.1.4.7 Blood Glucose Regulator
40.1.4.8 Urinary Microalbumin
40.1.4.9 Genetic Test
40.1.4.10 Autoimmune Markers
40.1.4.11 Other
40.1.5 Management
40.1.6 Conclusion
40.1.7 Typical Medical Case
40.2 Thyroid Disease
40.2.1 Overview
40.2.2 Thyroid Carcinoma
40.2.2.1 Classification of Thyroid Carcinoma
40.2.2.2 Differentiated Thyroid Carcinoma
40.2.2.3 Medullary Thyroid Carcinoma
40.2.2.4 Typical Case
40.2.3 Nontoxic Goiter
40.2.3.1 Etiology and Epidemiology
40.2.3.2 Classification of Goiter
40.2.3.3 Laboratory Diagnosis of Nontoxic Goiter
40.2.3.4 Typical Case
40.2.4 Thyroid Nodule
40.2.4.1 Definition and Clinical Manifestations
40.2.4.2 Laboratory Diagnosis
40.2.4.3 Typical Case
40.2.5 Thyroiditis
40.2.5.1 Introduction
40.2.5.2 Classification and Laboratory Diagnosis of Thyroiditis
40.2.5.3 Typical Case
40.2.6 Hypothyroidism
40.2.6.1 Definition and Classification of Hypothyroidism
40.2.6.2 Diagnosis of Hypothyroidism
40.2.6.3 Typical Case
40.2.7 Hyperthyroidism and Thyrotoxicosis
40.2.7.1 Graves’ Disease
40.2.7.2 Toxic Adenoma and Multinodular Toxic Goiter
40.2.7.3 Typical Case
40.3 Dwarfism
40.3.1 Clinical Appearance
40.3.1.1 Disproportionate Dwarfism
40.3.1.2 Proportionate Dwarfism
40.3.1.3 Others
40.3.2 Diagnosis
40.3.2.1 Disproportionate Dwarfism
40.3.2.2 Proportionate Dwarfism
40.3.2.3 Others
40.3.3 Correlation Between Phenotype and Genotype
40.3.3.1 Achondroplasia
40.3.3.2 Spondyloepiphyseal Dysplasia Congenita
40.3.3.3 Growth Hormone Deficiency
40.3.3.4 Others
40.3.4 Genetic Counseling
40.3.4.1 Achondroplasia
40.3.4.2 Spondyloepiphyseal Dysplasia Congenita
40.3.4.3 Combined Pituitary Hormone Deficiency
40.3.4.4 Others
40.3.5 Prenatal Diagnosis and Preimplantation Genetic Diagnosis
40.3.5.1 Achondroplasia
40.3.5.2 Others
40.3.6 Typical Clinical Case
40.4 Adrenopathy
40.4.1 Overview
40.4.1.1 Structure of Adrenal Gland
40.4.2 Cushing’s Syndrome
40.4.2.1 Pathogeny and Clinical Manifestation
40.4.2.2 Laboratory Diagnosis of Cushing’s Syndrome
40.4.2.3 Typical Medical Case
40.4.3 Primary Aldosteronism
40.4.3.1 Pathogeny and Clinical Appearance
40.4.3.2 Laboratory Diagnosis of Primary Aldosteronism
40.4.3.3 Genetic Testing
40.4.3.4 Typical Medical Case
40.4.4 Addison’s Disease
40.4.4.1 Epidemiology and Clinical Appearance
40.4.4.2 Laboratory Diagnosis of Addison’s Disease
40.4.4.3 Typical Medical Case
40.4.5 Pheochromocytoma
40.4.5.1 Overview
40.4.5.2 Laboratory Diagnosis of Pheochromocytoma
40.4.5.3 Genetic Testing
40.4.5.4 Typical Medical Case
40.4.6 Congenital Adrenocortical Hyperplasia
40.4.6.1 Overview
40.4.6.2 Laboratory Examination
40.4.6.3 Genetic Testing
40.4.6.4 Typical Medical Case
40.5 Gout
40.5.1 Overview
40.5.2 Clinical Appearance
40.5.3 Laboratory Diagnosis
40.5.3.1 Blood Uric Acid
40.5.3.2 Urine Uric Acid
40.5.3.3 Synovial Fluid Analysis
40.5.3.4 Other Blood Tests
40.5.3.5 Gene Testing
40.5.3.6 Others
40.5.4 Management
40.5.5 Conclusion
40.5.6 Typical Medical Case
References
41: Neurological Disease
41.1 Alzheimer’s Disease
41.1.1 Overview
41.1.2 Etiology
41.1.3 Clinical Appearance
41.1.4 Clinical Diagnosis
41.1.4.1 Diagnostic Criteria
41.1.4.2 Laboratory Diagnosis
Cerebrospinal Fluid (CSF) Biomarkers
Amyloid-β
Tau Protein
Blood-Based Biomarker Candidates
Aβ1–42:Aβ1–40 Ratio
Phosphorylated Tau Protein (P-Tau)
Neurofilament Light (NFL)
Classical Genetic Testing
Genes Implicated in EOAD
Genes Implicated in LOAD
Other Potential Genetic Risk Genes of AD
Candidate Epigenetics Biomarkers
DNA Methylation
Histone Modifications
MicroRNAs
Imaging Diagnosis
41.1.5 Management
41.1.6 Conclusion
41.1.7 Typical Medical Case
41.2 Glioma
41.2.1 Overview
41.2.2 Classification
41.2.2.1 By Type of Cell
41.2.2.2 By Grade
41.2.2.3 By Location
41.2.3 Signs and Symptoms
41.2.4 Causes and Pathogenesis
41.2.4.1 Genetic Factors
41.2.4.2 Adult Stature and Body Weight
41.2.4.3 Allergies and Other Medical Conditions
41.2.4.4 Dietary Factors
41.2.4.5 Ionizing Radiation
41.2.5 Molecular Markers in Gliomas
41.2.5.1 Loss of Heterozygosity (LOH) of 1p19q
41.2.5.2 IDH1 and IDH2 Mutations
41.2.5.3 MGMT Promotor Methylation
41.2.5.4 Germline Mutation of TP53
41.2.5.5 Important Factors in Glioma Biology
RTK/RAS/PI(3K), P53
IDH Mutation
Hypoxia, Pseudohypoxia, and Angiogenesis
41.2.6 Treatment
41.2.6.1 Surgical Resection
41.2.6.2 Radiation Therapy
41.2.6.3 Chemotherapy
41.2.7 Typical Medical Case
41.3 Muscular Dystrophy/Muscular Atrophy
41.3.1 Overview
41.3.2 Clinical Appearance
41.3.2.1 Muscular Dystrophy
Duchenne Muscular Dystrophy (DMD) and Becker Muscular Dystrophy (BMD)
Congenital Muscular Dystrophy
Myotonic Dystrophy
Facioscapulohumeral Muscular Dystrophy
Limb-Girdle Muscular Dystrophy
Oculopharyngeal Muscular Dystrophy
Distal Myopathy
Emery-Dreifuss Muscular Dystrophy
41.3.2.2 Muscular Atrophy
Spinal Muscular Atrophy
Amyotrophic Lateral Sclerosis
41.3.3 Diagnosis
41.3.3.1 Muscular Dystrophy
DMD and BMD
CMD
DM
FSHD
LGMD
OPMD
Distal Myopathy
EDMD
41.3.3.2 Muscular Atrophy
SMA
ALS
41.3.4 Correlation between Phenotype and Genotype
41.3.4.1 Muscular Dystrophy
DMD/BMD
CMD
DM
FSHD
OPMD
Distal Myopathy
EDMD
41.3.4.2 Muscular Atrophy
SMA
ALS
41.3.5 Genetic Counseling
41.3.6 Prenatal Diagnosis and Preimplantation Genetic Diagnosis
41.3.7 Typical Clinical Case
41.4 Epilepsy
41.4.1 Overview
41.4.2 Diagnosis
41.4.3 Genetic Testing Algorithm
41.4.3.1 Exclude Nongenetic Disorders
41.4.3.2 Family History
41.4.3.3 General Physical and Neurologic Evaluations
41.4.3.4 Inherited Metabolic Disorders
41.4.4 Correlation between Phenotype and Genotype
41.4.5 Genetic Counseling
41.4.6 Prenatal Diagnosis and Preimplantation Genetic Diagnosis
41.4.7 Typical Clinical Case
References
42: Reproductive Organ Cancer
42.1 Breast Cancer
42.1.1 Overview
42.1.2 Anatomy of the Breast
42.1.3 Histological Classification
42.1.4 Surveillance and Diagnosis
42.1.4.1 Imaging Technologies and Applications in Early Diagnosis and Prognosis for Breast Cancer
42.1.4.2 Mammography
42.1.4.3 Ultrasound
42.1.4.4 Magnetic Resonance Imaging
42.1.4.5 Breast Cancer Biomarkers for Risk Assessment, Screening, Detection, Diagnosis, and Prognosis
42.1.4.6 Genomic Biomarkers
42.1.4.7 Epigenomic Biomarkers and Methylation Biomarkers
42.1.4.8 miR Biomarkers
42.1.4.9 Breast Circulating Tumor Cells Potential Biomarkers for Breast Cancer Diagnosis and Prognosis Evaluation
42.1.4.10 Tumor-Specific Protein 70 (SP70)
42.1.4.11 Other Protein Markers
42.1.5 Typical Medical Case
42.2 Ovarian Cancer
42.2.1 Overview
42.2.2 Clinical Appearance
42.2.3 Laboratory Diagnosis
42.2.3.1 Tumor Markers
CA125
Human Epididymis 4 (HE4)
Mesothelin
Osteopontin
Kallikreins
B7-H4
Interleukins
Vascular Endothelial Growth Factor (VEGF)
MicroRNAs
Exosomes
Circulating Cell-Free DNA
42.2.3.2 Genetic Test
Genetic Mutation
Epigenetic Regulation
42.2.4 Management
42.2.5 Conclusion
42.2.6 Typical Medical Case
42.3 Cervical Cancer
42.3.1 Overview
42.3.1.1 Staging and Classification
42.3.1.2 HPV Introduction
HPV Infection Procedure in the Cervix
Ingestion of the Virus and Delivery of the Genome to Nucleus
Virus Transcription and Life Cycle
Self-limiting and Persistent Infection of the Virus
42.3.2 Clinical Appearance
42.3.3 Laboratory Diagnosis
42.3.3.1 Papanicolaou Test (Pap Test)
42.3.3.2 Molecular Marker
Squamous Cell Carcinoma Antigen (SCCA)
Serum Fragments of Cytokeratin (CYFRA)
42.3.4 Management
42.3.5 Conclusion
42.3.6 Typical Medical Case
References
43: Prenatal Diagnosis and Preimplantation Genetic Diagnosis
43.1 Noninvasive Prenatal Testing
43.1.1 Overview
43.1.2 Laboratory Examination
43.1.2.1 Cell-Free Fetus DNA(cffDNA) and Its Detection in Maternal Plasma
43.1.2.2 Fetal Nucleated Red Blood Cells
43.1.3 Clinical Application
43.1.3.1 Chromosomal Aneuploidy
43.1.3.2 Subchromosomal Abnormalities
43.1.3.3 Single Gene Mutations
43.1.3.4 Fetal Sex Determination
43.1.3.5 Hemolytic Disease of the Newborn
43.1.3.6 Other Applications
43.1.4 Summary
43.1.5 Typical Medical Case
43.2 Mitochondrial Deafness
43.2.1 Introduction
43.2.1.1 Mitochondria and Mitochondrial Genome
43.2.1.2 Mitochondrial Diseases
43.2.1.3 Deafness and Mitochondrial Deafness
43.2.2 Clinical Appearance
43.2.2.1 Mitochondrial Syndromic Hearing Loss
Kearn-Sayre Syndrome
Mitochondrial Encephalopathy with Lactic Acidosis, and Stroke-like episodes
Myoclonic Epilepsy with Ragged Red Fibers
43.2.2.2 Mitochondrial Non-Syndromic Hearing Loss
MT-RNR1 Related Mitochondrial Hearing Loss
Sensorineural Hearing Impairment Induced by Aminoglycosides
Sensorineural hearing impairment without aminoglycosides exposure
MT-TS1 Related Mitochondrial Hearing Loss
43.2.3 Diagnosis
43.2.3.1 Mitochondrial Sensorineural Hearing Loss
Initial Diagnosis
Establishing the Diagnosis
43.2.3.2 Differential Diagnosis
Aminoglycosides-Induced Ototoxicity
43.2.4 Management
43.2.4.1 Treatment of Manifestations
43.2.4.2 Prevention of Primary Manifestations
43.2.4.3 Prevention of Secondary Complications
43.2.4.4 Surveillance
43.2.4.5 Agents and Circumstances to Avoid
43.2.4.6 Evaluation of Relatives at Risk
43.2.5 Prenatal Diagnosis and Preimplantation Genetic Diagnosis in Mitochondrial Deafness
43.2.5.1 Prenatal Diagnosis in Mitochondrial Deafness
43.2.5.2 Preimplantation Genetic Diagnosis in Mitochondrial Deafness
43.2.6 Conclusion
43.2.7 Internet Resources
43.3 Hereditary Vascular Retinopathy
43.3.1 Overview
43.3.1.1 The Anatomy of the Retina
43.3.1.2 The Physiology of the Retinal Vessels
43.3.1.3 The Examination of the Retina
43.3.2 von Hippel-Lindau Syndrome
43.3.2.1 Overview
43.3.2.2 Clinical Appearance
43.3.2.3 Examination
43.3.2.4 Diagnosis
43.3.2.5 Genotype–Phenotype Correlations
43.3.2.6 Differential Diagnosis
Isolated Hemangioblastoma or RCC
Pheochromocytoma
RCC
43.3.2.7 Management
43.3.2.8 Genetic Counseling
43.3.3 Retinal Vasculopathy with Cerebral Leukodystrophy
43.3.3.1 Overview
43.3.3.2 Clinical Appearance
43.3.3.3 Examination
43.3.3.4 Diagnosis
43.3.3.5 Genotype–Phenotype Correlations
43.3.3.6 Differential Diagnosis
43.3.3.7 Management
43.3.3.8 Genetic Counseling
43.3.4 Familial Exudative Vitreoretinopathy
43.3.4.1 Overview
43.3.4.2 Clinical Appearance
43.3.4.3 Examination
43.3.4.4 Diagnosis
43.3.4.5 Genotype–Phenotype Correlations
43.3.4.6 Differential Diagnosis
Retinopathy of Prematurity
Coats Disease
Persistent Hyperplastic Primary Vitreous
Norrie Disease
Toxocariasis
43.3.4.7 Management
43.3.4.8 Genetic Counseling
43.3.5 Typical Medical Case
References
44: Transplant Matching
44.1 Overview
44.1.1 Hyperacute Rejection
44.1.2 Acute Rejection
44.1.3 Chronic Rejection
44.2 HLA and Transplantation Matching
44.2.1 Overview
44.2.2 Genetic Characteristics of HLA
44.2.2.1 Phenotype, Monotype, and Genotype
44.2.2.2 HLA Genetic Model
Monotype Inheritance
Codominant Inheritance
Linkage Disequilibrium
44.3 Tissue Matching Technology and Related Experiments
44.3.1 HLA Typing
44.3.1.1 HLA Serological Typing Technology
44.3.1.2 HLA Genotyping Technology
44.3.1.3 The Basis of HLA Genotyping–Polymerase Chain Reaction
44.3.1.4 Restriction Fragment Length Polymorphism Typing
44.3.1.5 Single Strand Conformation Polymorphism Typing
44.3.1.6 Sequence-Specific Primers Typing
44.3.1.7 PCR-Sequence Specific Oligonucleotide Probe Hybridization
The Basic Principle of PCR-SSO
PCR-SSO Reverse Hybridization Typing
Flow Cytometry-SSO Typing Method
44.3.1.8 Gene Chip Typing
Basic Principle
Advantages of Gene Chip
44.3.1.9 HLA Typing Based on Sequence-Based Typing
44.3.2 HLA Antibody Detection
44.3.2.1 Complement Dependent Cytotoxicity
44.3.2.2 ELISA
44.3.2.3 Flow Fluorescent Microsphere Method
44.3.3 MICA Antibody Analysis
44.3.4 Non-HLA Antibodies
44.3.5 Cross-Matching Experiment of Donor and Recipient
44.3.6 Monitoring of Drug Concentration After Transplantation
44.3.7 Detection of Pathogens Associated with Infection After Transplantation
44.3.8 Routine Blood Cell Analysis and Biochemical Index Detection
44.4 Clinical Significance of HLA Matching in Organ Transplantation
44.4.1 HLA and Kidney Transplantation
44.4.2 HLA and Liver Transplantation
44.4.3 HLA and Heart Transplantation
44.4.4 HLA and Lung Transplantation
References
45: Paternity Testing
45.1 Overview
45.1.1 Alleged Father (AF) /Alleged Mother (AM)
45.1.2 Genetic Marker
45.1.3 Genetic Law
45.1.4 Basic Principles of Paternity Testing
45.2 Several Common Paternity Testing Techniques
45.2.1 Paternity Exclusion by Red Blood Cell Type
45.2.2 Paternity and Family Relationship Identification by DNA Fingerprint Techniques
45.3 Judgement and Analysis of Paternity Testing Results
45.3.1 Parent-Child Relationship Exclusion
45.3.1.1 Excluding Probability of Paternity (EP)
45.3.1.2 Calculation of Excluding Probability of Paternity (EP)
45.3.1.3 Cumulative Excluding Probability of Paternity (CEP)
45.3.1.4 Errors in Excluding Parent-Child Relationship and Its Solutions
45.3.2 Affirmation of Parent-Child Relationship
45.3.2.1 Paternity Index
45.3.2.2 Relative Chance of Paternity
45.3.3 Forensic Criteria of Paternity Testing
45.3.3.1 The Standard of Paternity Exclusion
45.3.3.2 The Standard of Paternity Affirmation
45.3.4 Laboratory Standards of Paternity Testing
45.4 Collection and Preservation of Paternity Test Samples
45.4.1 Collection of Paternity Test Samples
45.4.1.1 Blood/Bloodstain
45.4.1.2 Hair
45.4.1.3 Oral Swab
45.4.1.4 Saliva and Saliva Spots
45.4.1.5 Amniotic Fluid Samples
45.4.2 Preservation of Paternity Test Samples
References
Appendixes
Appendix A: Tests of Infectious Disease
Appendix B: Cancer Tests
Appendix C: Tests of Genetic Disease
Appendix D: Pharmacogenomics
D.1 Anticoagulation Therapy
D.2 Antihypertensive Therapy
D.3 Anticardiac Insufficiency
D.4 Antiangina Pectoris Treatment
D.5 Anti-inflammatory Treatment
D.6 Antigout Treatment
D.7 Antipeptic Ulcer Treatment
D.8 Antidepressant Therapy
D.9 Antipsychotic Treatment
D.10 Antiepileptic Treatment
D.11Antileukemia Treatment
D.12 Antineoplaston
D.13 Antifungal Therapy
D.14 Anti-hyperthyroidism
D.15 Anti-tuberculosis Treatment
D.16 Antiasthma treatment
D.17 Anesthetic Treatment of Paroxysmal Pain
D.18 Disease Prevention
D.19 Erectile Dysfunction
D.20 Hypoglycemic Therapy
D.21 Immunosuppressive Therapy
D.22 Lipid Control Therapy
D.23 Risk Profile

Citation preview

Shiyang Pan Jinhai Tang Editors

Clinical Molecular Diagnostics

123

Clinical Molecular Diagnostics

Shiyang Pan • Jinhai Tang Editors

Clinical Molecular Diagnostics

Editors Shiyang Pan Department of Laboratory Medicine The First Affiliated Hospital of Nanjing Medical University Nanjing China

Jinhai Tang Department of General Surgery The First Affiliated Hospital of Nanjing Medical University Nanjing China

ISBN 978-981-16-1036-3    ISBN 978-981-16-1037-0 (eBook) https://doi.org/10.1007/978-981-16-1037-0 © People’s Medical Publishing House Co. Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publishers, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

With the rapid development of research on genomics and proteomics, many disease-associated biomacromolecules have been identified and put into clinical practice. In recent years, with the rise of the concept of precision medicine and wide application of next-generation sequencing (NGS) and digital PCR in clinical laboratories, molecular diagnostics has gained further attention. So far, it has been widely used in disease diagnosis, guidance for individual therapy, and evaluation of treatment efficiency and prognosis. Therefore, in this era of increasing attention on molecular diagnostics, it is urgent to help clinical chemists, doctors, and medical students worldwide to acquire the information regarding the emerging molecular diagnostic items in short order. Thus, Clinical Molecular Diagnostics emerges at the right moment. For more than 20 years, Prof. Pan, the editor-in-chief of Clinical Molecular Diagnostics, has committed to the identification of novel molecular biomarkers, and has established several outstanding techniques in molecular diagnostics including quantitative detection of plasma DNA and methylation of tumor suppressor genes. Specially, he identified a novel tumor marker named tumor specific protein 70 (SP70) and established the corresponding detection methods which have been applied in the clinical laboratory measurement. Prof. Tang is an accomplished breast surgeon, having not only done much pioneering work in breast cancer diagnosis, surgical treatment, and molecular marker screening, but also made great achievements in the research on the drug resistance mechanisms of breast cancer. In addition, this book is also the brainchild of many experts active in molecular diagnosis, molecular medicine research, and clinical medicine from different universities or hospitals. I believe that under the elaborate organization by Prof. Pan, this book will manifest the latest advances in molecular diagnostics from the perspective of clinical practice. The first distinguishing feature of this reference book is its clinical application. In addition to traditional molecular biological techniques, the book includes brand-new perspectives requiring greater recognition and understanding from clinical practice in the hope of serving as a source of inspiration for readers who run into clinical problems. Besides, the book has a wide coverage, including basic introduction, molecular markers, and methodological techniques of molecular diagnostics for infectious, genetic, or autoimmune diseases, cancers, and other rare diseases. Many novel concepts, such as companion diagnostics and precision medicine, can be found in this reference book. Thus, Clinical Molecular Diagnostics is not only a tool for clinical chemists but also a guideline for clinical diagnosis and therapy. Since the end of 2019 and still not resolved at this moment, the outbreak of COVID-19 has posed a serious threat to human health. Considering the requirement and necessity of insight into the basic characteristics of 2019-nCoV as well as nucleic acid detection, authors of this book specially added corresponding contents to supply theoretical and practical support to health workers all over the world. It is both a privilege and an honor to have been invited to take part in the edition of such an exceptional book. To observe and comment on its birth and growth is both rewarding and stimulating. I will be truly proud to know that clinical chemists, doctors, and medical students

v

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Foreword

use this text as a primary source. Maintaining the highest standards of quality while providing crucial contemporary information that is both concise and readable, this volume will be a suitable tool for medical professionals.

Chinese Academy of Engineering Nanjing Medical University, Nanjing, China

Hongbing Shen

Coordinators

Xuan Cao Ying Chen Hongmei Ding Ming Gu Wenjing Guo Bin Hu Jiahong Jiang Huanyu Ju Kara Pan Jie Li

Huanyu Liang Jingping Liu Liang Ma Shuxian Miao Wenwen Shang Meiyan Shu Kankan Su Mengya Shan Dan Wang

Li Wang Lin Wang Ming Wang Yaman Wang Rongrong Wang Xiaohong Xia Erfu Xie Mengxiao Xie Li Xue

Shuxian Yang Mengyao Yu Litao Zhang Nannan Zhang Wei Zhang Xiang Zhang Yiting Zhang Yuanda Zhang Zheng Zhang

vii

Preface

With the advancement of research in human genomics and proteomics, a large number of biological macromolecules related to diseases have been discovered and applied to the clinic. Molecular diagnosis has been widely used in the field of diseases diagnosis, individualized treatment, efficacy monitoring, and predicting prognosis, becoming an indispensable assistant of doctors. With the rapid development of molecular diagnosis, the broad masses of medical workers need to know a growing number of molecular diagnostic techniques; at the same time, the growing molecular inspection team also needs to keep up with the development of technology. This book of clinical molecular diagnostics is aimed at solving the presenting problems during application of the emerging molecular medicine research results to the clinic. The committee of Clinical Molecular Diagnostics is composed of more than 30 active experts in molecular diagnosis, molecular medicine research, and clinical medicine. This book includes four parts. The basic principles and techniques are mainly introduced in the first part. It lays a good foundation of theoretical knowledge for medical workers and also provides guidance for researchers engaged in molecular diagnosis transformation research. The second part focuses on the research and application achievements of molecular diagnostic markers in recent years and reevaluation of markers which have been used in clinic for many years. In addition, prospect and value assessment of more new markers are introduced. We hope that it can bridge the gap between basic research and clinical application of molecular markers. The third part, Molecular Detection Technology, introduces a series of emerging technologies such as next-generation sequencing, digital PCR, and liquid biopsy, opening the door for medical workers to understand the rapid developing molecular diagnostic technology. The fourth part mainly focuses on molecular diagnosis of common clinical diseases; this part systematically expounds the pathogenesis, clinical manifestations, and routine basis of diagnosis and treatment based on the clue of diagnosis and treatment path. Laboratory diagnosis plays an important role in clinical precision medicine. There are millions of active biological molecules that are related to the occurrence and development of diseases. However, less than one percent of the molecular markers have been identified and applied in the clinic. With the rapid development of molecular detection and diagnostic technology, liquid biopsy technologies such as CTC and ctDNA detection have emerged. Although these novel technologies have been gradually applied in clinical practice, they are still in the initial stage, and the scope of technology application should be established to avoid overuse. This book is a collection of the latest research results in clinical molecular diagnostics, which can help strengthen the mutual understanding between laboratory research and clinical practice. It hopes to serve as a trigger and to lay a foundation for further research in the field of clinical molecular diagnosis. Thanks for your efforts. Nanjing, China 

Shiyang Pan Jinhai Tang

ix

Acknowledgments

During the compilation of Clinical Molecular Diagnostics, we aim to produce a handbook for molecular diagnosis used by clinic. It is also of great value for scholars who are devoted to clinical molecular diagnostics. To facilitate reading and using in everyday work, we have included numerous illustrations as well as concise summary tables. This book has received great support from many units and experts. We are grateful to the National Natural Science Foundation of China, National Clinical Research Center of Laboratory Medicine, National Key Clinical Department of Laboratory Medicine, Key Laboratory for Laboratory Medicine of Jiangsu Province of China, Nanjing Medical University, and First Affiliated Hospital of Nanjing Medical University. We appreciate the help of Prof. Hong Shang, the Chairman of Chinese Medical Doctor Association Laboratory Physicians Branch, and Prof. Chengbin Wang, the Chairman of the Chinese Society of Laboratory Medicine. The book contains illustrations provided by many scholars who have offered good advice and valuable points of view. We thank them for their contributions.

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Contents

Part I Principles of Clinical Molecular Diagnostics 1 Molecules of Disease and Their Detection Methods �����������������������������������������������   3 Lutao Du and Chuanxin Wang 2 Assay Performance Evaluation���������������������������������������������������������������������������������  11 Lixin Wang and Zhiyun Shi 3 Establishment of Biological Reference Interval�������������������������������������������������������  27 Lixin Wang and Zhiyun Shi 4 Ethics: Informed Consent, Patient Privacy �������������������������������������������������������������  39 Qinghe Meng and Xu Qian 5 Bioinformatics�������������������������������������������������������������������������������������������������������������  45 Chenglu He and Yong Duan 6 Report and Consultation �������������������������������������������������������������������������������������������  61 Yongqing Tong 7 Factors Associated with Variation�����������������������������������������������������������������������������  91 Xue Qin 8 Quality Control and Quality Assurance�������������������������������������������������������������������  97 Gaowei Fan and Qingtao Wang 9 Precision Medicine ����������������������������������������������������������������������������������������������������� 115 Yingping Cao and Xianjin Zhu Part II Molecular Biomarkers and Signals from Diseased Functional Organ 10 Molecules in Body Systems ��������������������������������������������������������������������������������������� 123 Shiyang Pan and Xincen Duan 11 Molecules in Signal Pathways ����������������������������������������������������������������������������������� 139 Shiyang Pan and Wei Zhang 12 Endocrine and Metabolism ��������������������������������������������������������������������������������������� 155 Shichang Zhang and Xiaoting Chen 13 Immune System����������������������������������������������������������������������������������������������������������� 167 Xiuru Guan 14 Lipoproteins����������������������������������������������������������������������������������������������������������������� 179 Changmin Wang and Zhiwei Li 15 Transport and Carrier Proteins��������������������������������������������������������������������������������� 195 Changmin Wang and Zhiwei Li

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16 Coagulation and Fibrinolysis������������������������������������������������������������������������������������� 207 Hong Wang and Yun Ling 17 Cardiovascular System����������������������������������������������������������������������������������������������� 221 Haitao Ding and Juan He 18 Pregnancy ������������������������������������������������������������������������������������������������������������������� 229 Xianzhang Huang and Enyu Liang 19 Urine����������������������������������������������������������������������������������������������������������������������������� 241 Bingfeng Zhang and Qing Li 20 Bone����������������������������������������������������������������������������������������������������������������������������� 253 Lieying Fan 21 Cancer ������������������������������������������������������������������������������������������������������������������������� 261 Wenling Zhang, Yumei Huang, and Jian Xu 22 Translation Research of Novel Biomarker��������������������������������������������������������������� 285 Shiyang Pan and Yuexinzi Jin Part III Advanced Molecular Diagnostic Techniques 23 Next-Generation Sequencing (NGS)������������������������������������������������������������������������� 305 Min Wang 24 Digital PCR����������������������������������������������������������������������������������������������������������������� 329 Min Wang and Xianping Li 25 Biosensor��������������������������������������������������������������������������������������������������������������������� 345 Lei Zheng and Ye Zhang 26 Microfluidic Chip������������������������������������������������������������������������������������������������������� 357 Xueen Fang 27 Liquid Biopsy ������������������������������������������������������������������������������������������������������������� 377 Jianyu Rao, Weibo Yu, Teresa Kim, and Thomas Lee 28 Molecular-Targeted Imaging������������������������������������������������������������������������������������� 395 Fang Wang, Jian Xu, and Wenying Xia 29 Fluorescence In Situ Hybridization��������������������������������������������������������������������������� 405 Min Hu and Weimin Wu 30 Circulating DNA Quantification������������������������������������������������������������������������������� 413 Min Hu and Zeyou Wang 31 DNA Methylation Detection Techniques������������������������������������������������������������������� 427 Shiyang Pan and Jiexin Zhang Part IV Disease Presentation and Clinical Laboratory Procedures 32 Immunological Disorders������������������������������������������������������������������������������������������� 439 Hong Mu, Chunlei Zhou, Ling Fang, Feng Xie, Yan Zhang, and Huanhuan Chen 33 Liver Diseases ������������������������������������������������������������������������������������������������������������� 463 Qishui Ou, Hong Mu, Chunlei Zhou, Zhaojing Zheng, and Juan Geng 34 Pancreatic Diseases����������������������������������������������������������������������������������������������������� 493 Lu Yang, Huanyu Ju, Yuan Mu, and Chunrong Gu

Contents

Contents

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35 Digestive Tract Disease����������������������������������������������������������������������������������������������� 511 Genyan Liu, Yuqiao Xu, Shiyang Pan, Weijuan Song, Jia Wang, Fei Jin, Zhenzhen Cai, Yi Zhang, and Xiang Qian 36 Kidney Diseases����������������������������������������������������������������������������������������������������������� 553 Zhaojing Zheng, Juan Geng, Ye Jiang, Meijuan Zhang, Ruixia Yang, Gaoxia Ge, Huaguo Xu, and Xiaojie Zhang 37 Cardiovascular Disease���������������������������������������������������������������������������������������������� 583 Zhou Zhou and Yahui Lin 38 Lung Disease��������������������������������������������������������������������������������������������������������������� 595 Liang Ming, Ting Sun, Haitao Ding, Juan He, Wenjuan Wu, Min Zhang, Simin Yang, Huaguo Xu, Fang Ni, Shiyang Pan, Qun Zhang, and Yongping Lin 39 Blood Disorders����������������������������������������������������������������������������������������������������������� 641 Zhuang Zuo, Cheng Cameron Yin, Lixia Zhang, Lin Wang, and Zhen Ren 40 Endocrine and Metabolic Diseases ��������������������������������������������������������������������������� 665 Hong Yuan, Jingyuan Zhao, Erfu Xie, Lujiang Yi, Zhaojing Zheng, and Juan Geng 41 Neurological Disease��������������������������������������������������������������������������������������������������� 717 Jie Wu, Yutong Zou, Yingchun Xu, Mengxiao Xie, Zhaojing Zheng, and Juan Geng 42 Reproductive Organ Cancer ������������������������������������������������������������������������������������� 751 Jinhai Tang, Xiangjun Cheng, Jieshi Xie, Zheng Cao, Yanhong Zhai, and Boyan Song 43 Prenatal Diagnosis and Preimplantation Genetic Diagnosis����������������������������������� 769 Chengcheng Liu, Xiaoting Lou, Jianxin Lyu, Jian Wang, and Yufei Xu 44 Transplant Matching ������������������������������������������������������������������������������������������������� 801 Binwu Ying and Lijuan Wu 45 Paternity Testing��������������������������������������������������������������������������������������������������������� 813 Binwu Ying and Juan Zhou Appendixes��������������������������������������������������������������������������������������������������������������������������� 821

About the Editors and Contributors

About the Editors Shiyang  Pan  Prof. Shiyang Pan has studied lung cancer over 20  years, during which time he has found novel biomarkers for NSCLC and authored more than 100 peer-reviewed reports. Dr. Pan is the editor-in-chief of Clinical Molecular Diagnostics, which was published in the end of 2013 by People’s Medical Publishing House (PMPH), and has served on the editorial board of the Chinese Journal of Laboratory Medicine. Dr. Pan is the director of the Laboratory Medicine Department of Nanjing Medical University and the National Key Clinical Department of Laboratory Medicine. He is the chairman of the Jiangsu Society of Laboratory Medicine, vice chairman of the Chinese Society of Laboratory Medicine and IFCC committee member of Molecular Diagnostics, and he has served on review committees for the NSFC and Health Administration of China. Jinhai Tang  Prof. Tang is Full Professor of General Surgery, the director of Jiangsu Province Hospital (the First Affiliated Hospital with Nanjing Medical University), and the vice principal of Nanjing Medical University. Prof. Tang is vice president of China Hospital Association (CHA) and vice chairman of the Breast Disease Training Committee, Chinese Medical Doctor Association (CMDA). Prof. Tang is proficient in the diagnosis and treatment of breast cancer especially in breast conserving surgery. He is one of the active advocates and promoters of the standardized comprehensive diagnosis and treatment of breast cancer in China. He has achieved a series of innovative achievements in breast cancer diagnosis, surgical treatment, and molecular marker screening and mechanism research related to breast cancer prognosis. In recent years, Prof. Tang has focused on the research on drug resistance mechanism of breast cancer. In the past five years, he is the author of over 100 articles. Several articles have been published in top journals, such as Chemical Society Review, Journal of Controlled Release, and Blood Reviews. Moreover, Prof. Tang has been engaged in hospital management for many years and is the initiator of hospital management—airport two-way service platform theory.

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About the Editors and Contributors

Hongbing  Shen  Prof. Shen is an academician of the Chinese Academy of Engineering. He is the principal of Nanjing Medical University, director of Jiangsu Province Key Laboratory of Biomarkers and Prevention of Malignant Tumors, director of the Collaborative Innovation Center for Personalized Medicine in Tumors, and director of the International Joint Research Center of “Environment and Human Health.” He is mainly committed to the molecular and epidemiological studies of the environment and tumors, and has made outstanding achievements in the early diagnosis of tumors, genetic susceptibility and recurrence, and molecular markers of prognosis. He has received the National Outstanding Youth Science Foundation, the National “863” Project, the National “973” Project, the National Natural Science Foundation’s Key Projects, and the Innovation Research Group of the Fund Committee. Currently, he has published more than 400 SCI papers. He has received a series of awards including Outstanding Youth Fund, Changjiang Scholars Special Professor of the Ministry of Education, Special Government Allowance Specialist of the State Council, National Leading Talent of 10 Million Projects, and Ho Leung Ho Lee Foundation Science and Technology Progress Award.

Editorial Board Yongtong Cao  Department of Clinical Laboratory, China-Japan Friendship Hospital, Beijing, People’s Republic of China Liang  Chen Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Ming Chen  Department of Clinical Laboratory Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People’s Republic of China Wei Chen  Department of Clinical Laboratory, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shanxi, People’s Republic of China Yu  Chen Department of Laboratory Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Wei  Cui Department of Laboratory Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China Erhei Dai  Department of Clinical Laboratory Medicine, Hebei Medical University, The Fifth Hospital of Shijiazhuang, Hebei, People’s Republic of China Shengmiao  Fu  Central Laboratory, Hainan Provincial People’s Hospital, Haikou, Hainan, People’s Republic of China Weiling  Fu Department of Clinical Laboratory, Southwest Hospital,The First Affiliated Hospital of the Third MilitaryUniversity, Chongqing, People’s Republic of China Chunfang Gao  Department of Laboratory Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, People’s Republic of China Yuhua Gong  Department of Clinical Laboratory, The Third People′s Hospitel of Zhengjiang, Zhengjiang, Jiangsu, People’s Republic of China

About the Editors and Contributors

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Ming  Guan Department of Laboratory Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China Wei  Guo Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China Xiaolin  Guo Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China Xiaoke Hao  Clinical Laboratory Medicine Center of PLA, Xijing Hospital, Fourth Military Medical University, Shanxi, People’s Republic of China Yingyong  Hou Department of Pathology, Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China Zhibin Hu  Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Shan Huang  Guizhou Nursing Vocational College, Guiyang, People’s Republic of China Yong  Ji Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Mei  Jia  Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, People’s Republic of China Xuemei Jia  Department of Gynecology, Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, Jiangsu, People’s Republic of China Li  Jiang Department of Laboratory Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, People’s Republic of China Hui  Kang Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China Xiaopeng Lan  Institute for Laboratory Medicine, Fuzhou General Hospital, PLA, Fuzhou, Fujian, People’s Republic of China Jinming  Li  National Center for Clinical Laboratories, Beijing Hospital, Beijing, People’s Republic of China Li Li  Department of Laboratory Medicine, Shanghai General Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, People’s Republic of China Shijun Li  Department of Clinical Laboratory, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People’s Republic of China Yan Li  Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China Yongzhe  Li Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China Pu Liao  Chongqing Center for Clinical Laboratory, Chongqing, People’s Republic of China Alex J. Liu  Mayo Clinic(MayoBlvd.Phoenix), Rochester, USA Shuye  Liu Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin, People’s Republic of China

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About the Editors and Contributors

Wenen  Liu Department of Clinical Laboratory, Xiangya Hospital of Central South University, Changsha, People’s Republic of China Yong  Liu Department of Clinical Laboratory, Shengjing Hospital of China Medical University, Shengyang, Liaoning, People’s Republic of China Zhijuan  Liu Department of Laboratory Medicine, Tibet Autonomous Region People’s Hospital, Tibet, People’s Republic of China Hui  Lu  Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xinxin  Lu  Department of Clinical Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China Wanshan  Ma Department of Laboratory Medicine, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, People’s Republic of China Xiaoling Ma  Department of Laboratory Medicine, Anhui Provincial Hospital, Anhui Medical University, Hefei, Anhui, People’s Republic of China Hua  Niu Department of Clinical Laboratories, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, People’s Republic of China Baishen  Pan  Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China Lin Peng  Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, People’s Republic of China Jianping Ren  Department of Clinical Laboratory, Shanxi Provincial Hospital of Traditional Chinese Medicine, Taiyuan, Shanxi, People’s Republic of China A.  Xiangren  Department of Laboratory Medicine, People′s Hospitel of Qinghai Province, Xining, Qinghai, People’s Republic of China Baoen Shan  Research Center, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China Han  Shen Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu, People’s Republic of China Lisong Shen  Departments of Clinical Laboratory and Urology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China Zuojun Shen  Department of Clinical Laboratory, Division of Life Sciences and Medicine, The First Affliated Hospital of USTC, University of Science and Technology of China, Hefei, Anhui, People’s Republic of China Hongbin  Shen Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Haixiang  Su Department of Laboratory Medicine, Gansu Provincial Cancer Hospital, Lanzhou, Gansu, People’s Republic of China Guirong  Sun Department of Laboratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China Jianrong Su  Department of Clinical Laboratory, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China Ziyong Sun  Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China

About the Editors and Contributors

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Aiguo  Tang Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China Zhihua Tao  Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China Yaping  Tian Laboratory of Translational Medicine, Beijing Key Laboratory of Chronic Heart-Failure Precision Medicine, Chinese PLA General Hospital, Beijing, People’s Republic of China Chengbin  Wang Department of Clinical Laboratory Medicine, the First Medical Center, Chinese PLA General Hospital, Beijing, People’s Republic of China Clinical Laboratory Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China Hualiang Wang  Department of Molecular Biology, Shanghai Centre for Clinical Laboratory, Shanghai, People’s Republic of China Hui Wang  Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, People’s Republic of China Jianzhong  Wang Clinical Laboratory, Peking University First Hospital, Beijing, People’s Republic of China Lanlan Wang  Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China Lin  Wang Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China Peichang  Wang Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China Qingtao  Wang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China Xiaozhong  Wang Department of Clinical Laborotory, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China Yuming Wang  Department of Clinical Laboratory, Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China Guoqiu Wu  Center of Clinical Laboratory Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, People’s Republic of China Yong  Wu Department of Medicine Clinical Laboratory, The Third Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Xinyou Xie  Department of Clinical Laboratory, Xiasha Campus, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China Bin Xu  Center of Clinical Laboratory Science, Jiangsu Cancer Hospital, Nanjing, Jiangsu, People’s Republic of China Guobin  Xu Department of Clinical Laboratory, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China Wei  Xu Department of Clinical Laboratory, The First Hospital of Jilin University Changchun, Jilin, People’s Republic of China

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About the Editors and Contributors

Wenrong Xu  Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhengjiang, Jiangsu, People’s Republic of China Xiaohong Xu  Department of Clinical Lab, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, People’s Republic of China Yuanhong  Xu Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, People’s Republic of China Zekuan Xu  Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Di  Yang  Institute of Cardiovascular Disease, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Jun Yu  Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA Bingchang  Zhang Department of Microbiology, Clinical Laboratory, Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People’s Republic of China Guoxin  Zhang Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Hua  Zhang Department of Clinical Laboratory, Guizhou University, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, People’s Republic of China Man  Zhang Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of China Xin  Zhang  Clinical Laboratory, Hospital of Xinjiang Production and Construction Corps, Urumqi, Xinjiang, People’s Republic of China Yunli Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Liaoning Medical University, Jinzhou, Liaoning, People’s Republic of China Zhan Zhang  Department of Clinical Laboratory, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China Zhihong Zhang  Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xin Zhao  Department of Clinical Laboratory, Beijing hospital, Beijing, People’s Republic of China Qin  Zhou The Ministry of Education Key Laboratory of Clinical Diagnostics, School of Laboratory Medicine, Chongqing Medical University, Chongqing, People’s Republic of China Yiwen  Zhou Department of Clinical Laboratory Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, People’s Republic of China

Contributors Zhenzhen Cai  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Yingping Cao  Department of Clinical Laboratory, Fujian Medical University Union Hospital, Fuzhou, Fujian, People’s Republic of China Zheng Cao  Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China

About the Editors and Contributors

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Huanhuan  Chen Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xiaoting Chen  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xiangjun  Cheng Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Haitao  Ding Inner Mongolia People’s Hospital, Huhhot, Inner Mongolia Autonomous Region, People’s Republic of China Lutao Du  The Second Hospital of Shandong University, Shandong, Jinan, People’s Republic of China Xincen Duan  Department of Laboratory Medicine, Zhongshan Hospital of Fudan University, Shanghai, People’s Republic of China Yong  Duan  Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China Gaowei Fan  Department of Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China Lieying Fan  Department of Clinical Laboratory, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China Ling Fang  Department of Laboratory Medicine Center, China-Japan Union Hospital of Jilin University, Changchun, Jilin, People’s Republic of China Xueen  Fang Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, People’s Republic of China Gaoxia  Ge Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Juan Geng  Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China Chunrong Gu  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xiuru Guan  The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People’s Republic of China Chenglu  He  Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China Juan He  Inner Mongolia People’s Hospital, Huhhot, Inner Mongolia Autonomous Region, People’s Republic of China Min Hu  Department of Laboratory Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Xianzhang Huang  Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China Yumei  Huang  Department of Laboratory Medicine, Hunan Cancer Hospital and Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China

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About the Editors and Contributors

Ye  Jiang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Fei Jin  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Yuexinzi Jin  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Huanyu  Ju  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Teresa Kim  Department of Pathology and Laboratory Medicine, University of California at Los Angeles, Los Angeles, CA, USA Thomas Lee  Department of Pathology and Laboratory Medicine, University of California at Los Angeles, Los Angeles, CA, USA Qing Li  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xianping Li  Department of Laboratory Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Zhiwei  Li Clinical Laboratory Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China Enyu  Liang Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China Yahui Lin  Center of Laboratory Medicine, Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China Yongping Lin  Department of Clinical Laboratory, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, People’s Republic of China Yun  Ling Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Chengcheng  Liu Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Genyan Liu  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xiaoting  Lou Key Laboratory of Laboratory Medicine, Ministry of Education of China, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China Jianxin Lyu  Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China Qinghe Meng  Department of Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Liang Ming  Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China Hong Mu  Tianjin First Central Hospital, Tianjin, People’s Republic of China

About the Editors and Contributors

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Yuan  Mu Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Fang  Ni Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Qishui  Ou The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China Shiyang Pan  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xiang  Qian  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Xu  Qian  Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Beijing, People’s Republic of China Department of Laboratory Medicine, Cancer Hospital of the University of Chinese Academy of Sciences, Beijing, People’s Republic of China Department of Laboratory Medicine, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, People’s Republic of China Xue  Qin  Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China Jianyu Rao  Department of Pathology and Laboratory Medicine, University of California at Los Angeles, Los Angeles, CA, USA Zhen  Ren Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Zhiyun  Shi Department of Medical Experimental Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, People’s Republic of China Boyan  Song Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China Weijuan Song  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Ting  Sun Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China Jinhai Tang  Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Yongqing Tong  Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China Changmin  Wang Clinical Laboratory Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China Chuanxin Wang  The Second Hospital of Shandong University, Shandong, Jinan, People’s Republic of China Fang  Wang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Hong Wang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China

xxvi

About the Editors and Contributors

Jia  Wang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Jian  Wang Department of Molecular Genetic Diagnostics, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China Lin  Wang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Lixin  Wang Department of Medical Experimental Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, People’s Republic of China Min  Wang  Department of Laboratory Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Qingtao  Wang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China Zeyou Wang  Department of Laboratory Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Jie  Wu Department of Laboratory Medicine, Peking Union Medical College Hospital, Beijing, People’s Republic of China Lijuan Wu  Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China Weimin Wu  Department of Laboratory Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China Wenjuan Wu  Shanghai East Hospital South Branch Affiliated to Tongji University, Shanghai, People’s Republic of China Wenying Xia  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Erfu  Xie Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Feng Xie  Department of Laboratory Medicine Center, China-Japan Union Hospital of Jilin University, Changchun, Jilin, People’s Republic of China Jieshi Xie  Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China Mengxiao Xie  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Huaguo  Xu  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Jian  Xu Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Yingchun Xu  Department of Laboratory Medicine, Peking Union Medical College Hospital, Beijing, People’s Republic of China Yufei  Xu Department of Molecular Genetic Diagnostics, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China

About the Editors and Contributors

xxvii

Yuqiao  Xu Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Lu  Yang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Ruixia Yang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Simin Yang  Shanghai East Hospital South Branch Affiliated to Tongji University, Shanghai, People’s Republic of China Lujiang  Yi  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Cheng  Cameron  Yin Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Binwu Ying  Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China Weibo Yu  Department of Pathology and Laboratory Medicine, University of California at Los Angeles, Los Angeles, CA, USA Hong Yuan  Dalian Municipal Central Hospital, Dalian, People’s Republic of China Yanhong  Zhai Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China Bingfeng  Zhang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Jiexin Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Lixia Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Meijuan Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Min Zhang  Shanghai East Hospital South Branch Affiliated to Tongji University, Shanghai, People’s Republic of China Qun Zhang  The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China Shichang  Zhang Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Wei  Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Wenling  Zhang  Department of Medical Laboratory Science, The Third Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China Department of Medical Laboratory Science, Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China Xiaojie Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China

xxviii

About the Editors and Contributors

Yan  Zhang  Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China Ye  Zhang Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China Yi Zhang  Department of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China Yutong Zou  Department of Laboratory Medicine, Peking Union Medical College Hospital, Beijing, People’s Republic of China Jingyuan Zhao  The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People’s Republic of China Lei  Zheng Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China Zhaojing Zheng  Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China Chunlei Zhou  Tianjin First Central Hospital, Tianjin, People’s Republic of China Juan Zhou  Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China Zhou  Zhou Center of Laboratory Medicine, Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China Xianjin Zhu  Department of Clinical Laboratory, Fujian Medical University Union Hospital, Fuzhou, Fujian, People’s Republic of China Zhuang Zuo  Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Part I Principles of Clinical Molecular Diagnostics

1

Molecules of Disease and Their Detection Methods Lutao Du and Chuanxin Wang

The birth and development of molecular biology is an important event and has become a leading discipline in the natural science. It drives the research of life science to a new stage and has produced vast knowledge in clinical sciences. Molecular diagnosis is based on the theory of molecular biology, and the studies include the changes of the existence, structure, or expression regulation of endogenous or exogenous biological macromolecules and macromolecular systems in human body using the techniques and methods of molecular biology, so as to provide information and decision-­making basis for the prevention, prediction, diagnosis, treatment, and outcome of diseases. The development of molecular biology can be roughly divided into three stages: In the 1950s, it was determined that protein is the main basic material of life, and DNA is the material basis of biological inheritance, which laid a theoretical foundation for the development of molecular biology. In the early 1970s, Watson and Crick proposed that DNA double helix structure and base complementary pairing are the basic ways of nucleic acid replication and genetic information transmission, laying the most important foundation for understanding the relationship between nucleic acid and protein and its role in life. After the 1970s, with the emergence of PCR technology, the launch of the human genome project, and the rapid development of high throughput sequencing technology and mass spectrometry technology, humans began to deeply understand the nature of life and make great strides in the field of molecular diagnosis. This chapter will introduce the main methods of molecular diagnosis, including nucleic acid detection and protein detection. It can be assured that molecular has a bright future, but the road will be difficult and tortuous.

L. Du · C. Wang (*) The Second Hospital of Shandong University, Shandong, Jinan, People’s Republic of China e-mail: [email protected]

1.1

Overview

In the past, diagnosis of diseases often relied on clinical experience, and the disease was judged by summarizing the laws from signs and symptoms. With the development of molecular analyses, the various proteins, enzymes, hormones, and lipids contained in our body fluids can be detected by molecular biological detection methods. In addition to the above substances, nucleic acid has become a popular molecular target for detection in recent years, which have greatly expanded the role of clinical laboratory in various areas. Molecular diagnosis refers to the diagnosis or auxiliary diagnosis of diseases by detecting substances such as DNA, RNA, or protein and provides more direct evidence for the disease by analyzing the presence, variation, or expression of genes or proteins. At present, there are many molecular diagnostic methods, which are mainly divided into nucleic acid detection methods and protein detection methods.

1.2

Molecular Mechanism of Diseases

The occurrence and development of human diseases such as leukemia, malignant tumors, diabetes, neurodegenerative diseases, cardiovascular and cerebrovascular diseases, and hypertension are all related to the abnormal structure, function, and interaction of proteins and their complexes. The nature of disease is protein dysfunction, which is caused by changes in protein quality and quantity. Protein production process is shown in Fig. 1.1. The molecular mechanism of diseases mainly includes changes in gene structure, gene expression caused by cell regulatory factors or other factors, foreign pathogenic genes, post-translation processing, and degradation of proteins. Since proteins are the main molecules performing life functions, errors in the transcription, translation, degradation, and interaction of various molecules related to protein synthesis can lead to the occurrence of diseases.

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_1

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L. Du and C. Wang DNA methylation DNA

m7GPPPN

Pre-rRNA

PolyA Pre-mRNA

snoRNA

Pre-miRNA

rRNAs

PolyA m7GPPPN

m7GPPPN

Nucleus

Nuclear membrane

PolyA

Ribosome

Cytoplasm

miRNA mRNAs

m7GPPPN

PolyA m7GPPPN

PolyA m7GPPPN

PolyA

Noncoding RNA

Protein isoforms

Inhibition of protein synthesis

Fig. 1.1  Protein production process

1.3

Nucleic Acid Detection Methods

The common techniques for nucleic acid detection include nucleic acid amplification technology, sequencing technology, nucleic acid hybridization technology, chip technology, and biosensing technology.

1.3.1 Nucleic Acid Amplification Technology Nucleic acid amplification technology refers to a process in which a nucleic acid of a specific sequence (DNA or RNA) is selectively multiply replicated in vitro, while other genes are not affected. The most commonly used technique for nucleic acid amplification is polymerase chain reaction (PCR). PCR technology refers to a technique in which a

large amount of a target fragment is amplified using DNA polymerase and a pair of specific primers in vitro conditions. PCR technology is a cyclic reaction that is programed under suitable conditions, so the amplification products are rising exponentially in theory. The most advantage of PCR is the large increase of amplification products. The PCR reaction system mainly includes primers, Taq DNA polymerase, dNTPs, template DNA, buffer, and other components. Classical PCR reaction process requires denaturation, annealing, and extension. The denaturation temperature is usually selected at 95  °C, the annealing temperature is determined according to the Tm value of the primer, and the extension generally requires 72 °C. Doublestranded DNA is denatured into a single strand at a temperature of 95  °C in  vitro, and then the primer could combine with the denatured single-strand DNA under the

1  Molecules of Disease and Their Detection Methods

principle of complementary pairing at a low temperature (usually Tm-5  °C). Finally, at 72  °C, the dNTPs are continuously added to the 3′-OH of the primer to synthesize DNA.  Each of these three thermal reaction processes is called a cycle [1]. At present, there are dozens of technologies derived from PCR technology, including nested PCR, reverse transcription PCR (RT-PCR), quantitative real-time PCR (qRTPCR), droplet digital PCR (ddPCR), etc. Nested PCR is a variant of polymerase chain reaction (PCR) that uses two pairs (rather than one pair) of PCR primers to amplify a complete fragment. The first pair of PCR primers amplifying fragments is similar to normal PCR. The second pair of primers, called nested primers (because they are inside the first PCR amplified fragment), binds inside the first PCR product such that the second PCR amplified fragment is shorter than the first amplification. The advantage of nested PCR is that if the first amplification produces an erroneous fragment, the probability of primer pairing and amplification on the wrong fragment for the second time is extremely low. Therefore, the amplification of nested PCR is very specific and is generally applied to detection of microbes such as viruses and tumor genes [2]. Reverse transcription PCR is a widely used variant of the PCR. In RT-PCR, an RNA strand is reverse transcribed into a complementary DNA, and then amplification is performed by PCR using this complementary DNA as a template. Transcription of a single strand RNA into complementary DNA (cDNA) is referred to “reverse transcription” and is accomplished by an RNA-dependent DNA polymerase (reverse transcriptase). Subsequently, another strand of DNA is completed by a deoxynucleotide primer and a DNA-dependent DNA polymerase. Exponential amplification of RT-PCR is a very sensitive technique for detecting very low copy number RNA.  RT-PCR is widely used in the diagnosis of genetic diseases and can be used to quantitatively monitor the content of certain RNAs [3]. When quantitative analysis is performed by PCR after completion of the reverse transcription process, qRT-PCR or ddPCR techniques are also used for quantitative analysis as technology advances. Quantitative analysis is more sensitive and more accurate. QRT-PCR is a method of measuring the total amount of product after each PCR cycle with a fluorescent chemical in a DNA amplification reaction. Quantitative analysis of a specific DNA sequence in a sample to be tested can be calculated using an internal reference or an external reference. Depending on the fluorescent dye used, it is further divided into the SYBR Green I method and TaqMan probe method [4]. DdPCR is a new technique for the absolute quantification and amplification of nucleic acid molecules. Compared with traditional PCR, digital PCR has better reproducibility of results and more accurate quantification

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of nucleic acid molecules. This technology randomly distributes the nucleic acid template into a large number of reaction units for amplification reaction, and when the reaction is finished, the positive signals of each reaction unit are sequentially counted to realize quantitative analysis of the nucleic acid molecules. In view of the unique advantages of digital PCR technology, it is especially suitable for applications that cannot be distinguished by Ct values, e.g., copy number variation, mutation detection, gene relative expression research (such as allelic imbalance expression), second-generation sequencing results validation, miRNA expression analysis, single cell gene expression analysis, and the like [5].

1.3.2 Sequencing Technology Sequencing technology can quickly and accurately acquire the genetic information of organisms, which has been of great significance for life science research. For each organism, the genome contains genetic information for the entire organism. Sequencing technology can truly reflect the genetic information on genomic DNA, and thus reveal the complexity and diversity of the genome comprehensively, playing a very important role in life science research. Sequencing technology can be traced back to the 1950s [6]. As early as 1954, there had been reported the sequencing techniques, that is, Whitfeld and colleagues [7] used a chemical degradation method to detect a polyribonucleotide sequence. The chemical degradation method invented by Sanger and colleagues in 1977 marked the birth of the first generation of sequencing technology. Since then, the second generation of sequencing technology has been produced, including Roche’s 454 technology, Illumina’s Solexa technology, and ABI’s SOLiD technology [8]. Recently, Helicos’ single-­ molecule sequencing (SMS) technology, Pacific Biosciences’ Single Molecule Real Time (SMRT) sequencing technology, and nanopore single-molecule sequencing technology being studied by Oxford Nanopore Technologies Company are the third-generation sequencing technologies. The advantage of the third-generation sequencing technology is that it achieves the inherent continuity of the DNA polymerase, a reaction that can measure thousands of bases [9]. And the accuracy of the third-generation sequencing technology is very high, reaching 99.9999%. In addition, it can directly measure the sequence of RNA, greatly reducing the systematic error caused by reverse transcription in vitro. The third-generation sequencing technology is currently used for genome sequencing, methylation studies, and mutation identification. Nowadays, sequencing technology is moving toward high-­ throughput, low-cost, and long-read length (Table 1.1).

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L. Du and C. Wang

Table 1.1  Characteristics and applications of each sequencing technology Sequencing type The first generation of sequencing technology

The second generation of sequencing technology

The third generation sequencing technology

Advantages (1) Fast speed, low cost, and accurate positioning (2) Accurate measurement is about 800 bp, and the longest is 1000–1500 bp (1) It can measure multiple sequences at one time, and the amount of data is large (2) By accumulating DNA fragments by the database, the sequencer can detect all attached DNA sequence information at one time (3) Small fragments need to be spliced into growing fragments by bioinformatics analysis

(1) Fast speed, one performance test 10–40 kb (2) No longer need to assemble (3) Single molecule sequencing, no need for PCR amplification in the sequencing process (4) Expand application: RNA sequence, methylated DNA sequence

Disadvantages Only one single sequence can be measured at one time

Application (1) STR paternity test, forensic identification (2) SNaPshot sequencing technology

(1) Each detected segment is limited to 250–300 bp (2) Since the splicing is performed by overlapping regions of the sequence, some sequences may be detected many times (1%–1.5%) (3) Because PCR enrichment is used, a small number of sequences cannot be amplified, resulting in loss of information, and there is a probability that a mismatch base will be introduced in the PCR

(1) Whole genome resequencing (WGRS), looking for individual mutations, InDel, CNV, SV, etc., with a large amount of demand data (2) Whole exome sequencing (WES), capture all exons in the genome for sequencing, and obtain high sequencing depth (50 X–150 X) (3) Target sequencing, capturing and enriching the target region of interest for sequencing (4) Transcriptome sequencing (RNA-Seq), which studies sequencing at the transcriptional level, including mRNA, lncRNA, microRNA, and the like (1) Genomic sequencing (2) Methylation research (3) Mutation identification (SNP detection)

(1) High cost (2) The error rate is relatively high, about 15% (3) DNA polymerase-­dependent activity (4) The bio-­information analysis software is not rich enough

1.3.3 Nucleic Acid Hybridization Technology

1.3.4 Chip Technology

Nucleic acid hybridization technology is one of the basic techniques of molecular biology. In recent years, it is being widely used in molecular biology, and its detection process has the advantages of specificity, sensitivity, and rapidity. The basic principle of hybridization is the annealing of two single-stranded nucleic acids with base complementation to form a double strand. The hybridization partners for diagnostic purposes are viral probes of known sequence and viral nucleic acids in the sample to be tested, which are detected by specific methods after hybridization. If there is a hybridization signal, it indicates the presence of viral nucleic acid in the sample, which in turn demonstrates the presence of viral infection. The viral nucleic acid to be tested can be extracted from pathological tissues or extracted from purified virions. After extraction, it can hybridize to the probe on the membrane (solid phase hybridization), or hybridize directly in the hybridization solution of the test tube (liquid phase hybridization). In addition, viral nucleic acids can be hybridized directly on tissue sections or cell smears (in situ hybridization) [10].

At present, the chip technology for nucleic acid detection is mainly gene chip technology. Gene chips are also known as DNA chips, DNA arrays, cDNA chips, DNA microarrays, and oligonucleotide arrays. Gene chip technology is an advanced technology combination of multidisciplinary integration of molecular biology, microelectronics, physics, chemistry, and computer science. It utilizes hydrogen bonding between complementary bases of nucleic acid double strands to form a stable double-stranded structure, and the detection of the sample is achieved by detecting the fluorescent signal on the single strand of interest. Now, matured chips include a chip for detecting gene mutations and an expression gene chip for detecting gene expression levels of cells. Gene chips are the most matured and first commercialized products in biochip technology. The gene chip is developed based on the principle of nucleic acid probe complementary hybridization. Nucleic acid probe refers to a synthetic sequence in which a detectable substance is attached, and the probes are used to identify specific genes in a mixture of nucleic acids based on the principle of base complementation.

1  Molecules of Disease and Their Detection Methods

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The gene chip technology mainly includes four basic technical steps: (i) Chip preparation – mainly adopts surface chemistry method or combinatorial chemistry method to treat solid phase matrix such as glass piece or silica gel, and then arrange DNA fragments or protein molecules in a specific order on the substrate. Human gene chips containing 1 million DNA probes are currently being prepared. (ii) Biological sample preparation and processing: biological samples are a mixture of biological molecules, and generally cannot react directly with the chip except for a few special samples. The sample is subjected to specific biological treatment, and information molecules such as proteins or DNA and RNA are obtained and labeled to improve the sensitivity of the detection. (iii) The reaction between biomolecules and chip: the reaction between biomolecules and the chip is a critical step in chip detection. By selecting appropriate reaction conditions to optimize the process, the mismatch ratio is reduced, thereby obtaining a signal that best reflects the nature of the organism. (iv) The detection and analysis of the chip signal: now the most common method used to detect the chip signal is to put the chip into a chip scanner, and obtain the relevant biological information by collecting the fluorescence intensity and position of each reaction point and analyzing the image by relevant software [11].

These signals are converted into electrical or optical signals by transducers, and then processed by the signal processing and amplification system, and displayed or recorded on the instrument. It can be divided into enzyme sensor, microorganism sensor, immune sensor, tissue sensor, and cell sensor according to the different biological sensitive materials. Based on the transducer, the biosensor can be classified into electrochemical biosensor, photometric biosensor, mediator biosensor, semiconductor biosensor, and piezoelectric crystal biosensor. Now biosensing technology is mainly used in portable diagnostic instruments, blood glucose and oxygen monitoring, and continuous metabolite monitoring and is widely used in clinical examination, monitoring, and rehabilitation evaluation. At present, non-invasive and minimally invasive detection has become an important development direction of biomedical sensing technology [12].

1.3.5 Biosensing Technology

1.4.1 Spectrum Technology

Biosensors are products based on sensors that are infiltrated and integrated by disciplines such as biology, medicine, electrochemistry, optics, thermodynamics, and electronics. It is a selective small analytical device consisting of a bioactive substance as a sensitive component with a suitable transducer. Biosensor technology is characterized by rapidity, sensitivity, specificity, and simplicity and has broad application prospects in the field of molecular biology detection. The basic components of a biosensor include a sensor, a transducer, and a detector with molecular recognition capabilities. With molecular recognition capability, biological component (such as enzymes, antigens, antibodies, nucleic acids, etc.) or the organism itself (such as cells, organelles, or tissues) can be used as sensitive material. A membrane structure formed by immobilized sensitive material is a sensor of the biosensor. The working principle of the biosensor is to produce biochemical reaction with the biosensing material in biosensor and the test substance in sample through the molecular recognition function, and generate signals such as ions, protons, gases, light, heat, and mass changes. Under certain conditions, the magnitude of the signal is quantitatively related to the amount of the substance being tested in the sample.

The analysis of the radiation energy spectrum emitted by matter or the change of the energy spectrum caused by the interaction between radiant energy and matter is called spectral analysis. A photochemical instrument that separates complex color light into a spectrum is called a spectrometer. There are many types of spectrometers, in addition to spectrometers used in the visible range, as well as infrared spectrometers and ultraviolet spectrometers. According to the working principle of modern spectroscopy instruments, it can be divided into two categories: classic spectrometers and new spectrometers. The classic spectrometer is an instrument based on the principle of spatial dispersion; the new spectrometer is an instrument based on the modulation principle. Classic spectrometers are slit spectroscopy instruments. The modulation spectrometer is non-spatial spectroscopic, which uses a circular aperture to let the light enter. According to the principle of splitting of the dispersive components, spectroscopic instruments can be divided into prism spectrometer, diffraction grating spectrometer, and interference spectrometer. At present, the most commonly used spectroscopy techniques are UV-Vis spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and Raman spectroscopy [13].

1.4

Protein Detection Methods

At present, common molecular biotechnologies for protein detection include spectrum technology, protein chip technology, labeled immunoassay technology, and mass spectrometry technology.

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L. Du and C. Wang

1.4.2 Protein Chip Technology

1.4.4 Mass Spectrometric Technique

Protein chip refers to the immobilization of a large number of protein molecules on a carrier surface in a pre-set arrangement to form a microarray. Based on the principle of specific binding between protein molecules, a microfluidic biochemical analysis system is constructed to achieve accurate, rapid, and large information detection of biomolecules. Although Roger Ekin described the technical principles of protein chips in his environmental material theory in 1980s, microchip detection technology did not receive great attention until significant achievements in the field of genome and proteomics research. With just one experiment on one plate, the possibility of measuring thousands of cell biology parameters provides the perfect solution for building proteomic comprehensive assay tools. The DNA chip has a good hybridization system and can analyze the entire transcription system of the cell by a single reaction test. Since there is no absolute correspondence between mRNA and proteome in cells, solving the problem of differences between genomic and proteomic studies requires other high-throughput techniques that can directly analyze the properties of the detected proteins. In the past few years, different high-throughput analysis and detection technology platforms have been established, and the development of micro-chip technology has exceeded the DNA chip technology, and there are reports on protein chip detection based on a large number of different samples. Protein chip technology is mainly used in gene expression screening, antigen and antibody detection, biochemical reaction detection, and drug screening. This has the advantage of enabling rapid detection of small amounts of crude biological samples (serum, urine, body fluids, etc.) [14].

Mass spectrometry is an analytical method that analyzes the mass-to-charge ratio (m/z) of ions of a sample. The sample needs to be ionized firstly, and then the ions are separated by the mass-to-charge ratio (m/z) through using the different motion behavior of ions in the electric or magnetic field. Qualitative and quantitative results of the sample can be obtained by mass spectrometry and related information of the sample. Early mass spectrometers were mainly used for isotope determination and inorganic elemental analysis. They were used for organic matter analysis since the 1940s. Gas chromatography-mass spectrometers appeared in the 1960s, which greatly expanded the application field of mass spectrometers. Since then, mass spectrometers have become an important instrument for organic matter analysis. The application of computers has led to a dramatic change in mass spectrometry, making its technology more mature and more convenient. Some new mass spectrometry techniques have emerged since the 1980s, such as fast atom bombardment ionization sources, matrix-assisted laser desorption ionization sources, electrospray ionization sources, atmospheric pressure chemical ionization sources, liquid chromatography-­ mass spectrometry, inductively coupled plasma mass spectrometer, Fourier transform mass spectrometer, etc. These new ionization techniques and new mass spectrometers have made significant progress in mass spectrometry. Because mass spectrometry has the advantages of high sensitivity, low sample consumption, fast analysis speed, and simultaneous separation and identification, it has been widely used in various fields such as chemistry, chemical engineering, materials, environment, geology, energy, medicine, criminal investigation, life science, and sports medicine [17].

1.4.3 Labeled Immunoassay Labeled immunoassay technology is a general term for a large class of ultra-sensitive, high-specific detection technologies. Because of its many unique advantages, it has been widely used in basic medicine and clinical fields. Their basic principles are the same, and the signals emitted by the final measurement differ only by the difference of the markers. Although there are more than ten methods reported in the literature, there are some applications that have been widely used: radioimmunoassay (RIA), enzyme immunoassay (EIA), fluorescence immunoassay (FIA), time-resolved fluoroimmunoassay (TRFIA), and chemiluminescence immunoassay (CLIA) [15, 16].

1.5

Future Trends

The rapid development of molecular biology technology, especially omics sequencing technology, has raised the entire medical science research to the molecular level with rapid progress and impact on disease prevention, diagnosis, and therapy. Therefore, the significance of molecular biotechnology is not only reflected in pure scientific value, but more importantly, it will be important with another aspect to the disease evaluation by its application to personalized medicine.

1  Molecules of Disease and Their Detection Methods

References

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10. Nielsen BS, Holmstrom K.  Combined microRNA in situ hybridization and immunohistochemical detection of protein markers. Methods Mol Biol. 2019;1953:271–86. 1. Tatipally S, Srikantam A, Kasetty S.  Polymerase chain reaction 11. Pongor L, Kormos M, Hatzis C, et  al. A genome-wide approach (PCR) as a potential point of care laboratory test for leprosy diagto link genotype to clinical outcome by utilizing next generation nosis: a systematic review. Trop Med Infect Dis. 2018;3:107. sequencing and gene chip data of 6,697 breast cancer patients. 2. Zhang X, Zhang Y, Liu X, et al. Nested quantitative PCR approach Genome Med. 2015;7:104. for urinary cell-free EZH2 mRNA and its potential clinical applica 12. Bhalla N, Jolly P, Formisano N, et al. Introduction to biosensors. tion in bladder cancer. Int J Cancer. 2016;139:1830–8. Essays Biochem. 2016;60:1–8. 3. Bachman J.  Reverse-transcription PCR (RT-PCR). Methods 13. Tonannavar J, Deshpande G, Yenagi J, et  al. Identification of Enzymol. 2013;530:67–74. mineral compositions in some renal calculi by FT Raman and IR 4. Jozefczuk J, Adjaye J. Quantitative real-time PCR-based analysis spectral analysis. Spectrochim Acta A Mol Biomol Spectrosc. of gene expression. Methods Enzymol. 2011;500:99–109. 2016;154:20–6. 5. Cao L, Cui X, Hu J, et  al. Advances in digital polymerase chain 14. Feng Y, Wang B, Chu X, et al. The development of protein chips reaction (dPCR) and its emerging biomedical applications. Biosens for high throughput screening (HTS) of chemically labeling small Bioelectron. 2017;90:459–74. molecular drugs. Mini Rev Med Chem. 2016;16:846–50. 6. Morganti S, Tarantino P, Ferraro E, et  al. Complexity of genome 15. Zhao L, Wang D, Shi G, et  al. Dual-labeled chemiluminescence sequencing and reporting: next generation sequencing (NGS) techenzyme immunoassay for simultaneous measurement of total nologies and implementation of precision medicine in real life. Crit prostate specific antigen (TPSA) and free prostate specific antigen Rev Oncol Hematol. 2019;133:171–82. (FPSA). Luminescence. 2017;32:1547–53. 7. Whitfeld PR. A method for the determination of nucleotide 16. Sastre J, Sastre-Ibanez M.  Molecular diagnosis and immunothersequence in polyribonucleotides. Biochem J. 1954;58(3):390–6. apy. Curr Opin Allergy Clin Immunol. 2016;16:565–70. 8. van Dijk EL, Auger H, Jaszczyszyn Y, et  al. Ten years of next-­ 17. Aggarwal SK. A review on the mass spectrometric studies of amerigeneration sequencing technology. Trends Genet. 2014;30:418–26. cium: present status and future perspective. Mass Spectrom Rev. 9. van Dijk EL, Jaszczyszyn Y, Naquin D, et al. The third revolution in 2018;37:43–56. sequencing technology. Trends Genet. 2018;34:666–81.

2

Assay Performance Evaluation Lixin Wang and Zhiyun Shi

In vitro diagnostic reagents are special items used by medical institutions to diagnose diseases and provide clinical parameters for diagnosis and treatment. The quality of the tests directly affects the clinical test results. This is linked to the positioning of the nature of the disease, the mastery of the disease severity, the choice of treatment methods, and the judgment of prognosis. Nowadays, a large number of individualized in vitro detection reagents are gradually entering the market, and the scale is expanding. To ensure the quality of individualized diagnostic reagents and assure the effectiveness of the detection system, we need to verify the detection performance of these systems.

2.1

Precision

Precision refers to the closeness of independent measurement results obtained under specifying conditions. Usually we can only quantitatively measure the inconsistency of measurement results, that is, imprecision. The time-related precision components are mainly reproducible (in-batch precision), inter-assay precision, intra-day precision, daytime precision, and indoor precision. Precision performance is one of the basic analytical performance of the detection system. It is likewise the basis for other methodological evaluations. If the precision is poor, additional performance evaluation experiments cannot be performed. The US National Clinical and Laboratory Standards Institute (CLSI) issued EP05-A3 document “Evaluation of Precision of Quantitative Measurement Procedure; Approved Guideline-Third Edition” for a thorough introduction to precision performance evaluation, designed to provide a reference for precision performance evaluation experiments. Simultaneously, the committee issued another guidance doc-

L. Wang (*) · Z. Shi Department of Medical Experimental Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, People’s Republic of China

ument EP15-A3 “User Precision Verification and Deviation Estimation” to meet different needs. The current EP5-A2 document “Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline-Second Edition” has been widely used for precision performance evaluation, and this document will be detailed in this chapter [1, 2].

2.1.1 Terminology and Definitions • Precision (measurement) Closeness of independent test results obtained under specifying conditions. • Imprecision The degree of dispersion of each independent measurement under specific conditions. • Repeatability conditions Independent test results are obtained in a short period of time by the same operator and the same instrument using the same method on the same test substance. • Repeatability The closeness of the results is obtained by continuous measurement of the same test object under the same detection conditions. • Reproducibility conditions The test results are obtained by different operators using the same method on different instruments to measure the same test items. • Reproducibility The closeness of the results obtained from the same test object under varying detection conditions. • Run In the interval where the authenticity and precision of the detection system are stable, it usually does not exceed 24 h or less than 2 h. • Sample Derived from one or more parts of the population, which provides information about the population, usually as the basis for the conclusion of the population. Note: For example, collecting a small amount of serum from a large amount of serum. • Intermediate precision conditions The measurement results are obtained by measuring the same test item on the same instrument using the same test method under

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_2

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d­ ifferent operating conditions. It should be noted that (1) there are four elements of the operating conditions, time, calibration, operator, and instrument, and (2) the factors that change the operating conditions need to be clarified. In this precision evaluation, they are usually called “between-run,” “within-day,” “between-day,” “within-­ device,” and “within-laboratory.” • Intermediate precision Precision under intermediate precision conditions.

2.1.2 O  verview of the Precision Evaluation Process (Fig. 2.1) 2.1.3 Features of the EP5-A2 Program The program provides an experimental guide for evaluating quantitative measurement methods and instrument precision performance and should be the most comprehensive and statistically significant in the current precision evalua-

Need for Precision Evaluation

Assess Sources of Variation

Select and Optimize Study Protocol

tion protocols. It is utilized to verify and evaluate the precision performance of the instrument used or the self-built detection system. However, if the manufacturer’s evaluation should include this factor, it should also include variables such as unusual locations and equipment. The scheme adopts the 2 × 2 × 20 experimental method, that is, 2 batches are tested every day, and 2 batches are tested in each batch for 20 days, and 80 valid data are obtained. The program also provides a more intuitive and practical experimental record form. After the experimenter has completed the experiment and obtained enough data, the batch, batch, day, and total imprecision can be obtained through a simple calculation. In addition, after the experimenter obtains the imprecision data, if it is greater than the manufacturer’s declaration, the chi-square test can still be used to judge whether there is a meaningful difference. If there is no noteworthy difference, it is still acceptable.

2.1.4 E  P5-A2 Experimental Protocol and Requirements 2.1.4.1 Experimental Preparation Reagents and Calibrators The same batch of reagents and calibrators should be used throughout the evaluation process. Because the experiment does not independently evaluate certain variability factors, the addition of these variability can more accurately reflect the test performance. Experimental Sample Matrix  Select a matrix similar to a clinical sample as much as possible. Stable, serum matrix controls are typically used.

Perform Preliminary Checkout

Complete Experimental Steps

Check Data Integrity

Analyze Data

Summarize Results

End

Fig. 2.1  Steps in the evaluation of precision for quantitative measurement procedures

Concentration  It is recommended to use two concentrations. Try to choose a concentration that is close to the manufacturer’s stated performance or a concentration close to the “medical decision level.”

2.1.4.2 Experimental Method The entire experiment should collect 20 days of usable data, using 2 concentrations of experimental samples, 2 batches per day, 2 replicates per batch, and 80 acceptable data for each concentration. Evaluation experiments should be structured into several gradual stages according to requirements, and each stage should take the necessary quality control measures to detect outliers. The method should be tested for acceptability of all previous data every 5 days after the start of the method familiarization phase to assure the validity of the results. Instrument familiarity stage In order to avoid problems in the actual instrument performance evaluation process, the operator should be proficient in the instrument’s operating, maintenance, sample preparation, calibration, and testing procedures. This phase can be carried out after the training

2  Assay Performance Evaluation

period provided by the manufacturer or at the same time. There is no requirement to collect data at this stage until the operator can operate the instrument correctly. Method Familiarization Because some of the steps in the evaluation experiment are rarely used in routine measurements, in order to prevent these unfamiliar steps from affecting the results of the evaluation experiment, it is necessary to practice the method several times before performing the evaluation. The formal experiment was performed in two batches per day, and the test was repeated twice in each batch. Each batch was isolated by at least 2 h, and four data per day were obtained for each concentration. This phase typically lasts 5 days and gets data. For complex instruments, the method and familiarity can be extended appropriately. If the data in this stage pass the acceptability test, it will be counted together with the data of the subsequent experimental stage. At the end of the familiar phase of the method, a preliminary precision evaluation experiment is required. The usual practice is to continuously measure 20 times (2 concentrations) using the same quality control as the precision test, and then calculate the standard deviation and coefficient of variation of the consequences. If a significant difference is found from the expected results, the manufacturer must be contacted, and the follow-up experiment terminated until the problem is resolved. The data for this phase can also be used to determine intra-assay outliers in the familiar phrase of the method and in subsequent experiments. After the experimental phase of the method, the experiment still required to last for 15  days. The experimental method is the same as the method familiarization stage. Record the experimental data and recalculate the quality control limits in a series of QC charts every 5 days, and verify the acceptability of all data. If a batch is rejected because of quality control or operational difficulties, it is necessary to re-run a batch of experiments after finding and correcting the cause. If possible, add at least 10 patient specimens to each batch to simulate the actual procedure.

2.1.4.3 Quality Control A routine quality control procedure must be performed in the precision evaluation experiment. Use at least one appropriate concentration of quality control sample in each batch of measurement. If two or more concentrations of quality control are routinely utilized, this should also be the case in this experiment. At the end of the method familiarization phase, a preliminary quality control chart should be established, and the target value  x  and standard deviation (s) should be computed using the first 5 days of quality control data. Since the preliminary estimate has lower statistical performance, ±3 s is used as the warning limit. Use ±4 s as the limit of loss. Subsequent QC data is depicted in the figure. If there is runaway data, the cause should be found and the QC data

13

should be cleared. At the same time, the batch of experimental data should be removed and re-run a batch. Target values, warning limits, and loss of control limits for all acceptable data are calculated every 5 days. If the previously acceptable results are now unacceptable, the batch is rejected, and the experiment is continued until a total of 40 batches of valid data are obtained for 20 days.

2.1.5 D  ata Collection, Processing, and Statistical Analysis 2.1.5.1 Experimental Data Record In order to facilitate data management and statistical processing, each batch of acceptable data can be filled in a form. The record form can be changed according to the user’s situation, as long as it is convenient to use. 2.1.5.2 Outlier Test Daytime Outliers  The routine quality control program can detect batch or daytime outliers. The data of the out-of-­ control batch should be deleted after finding the cause, and then re-run a batch. Intra-group Outlier  The standard deviation obtained by the preliminary precision evaluation experiment was used as the criterion for judging the outliers of the repeated measurement results in the batch. If the absolute value of the repeated measurement is more than 5.5 standard deviations, then the batch of data is rejected. After finding the outliers, find the cause and repeat the batch analysis. If more than 5% of the data is rejected, the reason cannot be found. Then consider that the performance of the instrument is not stable enough; you should contact the manufacturer.

2.1.5.3 Repeatability Estimate Repeatability evaluation uses the following formula:



Sr 

  X 1

2

j 1

j 1

ij 1

 Xij 2 

2

4I

,



where I = total number of days (generally 20) j = run number within-day (1 or 2) Xij1 = result for replicate 1, run j on day i Xij2 = result for replicate 2, run j on day i Two results are required for each batch when using the above formula. If there is only one batch of results with no more than 10% of evaluation days during the experiment, the statistical calculation of the results is still valid.

14

L. Wang and Z. Shi

Otherwise, the number of experimental days should be increased until the requirements are satisfied [1, 2].

2.2

Accuracy

The accuracy performance is part of the important analytical properties of a detection system or method. The importance of the method performance evaluation experiment is second only to the precision evaluation experiment, which is the experimental basis for the subsequent analytical measurement range, analysis sensitivity, and biological reference interval evaluation. At present, there are many options for the evaluation of the accuracy of performance. This section mainly introduces the EP9-A2 document issued by the National Association of Clinical and Laboratory Standards Institute (CLSI) in 2002, namely, “Method Comparison and Bias Estimation Using Patient Samples; Approved Guideline-Second Edition,” which is important for the specification of methodological comparison experiments. It also introduced another guidance document, EP15-A2, “User Verification of Performance for Precision and Accuracy; Approved Guideline-Second Edition” to meet the need of different laboratories [3, 4].

2.2.1 Definitions • Trueness The complete expression is the measurement accuracy, which is the degree of consistency between the mean and the true value of a large number of test results. It is also a qualitative concept and can only be described by the degree. It is usually expressed by the “bias” statistic, which is the opposite of accuracy. This concept has eliminated the effect of imprecision. If there is still a bias, it means that there is a systematic error, so it is different from accuracy. • Accuracy The complete expression should be the measurement accuracy, which is the degree of consistency between the test result and the measured true value, which is related to the accuracy and precision of the measurement. Accuracy is a qualitative concept rather than a quantitative one, and can only be defined as good or bad. An estimate that measures accuracy from the reverse is “deviation.” • Bias Measurement bias refers to the difference between the expected value and the acceptable value of the measurement result. In general, using the accepted (determined or referenced) method and the evaluated method, the dispersion between the repeated test values of the sample is expressed in the unit of measurement or percentage. That is, the difference between the average and the reference value.

• Total error The combination of determination errors that can affect the accuracy of the analysis results, including random errors and systematic errors, is estimates of inaccuracy.

2.2.2 Features of the EP9-A2 Program The program is mainly used to evaluate the bias between the two measurement methods of the same test item and determine whether the bias is within an acceptable range. Usually the new method is called the experimental method, and the method compared with it is called the comparison method. The comparison method can be a reference method, or a method declared by the manufacturer or a conventional method currently used by the user, and the accuracy of the latter should have been confirmed. The program detects 8 samples per day for 5 days and statistically processes the data detected between the two methods, and the bias between the analytical methods is acceptable. The experimental process is relatively simple, with an emphasis on statistical processing. The scheme performs an outlier test and a constant evaluation of the bias of two different calculations and four graphs, and then performs an appropriate range test of the X value. Based on the results obtained above, the expected bias was calculated by linear regression and the residual method, and compared with the allowable bias; the whole result has higher statistical efficiency.

2.2.3 E  P9-A2 Experimental Protocol and Requirements 2.2.3.1 Experimental Preparation Sample Preparation • Source Collect and process fresh patient samples according to the protocol. • Storage If possible, avoid storing specimens, and measure them on the same day of collection; otherwise, select storage conditions and time according to the stability of the components to be tested. • The number of samples should be analyzed at least 40 specimens, which are a little better. Each sample must be of sufficient quantity for both methods to be tested in duplicate. If the required sample size is not obtained from one patient, two (no more than two) patient specimens with the same medical history and approximately the same concentration of the test substance can be mixed. • The concentration should be evaluated within the clinically meaningful range, i.e., within the medically determined level. It should normally be distributed from the reference range, below the reference range, and as far as possible within the analytical measurement range.

2  Assay Performance Evaluation

Comparison Method Selection It has been mentioned above that the methods currently used in the laboratory, the methods declared by the manufacturer, and the accepted reference methods are all comparable methods. The comparison method should have the following characteristics with respect to the experimental method and has better precision than the experimental method; it is not interfered by known interfering substances; the same unit as the experimental method is used; the result is traceable. In addition, the analytical measurement range of the comparison method is at least the same as the experimental method before it can be used for comparison.

2.2.3.2 Experimental Method Instrument Familiarity Stage  In order to avoid problems in the actual instrument performance evaluation process, the operator should be proficient in the instrument’s operating procedures, maintenance procedures, sample preparation methods, and calibration and testing procedures. It is usually 5 days, which can be shorter for very simple instruments and longer for complex instruments. Regular quality control procedures should be established at this stage. Formal Experiment  Eight samples were measured daily using two methods, and each sample was repeatedly measured twice for a total of 5 days. In the repeated measurement of the sample, the first measurement sequence is specified, and the second detection is performed in the reverse order. For example, the samples can be performed in the following order: 1, 2, 3, 4, 5, 6, 7, 8 and 8, 7, 6, 5, 4, 3, 2, 1. The concentrations in the sequence should be arranged as randomly as possible. The reverse order of the second specimen can reduce the effect of cross-contamination and drift on the average value of replicated specimens. Daily samples should be measured within 2  h to ensure analyte stability.

2.2.3.3 Quality Control Regular quality control procedures should be established prior to formal testing. Any method that occurs out-of-­ control should be re-measured until the required number of samples is reached.

2.2.4 Simple Accuracy Evaluation Plan EP15-A document of CLSI provides two procedures to verify accuracy: one is to use patient specimens for methodological comparisons, similar to the EP9-A2 document, but with experimental time, number of samples, number of repetitions, and statistical processing. The former is simple; the other is to calculate the recovery rate by testing the refer-

15

ence material to determine whether it is consistent with the manufacturer’s declaration or other specified performance requirements.

2.2.4.1 Comparison of Patient Sample Results to Those of Another Procedure Obtain 20 patient samples, the concentration of which should be distributed throughout the linear range. Do not use samples that exceed the linear range. Some concentrations are not easy to obtain, and the same disease specimens can be mixed (not more than 2). The collected specimens should be stored until there is a sufficient amount of specimens. These 20 specimens were measured by test and comparative procedures. These measurements can be completed on the same day, and it can also last for 3–4 days, and 5–7 samples can be measured every day. The latter conclusion is more reliable than the former. Each analytical method should be completed within 4 h. If the specimen is stored, it should be measured within 1–2 h after reconstitution. Each method is guaranteed by quality control procedures. Any batch that is rejected due to quality control or operational difficulties should be retested after the problem is corrected. Experimental data processing calculates the difference in results between the two methods for each specimen.  Test procedure result i –  Individual sample bias  bi   . Comparison p rocedure result i 



Individual sample bias in percent = %bi .  Test procedure result i   Comparison procedure result i %bi  100    Coomparison procedure result i  

  .   

Plot the bias or percentage bias of the results of the two methods for each specimen: the horizontal axis represents the comparison method, and the vertical axis represents the percentage bias. Check the bias graph to see if the differences in the results of the samples within the concentration range tested between the two methods are relatively consistent. If they are consistent, use the following average bias to compare with the manufacturer’s statement; If the bias or percentage bias is not consistent within the concentration range, the data should be divided into several parts, and each part independently calculates the average bias. If the bias shows a gradual change in the concentration, the average bias cannot be calculated. More data is needed to confirm the accuracy of the method. Calculate the bias and/or percent between the two procedures.

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L. Wang and Z. Shi

b



I

b

i 1 i

%b 



I

%bi

i 1

.

n n Calculate the standard deviations of the bias and/or bias in percent.

 b  b  I



Sb 

i 1

2

i

.

n 1

 %b  %b  I



S %b 



i 1

2

i

.

n 1



If the estimated or percentage bias is less than the manufacturer’s stated or percentage bias, the laboratory proves that the bias is consistent with the statement, and no further statistical analysis is required. If the bias or percentage bias is greater than the manufacturer’s stated bias or percentage bias, the following steps can be used to perform a statistical test of the difference. Assuming a false rejection rate is α, usually choose α = 1% or α = 5%. Determine the values of tα,n−1, where n represents the number of patient specimens. For example, if α  =  1%, n  =  20, t0.01,19 = 2.539, other values of tα,n−1 can be obtained from the t .s b statistics book; Calculation of bias verification value, n + β, where β is the bias value declared by the manufacturer.

•

•

•

Calculate the verification limits for bias as



t  sb n

and  

t  sb

, n

where β is the manufacturer’s claimed value of bias. Calculate the verification limits for percent bias as



t  s% b n

and  

t  s% b n

.

•



2.2.4.2 Method of Setting Reference Materials The accuracy evaluation can be verified by measuring the recovery rate or deviation of the reference material by using the abovementioned commonly used comparison test. Of course, the reference materials mentioned here are not limited to reference materials derived from reference methods or decisive methods, and may have multiple sources. Sources of Reference Materials • Fresh frozen human serum or some other body substance without ingredients. Analytes for such materials have been valued with reference or deterministic methods and

•

are available from the National Institute of Standards and Technology (NIST) and CAP. Their target values represent the mean of the method. Of course, the reference substance should have been clearly evaluated as suitable for the method. In addition, the target value of the reference substance may be related to the reagent lot number. If the reagent lot number is changed, the target value of the specific method may not be suitable for the new lot number of reagents. The manufacturer should be consulted for advice on the correctness of the method and the applicable reference material. A reference obtained from an ability comparison test. These substances are set by a large number of laboratories and a number of representative reagents and system calibrators. Accuracy confirmation or quality control provided by the manufacturer. These substances are specifically designed for use in analytical systems, but are generally not applicable to methods from another manufacturer. Here we mainly talk about authenticity control products. Of course, we think that it is also feasible to use calibrators with different lot numbers, because it has partially eliminated the matrix effect with the reference method, and it is currently commonly used for calibration verification. Interlaboratory quality assessments are analyzed by numerous laboratories, and their mean values can be used to assess consistency. Of course, if there are a sufficient number of laboratories in the same group, the average value is reliable. For a reliable mean, a minimum of 10 laboratories within the same methodology group is required. The quality evaluation may take a sample value from a small number of reagent batches. For a single laboratory using a new batch of reagents, this method may affect the reliability of the target value. Substances provided by third parties that have been valued in a number of different ways. These substances are similar to the ability to compare experimental objects or regional property controls. Usually, fewer laboratories are involved in calculating the mean of the same group, and the results have less reliable target values. In addition, a relatively small number of reagents with different lot numbers are sampled, which also affects the reliability of the specified target value. The analyte concentration may be fixed to a predetermined concentration. For example, the pressure of a part of the gas in the blood may be fixed at a predetermined value by tonometry.

Procedure for Demonstration of Accuracy with Reference Materials • Select the material most suitable for this method. A minimum of two levels is required. Although five levels were selected to simulate capacity comparison experiments,

2  Assay Performance Evaluation

more levels are suitable for fully evaluating the entire measurement range. The level chosen should be representative of the minimum and maximum measurement ranges of the method. The user should pay attention that the selected level value may represent a good precision level value of the method. • Prepare samples according to the manufacturer’s instructions. The analytes should be thoroughly mixed before use, and each sample measured in duplicate. • Calculate the mean  x  and standard deviation (SD) of the test results at each concentration. • The results are compared to the set requirements, such as the ability to compare the acceptance criteria of the experimental organizer or the total allowable error of the medical.

17

•

•

•

2.3

Sensitivity

The minimum analyte concentration detectable by a detection system or method is referred to as analytical sensitivity or detection limit. For items of particularly low concentration, determining the limits of detection is important for the diagnosis or treatment monitoring of the disease. For example, elevated cTn is an essential basis for the diagnosis of acute myocardial infarction. All of them explicitly require the laboratory to determine the low limit of detection (LoD) and the variation at low concentrations; another example is prostate-specific antigen (PSA), which is an important indicator to monitor the recurrence of patients after treatment. For a long time, the clinical requirements clearly report the minimum amount of PSA.  Negative and positive nucleic acid test reports also require that the minimum amount of nucleic acid that can be detected should be equivalent to how many viruses. Therefore, determining the detection limit of a detection system or method is one of the important tasks of the laboratory [5].

•

•

•

2.3.1 Definitions In 2004, the Clinical and Laboratory Standards Institute (CLSI) issued the EP17-A document, the “Protocols for Determination of Limits of Detection and Limits of Quantitation; Approved Guideline,” which recommended the use of blank limit detection limits and quantitative tests. The limits are used to indicate the sensitivity performance of the detection system or method. The concepts and terminology are now described as follows: • Accepted reference value A widely recognized reference value, which is derived from the following: (1) theoretical or established values based on scientific principles; (2)

values assigned or verified by experiments of national or international organizations; (3) according to the survey value or verification value obtained by the scientific or engineering organization’s cooperative experiment; (4) when none of the above is available, the expected value can be determined quantitatively, such as the mean value measured by a special group. Alpha (α) error/Type I error/False positive The possibility of rejecting invalid assumptions. No analyte is present in the sample, but the result is positive, which is the possibility of a false positive. Beta (β) error/Type II error/False negative Possibility of falsely accepting invalid assumptions. There is a certain amount of analyte in the sample, but the result is negative, which is the possibility of false negatives. Blank Samples that do not contain the analyte to be detected, or samples whose concentration is at least one order of magnitude lower than the lowest level of the analyte. Limit of blank (LoB) The maximum test result observed for a blank sample under the specified probability conditions. (1) LoB is not the actual concentration detected, but to ensure that the positive signal at the actual concentration becomes the detection limit; similarly, LoB is the lowest value expected for a sample containing an analyte equal to the LoD level under the specified possibility conditions. (2) This is the same as the “lower limit of detection,” which is obtained by the prescribed measurement procedure and can give the lowest detection result with a certain measurement uncertainty. Also called “critical value.” Limit of detection (LoD) The minimum amount of analyte in the sample can be detected under specified probability conditions, but may not be quantified to an exact value. Also called “lower limit of detection,” “minimum detectable concentration.” Sometimes used to indicate “sensitivity.” Limit of quantitation (LoQ)/Lower limit of quantitation With specified acceptable precision and accuracy, the minimum amount of analyte in a sample can be quantitatively determined. Also known as “determined lower limit” and “lower limit of detection ranges.”

2.3.2 Discussion of Several Common Terms • The lowest limit is the “blank limit” (LoB), which is the maximum value that you would expect to see for a series of analyte-free samples. It should be noted that LoB is an observed test result, and all other limits refer to the actual concentration of the analyte. • The second lowest limit is the “detection limit” (LoD), which refers to the actual concentration of the analyte.

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The observed result at this concentration is just greater than LoB, so it is called “detected.” • The “limit of quantitation” (LoQ) is the lowest actual concentration of the analyte at which the analyte can be detected. At the same time, the uncertainty of the observed test results is lower than or equal to the quality target set by the laboratory or manufacturer. Uncertainty goals (or bias and imprecision) must be consistent with LoQ or be feasible for the laboratory. • The lower end of the measurement range (LMR) is the lowest level that meets the qualifications. These qualifications include all specified properties of the method, such as bias and imprecision, uncertainty, and other common properties. • The lower end of the linear range (LLR) is the lowest concentration that has a linear relationship between the response of the method and the true concentration. This also requires that the laboratory set nonlinear error goals that must be consistent with all regulations regarding linearity. The above limits have a relationship of LoB  0.05), it is considered that there is a linear relationship. When the precision is good, the analysis is completed. If the nonlinear coefficient b2 of the quadratic polynomial model, or any of b2 or b3 of the cubic polynomial model is compared with 0, and there is a significant difference (p 6000 samples) were constructed. This regulatory interaction network information is based on high-throughput CLIP-­ Seq experimental data (Website: http://starbase.sysu.edu. cn/). ChIP Base provides a comprehensive identification and annotation of expression profiles and transcriptional control of long non-coding RNAs. The high-throughput RNA-seq identification of lncRNA, expression profile, and transcription factor binding sites identified by ChIP-Seq assay were integrated (Website: http://rna.sysu.edu.cn/chipbase/). CircRNA Database  It is a type of circular RNA molecule that exists in almost every organism. Currently, thousands of circRNA have been identified in humans and other model organisms. Studies have shown that circRNA acts as a sponge molecule for miRNA or RNA-binding proteins and can play an important role in physiological and disease processes. As circRNA research increases, known circRNA data information is growing rapidly. Common databases are as follows: • CircRNA Base Human (hg19), mouse (mm9), and information on collecting circRNA from multiple species, including C. elegans (ce6), Drosophila melanogaster (dm3), Spearfish (latCha1), and Coelacanth (Website: http://www.circbase.org/). • Circ Net database provides identification of new circRNA; integration of circ RNA-microRNA-RNA interaction network; expression level of circRNA subtypes; genomic annotation of circRNA subtypes; and sequence of circRNA subtypes (Website: http://circnet.mbc.nctu. edu.tw/). Protein Database  It is a protein information database established in May 2005. The content includes qualitative descriptions such as molecular structure, sample source,

5 Bioinformatics

expression vector, host, chemical analysis method, and molecular structure composition. There are many commonly used protein databases, but Uniprot is considered to be the most comprehensive protein database with the most comprehensive annotation information. Other protein databases include PDB (Protein Data Bank). Uniprot (Universal Protein) integrates the data of Swiss-­ Prot, TrEMBL, and PIR-PSD. It is the most informative and resource-rich, free of charge, available at http://www.uniprot.org/. PDB is a structural database containing proteins, nucleic acids, and other biological macromolecules. It is an important resource in structural biology research (Website: http:// www.rcsb.org/). SNP Database  A Single Nucleotide Polymorphism (SNP) refers to a single base-pair mutation in a DNA sequence or an alteration of A, T, C, or G in the DNA sequence. In other words, it means one or more of the specific localization sites in the genome. It is the most common genetic variation in human beings, which accounts for more than 90% of all known polymorphisms. SNPs are widely present in the human genome, with an average of one base pair per 500– 1000 bases. The polymorphism of SNP only involves the variation of a single base, which can be caused by the transition of a single base, the insertion, or deletion of a base. But usually, the SNP does not include the latter two cases. The Single Nucleotide Polymorphism Database dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) was established by NCBI in collaboration with the National Human Genome Research Institute. It is a resource bank for single base substitution, short insertion, and deletion of polymorphisms. dbSNP was developed to complement and assist GenBank, which contains nucleotide sequences from any organism. It should be noted that the database will stop receiving SNP submissions from non-human species on September 1, 2017, and stop SNP queries from non-human species on November 1, 2017. However, all previous SNP data of nonhuman species can still be downloaded in FTP of dbSNP database. Disease Database  The most common human diseases, such as tumors, cardiovascular diseases, and immunerelated diseases, are complex diseases. Complex diseases, unlike single-­deficient genetic diseases, do not conform to Mendel’s Law. The development of the diseases is a complicated biology process caused by genetic material changes and external environmental changes that are involved. There are several corresponding databases to study different disease types, such as TCGA, the tumor-related database related to the molecular mutation map associated with cancer development and development, COSMIC, gene fusion, and SNP.  GeneCards, including gene expression

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information, is an automated and comprehensive database of human genes, genome maps, proteins, and diseases. It covers several databases of genetic analysis data, integrating literature information, expression, and function to organize comprehensive information in terms of the locations, pathways, mutations, homologous genes, sources, references, diseases, etc. There are also cardiovascular biomedical databases (Cardio) and immune disease-related databases (FIMM and IDR).

5.2

Biological Sequence Analysis

Traces of human evolution over hundreds of millions of years are often reflected in biological sequences. When the origins are the same, different sequences might have specific structural, compositional, and functional similarities. Once an unknown new sequence is obtained, biological sequences analysis software is often used to analyze whether it has similar structure or composition with other genes, or whether it encodes a protein with other function. The most commonly used analysis software programs are BLAST, FASTA, and multi-sequence analysis software cluster. The software compares unknown new sequences with sequence data and draws corresponding conclusions.

5.2.1 Sequence Analysis Data Format  Because the sequence analysis software only recognizes specialized input data format, it is necessary to arrange the data sequence according to the specific data sequence format before data analysis. These formats are readable texts that describe the sequence with identification and related information in a particular order and form. FASTA is a commonly used format for a single sequence. The FASTA format begins with a greater than sign (>), followed by the identifier (>sp | p42261.2 | GRIA1_HUMAN) of the sequence, and then the description information of the sequence. Following a line break is sequence information. Spaces, line breaks, and empty lines are allowed in the sequence until the next greater than sign indicates the end of the sequence, as shown in Fig. 5.1. When we need to convert a sequence to a format such as PIR, we can use ReadSeq and other tools for the conversion. Sequence Retrieval  Before sequence analysis, we need to retrieve the database to obtain the sequence of interest. The Entrez of NCBI is the most commonly used platform (https:// www.ncbi.nlm.nih.gov/nuccore), as shown in Fig.  5.2. Entrez includes nucleic acids, proteins, and Medline Abstract databases and has established perfect links among these three databases. Therefore, protein products and related lit-

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Fig. 5.1  FASTA format

Fig. 5.2  NCBI Home Page

erature can be queried from a DNA sequence, and each entry has the information, which gives information close to the query entry.

masker.org) is designed explicitly for genome repeat sequence processing with the study of the genome, non-­ coding RNA, and other related fields.

Repeat Sequence Processing  Repeat sequence is multiple copies of gene sequences. In the biological genome, it can be divided into three categories according to the frequency of repetition. (1) A highly repetitive sequence is a sequence repeated at hundreds to millions of times, usually less than 10 short fragments of nucleotide residues. For example, satellite DNA in heterochromatin accounts for about 10% of the genome in mice. (2) Moderate repeats are repeats ranging from ten to hundreds of copies, accounting for about 20% of mice, such as the rRNA gene and tRNA gene. (3) Mild repeat sequence refers to some sequences that repeat 2–10 times, such as the tRNA gene. Repeat Masker (http://www.repeat-

Conceptual Translation  It is a way of predicting whether the DNA product encodes or how long the amino acid sequence is encoded by a given DNA sequence that based on the genetic code and relied on gene recognition software. ORF Finder (http://ncbi.nlm.nih.gov/gorf/gorf.html) is commonly used for gene recognition. It has an excellent ability to identify open reading frames of prokaryotes. Of course, there is still a lack of software for 100% prediction results. It is necessary to select appropriate analysis software according to the specific advantages of different software programs so that the analysis can predict unknown genes comprehensively and accurately.

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Analysis of Nucleic Acid Sequence Characteristics  In the process of searching for genes, prokaryotic genomes are relatively simple compared with eukaryotic genome because they do not contain introns. In other words, the eukaryotic coding region is more complicated because it is often divided into different exons depending on the non-coding region. For example, the 5′ non-coding region of many eukaryotic genes has a CpG island structure and plays a role in transcriptional regulation. But CpG islands are rare in the coding region. So how do we study CpG islands within the non-coding regions? The region distribution mismatch is used to predict the promoter region of a gene. In addition, since sequence analysis revealed the sequence of junction regions between exons and introns, those sequences are frequently stored according to the GT-AG rules in online analysis software such as ORF (http://ncbi.nlm.nih.gov/gorf/gorf.html for the open reading frame of the gene of interest).

5.2.2 Multiple Sequence Analysis When we analyze unknown DNA, we often need to know whether there is a similarity between unknown DNA and known biological sequence to determine the biological properties of the sequence. The unknown DNA sequence is then compared with multiple sequences from a group of known homologous but different species of origin, in order to determine the size of homology between the sequence and other sequences. On the one hand, we can understand the genetics. On the other hand, it can be used to describe the distances between homologous genes and to analyze molecular evolution. Clustal is a common tool for multi-sequence alignment. It is an offline multi-sequence alignment tool based on progressive alignment. The simple principles are as follows: first, the distance matrix is constructed by the alignment of multiple sequences, inferring the relationship between the two sequences. Then, the phylogenetic tree is generated by calculating the distance matrix, weighting the closely related sequences. Next, starting from the closest two series, gradually introduce adjacent sequences, and reconstruct alignments until all sequences are added as shown in Fig. 5.3.

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Inputs multiple sequences by Clustal

Calculate the distance between sequences by fast alignment of sequence pairs and obtain a distance matrix

Constructing a Guide Tree by Adjacency Method

Progressive alignment of multiple sequences based on the boot tree

Fig. 5.3  Cluster multi-sequence alignment process

gous macromolecules of different species are compared (assuming that the structures of these macromolecules are known), the difference (amino acid or nucleotide substitution number) is only proportional to the independent evolution time of the compared organisms after mutation from the common ancestors. This difference is used to determine the position in the re-evolution of the compared species, and a phylogenetic tree is thus created. The common method used to build evolutionary trees is agglomerative hierarchical clustering. This method represents different classes with a distance matrix. The algorithm merges any two closest classes into a combined partition at each loop and then merges all classes step by step into only one large class. It should be noted that the evolutionary tree is only an intuitive way to represent the evolutionary relationship. It cannot guarantee that it can genuinely reflect the real changes in the evolutionary process. We need to integrate other means for comprehensive analysis.

5.2.3 Molecular Polygenetic Tree Assuming that the rate of evolution of biological macromolecules is relatively constant, the amount of evolutionary change in macromolecules is positively correlated with the time it takes for macromolecular evolution. In other words, the evolutionary change of macromolecules is a function of evolutionary time, so it can be used as an indicator to measure the genetic relationship between different evolutionary units (such as species). If the primary structures of homolo-

5.2.4 Comparative Genomics Comparative genomics is a discipline based on genome mapping and sequencing, which compares known genes and genome structures to understand gene function, expression mechanism, and species evolution. Using the coding sequence and structural homology between model organism genome or human genome, we can clone human disease

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genes, reveal gene function and molecular mechanism of diseases, and elucidate the evolutionary relationship of species and internal structure of the genome. Through bioinformatics, we compare the genome sequences with similar evolutionary relationships; find new genes, non-coding RNAs and regulatory sequences, etc.; and try to use the features formed by evolutionary selection to reach various aspects of their sequences, for example, the similarity of the sequence, the location of the gene, the length and number of the coding region of the gene, the proportion of the non-coding region in each genome, and more. For instance, when compared with the human genome, the number of bases in the mouse is roughly the same, but the number of genes is far apart. At the same time, the genetic structure and activity of human beings are far different from those of mice. A little bit unveiled the superiority of human beings on the road to evolution.

5.3

Transcriptomics Data Analysis

5.3.1 Gene Expression Profile Analysis Gene expression process is also part of the genetic information for an organism. It reflects various functional states of the gene and is regulated by factors such as time and space. Therefore, it is necessary to study the gene as well as the inseparable expression aspects of the research gene. With the development of biochip technology, gene expression research has entered a high-throughput era, enabling the simultaneous study of the expression of thousands of genes. The gene chip data is derived from the fluorescence intensity signal of the hybridization points extracted from the high-density hybrid dot matrix of the gene chip. After systematic collection, processing, analyzing, screening with valid data, and aggregation of relevant gene expression profiles, the cells at the hybridization point are finally integrated. Learn information, extract functional gene expression profiles, and lay the foundation for further research. However, in the face of such a huge amount of data on gene chips, we extract effective data, link the hybrid information of thousands of gene points with organic life activities, and characterize the characteristics and laws of life and the function of genes. The way to clarify is bioinformatics that is worth considering. In particular, the problem begins primarily with the following aspects: Raw Data Processing  Gene chip data is generally stored in text format, and the data appears in a matrix. Each column represents a different experiment. Each row represents the expression profile of each gene or probe under different experimental conditions. The standard format is used to rep-

resent basic information and to share gene chip data. In 2016, an article published in Nature Protocols called transcript-­ level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown introduced the analysis method of transcriptome data, mainly using the following three software programs: (1) HISAT (https://ccb.jhu.edu/ software/hisat/index.shtml) – this software uses a large number of FM indexes to cover the entire genome, enabling a rapid comparison of RNA-seq reads with genomes, compared to software speeds such as STAR Fast. (2) StringTie (http://ccb.jhu.edu/software/stringtie)  – this software is capable of applying transcript neural network algorithms and optional de novo assembly for transcript assembly and predicting expression levels. Compared with programs such as Cufflinks, StringTie achieves more complete and accurate gene reconstruction and better predicts expression levels. (3) Ballgown (https://github.com/alyssafrazee/ballgown) is a gene expression analysis in R language. The tool can predict the differential expression of genes and transcripts using the results of RNA-seq experimental data (StringTie, RSEM, Cufflinks). However, Ballgown does not detect differential exons well, and DEXSeq, rMATS, and MISO can make up for the above deficiencies. The detailed analysis steps are as follows: (1) Use HISAT to match the read fragments to the reference genome and annotate, but HISAT will still detect the clipping sites not listed in the annotation file. (2) The reads on the alignment will be presented to StringTie for transcript assembly, and StringTie will assemble each sample separately while estimating the expression level of each gene and isoform during assembly. (3) All transcripts are presented to StringTie’s merge function to merge, and then create a transcript of all the samples, to facilitate the next step of comparison. (4) The merged data is once again presented to StringTie. In addition to using the merge data to re-estimate the transcript abundance, StringTie can additionally provide data on the number of transcripts reads to the next Ballgown. (5) Ballgown obtained all transcripts and their abundance from the previous step and classified them according to experimental conditions [2]. Genome Annotation  The genome annotation is a hotspot of current functional genomics research using bioinformatics methods and tools to perform high-throughput annotation of the biological functions of all genes in the genome. The research of genome annotation includes two aspects: gene recognition and gene function annotation. The core of gene recognition is to determine the exact location of all genes in a genome-wide sequence. The prediction of new genes from genomic sequences is mainly based on a combination of three methods: (1) analysis of mRNA and EST data to obtain results directly; (2) indirect evidence from known genes and protein sequences through similarity alignment; and (3) De

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novo prediction based on various statistical models and aglorithms. High-throughput functional annotation of predicted genes can be annotated with new annotations using annotation information from known functional genes: (1) sequence database similarity search; (2) sequence motif search; and (3) clustering of orthologous group (COG). With the accelerated rate of microbial genome-­wide sequencing, it is necessary to develop an efficient, comprehensive genome annotation system with a Web interface. In recent years, there have been some such tools in the world, such as the Java-based microbial genome database (JMGD) interface. Although JMGD provides a good graphical interface program, it does not have automatic genome annotation. The Protein Extraction, Description, and Analysis Tool (PEDANT) developed by the German National Center for Environmental and Health Research is a large-scale genomic analysis system that integrates many genomic functional information and structural information. PEDANT annotations are powerful and versatile, but there is no easy-to-use graphical interface and a strong hardware system support. At present, the complete sequencing of microbial genomes is usually done independently by small and medium laboratories. It is necessary to develop and integrate a genomic information annotation system based on Linux system with a free database management system, free software, and public database resources. Differential Gene Expression Analysis  Differential gene refers to a gene that expresses a significant difference in RNA level at different levels of environmental stress, time, space, etc. Changes in structure, gene mutation, or methylation under different factors lead to the differential expression of genes. One of the purposes of the gene expression profiling chip experiment is to find differentially expressed genes between two samples, usually using the ratio of the signal in the experimental group and the control group as a measure of the difference in gene expression between the two states, in a two-color fluorescence system. The ratio of Cy5/Cy3 is used to measure the difference in gene expression, also known as the expression difference value. In a short oligonucleotide chip such as Affymetrix, a single-color fluorescent labeling method is used, and the experimental group and the control group are respectively detected by two chips, and the difference value is the signal ratio of the two chips. It is worth noting that the noise and some factors of the chip itself and the characteristics of the biology itself bring great trouble to the screening of differentially expressed genes. It is necessary to set a criterion for the differentially expressed genes. This screening criterion is called the differentially expressed gene threshold. According to different research purposes, gene chip data analysis methods mainly include the following categories:

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• Fold Change Fold analysis is the fastest method applied to the analysis of gene chip data, classifying the ratio of gene chips from large to small, the ratio being the ratio of cy3/cy5, also called R/G value. In general, there is no significant difference in gene expression in the range of 0.5 to 2.0, and gene expression is thought to change significantly outside the range. Because the experimental conditions are different, this threshold range must be adjusted according to the confidence interval. The information obtained after processing is output in different formats depending on different requirements, such as column charts, pie charts, and dot charts. The advantage of this method is that the necessary chips can save research costs. The downside is that the conclusions are too simple, and it is difficult to find clues of high-level functionality. In addition to genes with very large fold changes, the reliability of other small changes is valuable. This method may be suitable for preliminary experiments or screening. Also, the multiple values are arbitrary and may be inappropriate. For example, if a differentially expressed gene is screened twice, no one will be selected, the sensitivity will be zero, and many differentially expressed genes may also appear. The results led to the belief that the multiple screening methods were blindly speculated. • T-test Another method of differential gene expression analysis is a t-test, where two samples for comparison are considered different if t exceeds a criterion selected based on confidence. However, t-tests are often limited by sample size. Due to the high cost of gene chips, it takes time to repeat experiments. Small sample microarray experiments are very common, but small samples can lead to unreliable mutation estimation. To overcome this shortcoming, researchers have proposed a regulated t-test based on the existence of a correlation between gene expression levels and mutations. Bayesian conditional probabilities (Bayes’ theorem) statistical methods can supplement the estimation of the variability of any gene by detecting the expression levels of other genes adjacent to the same chip. This method is superior to the simple t-test and corrects the multiple analysis of the standard deviation of gene expression. • Nonparametric Analysis Because the microarray data have “noise” interference and do not satisfy the hypothesis of a normal distribution, it may be risky to use t-test and regression model for screening. The non-parametric test does not require data to fulfill the assumption of particular distribution, so it is feasible to use the non-parametric method to filter variables, although extensive. At present, in addition to the traditional non-parametric t-test and Wilcoxon rank-sum test, some new non-parametric methods are also applied to the analysis of gene expression profile data, such as empirical Bayes method and sig-

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nificance analysis of microarray (SAM). The disadvantage of the parametric method is that there are hypothesis tests for the analysis data. For example, changing the variation in the sample can significantly affect the results of the analysis, and the transformation of the same data (such as logarithm) can also have a significant impact on the results of the analysis. The nonparametric method is more effective for this situation, but it is less sensitive to express data analysis than the parametric method. • Regression Analysis Some simple parametric analysis methods currently used are based on data transformation (such as logarithm) to achieve a normal distribution as a premise, or an estimated empirical distribution. However, these two methods may be unreasonable for gene expression data. The non-parametric method ignores the distribution of data, and the parameter method misjudges the distribution of data. Regression analysis of gene expression profiles is a statistical method that can handle the linear dependence of multiple genetic variables. So the researchers proposed using regression analysis of gene expression profiling data or using the cross-variable (Cox) regression method to analyze gene expression profile data for patient survival prediction. Graphics  With the exponential growth of gene sequence, the graphical visualization method of gene sequence has become an important means to study gene sequence. It is more helpful for us to classify genes and analyze their evolutionary relationship by using graphical expression method. The graphical types of common gene sequences mainly include the following:

area) represents a graph of a collection and its relationships. It can be used to represent the approximate relationship between multiple data sets, but also to perform set operations. There are many tools for drawing Venn diagrams, such as the online drawing tool VENNY2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html). • Volcano Plot Volcano Plot (Fig.  5.5) is a type of image used to show differences between groups because most of the gene expression is none or very small from a global perspective when the organism changes. Only a small part of the gene expression has undergone significant changes. Therefore, volcano maps are commonly used in RNA expression profiling and chip data analysis and are most commonly used to analyze differential expression of genes and can also be applied in other omics. The essence of the volcano map is a plus version of the scatter plot, which contains two important concepts: (1) significant, that is, p-value, the difference test p-values of the two sets of samples, with negative log-log10 (p-value) conversion as the ordinate; (2) with log2 (Fold Change) as the horizontal axis, you can get the volcano map. Using certain screening conditions (such as Fold Change greater than 2 times, significant P value less than 0.05), you can filter out significantly differentially expressed genes for follow-up studies. There are many tools for drawing volcano maps, such as GraphPad Prism. • Bar Chart (Fig.  5.6) is a statistical report graph of the expression level that is visualized by the length of the rectangle. The data distribution is represented by a series of vertical stripes with different heights. It usually shows one variable with one or more values (different times or up:639

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• Venn Diagram (Fig.  5.4) It was first used by Venn in 1880  in the article “On the Graphical and Mechanized Performance of Propositions and Reasoning” in the form of a fixed-position cross-ring. A closed curve (internal

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Fig. 5.4  Venn Diagram

Fig. 5.5  Volcano Plot

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up-regulated

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sis (ORA) was only for a group of genes, and the development of high-throughput omics data made functional class scoring (FCS) come into being. The better improvement and understanding of biological pathways and complex networks have led to the development of pathway topology (PT) and network topology (NT) based methods. Gene functional enrichment analysis mainly includes KEGG analysis, GO analysis, signal pathway analysis, and drug target analysis.

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Kyoto Encyclopedia of Genes and Genomes (KEGG)  It is a database that integrates information on genomic, bio107 chemical, and systemic functions to reveal the genetic and biochemical blueprints of life phenomena. It is a manually 79 created knowledge base on knowledge of system functions. It is formed using a computable form of capture and organization of experimental knowledge. In addition, KEGG has powerful graphics capabilities that use graphics to introduce numerous metabolic pathways and relationships between pathways. Genomic information is stored in the GENES database, including complete and partially sequenced male female genomic sequences; functional information is stored in the PATHWAY database, including graphical cellular biochemiFig. 5.6  Bar Chart cal processes such as metabolism, membrane transport, signaling, cell cycle, and concomitant sub-pathways different conditions) for smaller dataset analysis. The bar information; the LIGAND database includes information graph can also be arranged horizontally or in multiple about chemicals, enzyme molecules, enzyme reactions, and ­dimensions. There are many drawing tools for histograms, more. By connecting with other large bioinformatics datasuch as EXCEL. bases in the world, KEGG can provide researchers with more extensive biological information. KEGG provides Java’s graphical tools for accessing genomic maps, comparing 5.3.2 Functional Enrichment Analysis genomic maps and operational expression maps, as well as other tools for sequence comparison, graph comparison, and With the rapid development of high throughput sequencing pathway computation. KEGG established the KEGG technology and the wide application of related technologies, Orthology (KO) System to develop and facilitate cross-­ biomedical related research fields have entered the post-­ species annotation processes by linking molecular network genome era in which large-scale omics data has grown expo- related information to the genome. nentially. On the one hand, this has enabled biomedical research to shift from the analysis of individual genes to the Gene Ontology Analysis  Today’s biologists waste too research at the system level, which has an important impetus much time and energy searching for biometric information. to reveal the basic molecular mechanisms of biomedicine. This situation boils down to the reason of the confusing bioOn the other hand, such a large amount of data also pose a logical definitions: not only is it difficult for computers to huge challenge to extraction and analysis of such huge accurately find definitions which randomly changes over amount of data. In order to discover patterns from complex time and changes because of multiple human factors; even if omics data, researchers often perform enrichment analysis of it is completely handled by humans, it is still almost imposgene functions, expecting to discover biological pathways sible to do. The Gene Ontology (GO) project is the result of that play a key role in biological processes, thereby revealing efforts to align functional descriptions of gene products in and understanding the basic molecular mechanisms of bio- various databases. The project was originally initiated by the logical processes. The enrichment analysis of gene function integration of three model biological databases in 1988: has become a routine means of functional omics data analy- FlyBase (Drosophila), Saccharomyces Genome Database sis, and with the development of high-throughput omics (SGD), and the Mouse Genome Database (MGD). Since data, such as the change from gene chip data to RNA-seq then, GO has grown and expanded, and now contains dozens data, a series of corresponding analytical methods have been of databases of animals, plants, and microbes. Gene Ontology developed. The earliest developed over-representation analy- can be divided into three parts: molecular function, biologi-

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cal process, and cellular component. The GO analysis calculates the hypergeometric distribution relationship between these differential genes and one or several specific branches of the GO classification based on the selected differential genes. The GO analysis returns a p-value for each GO with the presence of the differential gene. The p-value indicates that the differential gene is enriched in the GO. The GO analysis can be suggestive on the experimental results. Through GO analysis of differential genes, we can find GO classification entries that enriched for differential genes. It can help to understand which gene function is related to which differentially expressed gene in different samples. Molecular Function  Molecular function describes activity in molecular biology, such as catalytic activity or binding activity. GO molecular functions define functions rather than the whole molecule, and do not specifically indicate the specific temporal and spatial information of these functions. Molecular function mostly refers to the function of a single gene product, and a small part of it also refers to the function of a complex formed by this gene product. The definitions of the function include catalytic activity, transport activity, binding activity, etc., and more narrow definitions include adenylate cyclase activity or bell-shaped receptor binding activity. Biological Process  Biological pathways are multi-step processes with an ordered structure of molecular functions. For example, cell growth and maintenance and signal transduction are relatively extensive. Some more specific examples include transport such as pyrimidine metabolism or alpha glycosyl groups. Biological processes are not exactly equal to biological pathways. Therefore, GO does not include complex mechanisms or path dependencies. Cellular Component  The intracellular location refers to the organelle or gene product group where the gene product is located (e.g., rough endoplasmic reticulum, nucleus or ribosome, proteasome, etc.). Pathway Analysis  Pathway analysis calculates the hypergeometric distribution of selected differential genes. It returns a p-value for each path with a differential gene, and a small p-value indicates that the differential gene is enriched in the pathway. The pathway analysis is suggestive on the experimental results. Pathway analysis of differential genes can be used to find pathway entries with enriched differential genes, and to find out which differential genes may be involved in the change of pathways of different samples. Unlike GO analysis, the results of pathway analysis are more indirect because pathways are interactions between proteins, and changes in pathways can be caused by changes in the amount of protein involved in the pathway or the activity of

C. He and Y. Duan

the protein involved in the pathway. The result obtained by the chip is the change in the amount of mRNA expression encoding these proteins. From mRNA to protein expression, microRNA regulation, translational regulation, post-­ translational modification (such as glycosylation, phosphorylation), protein transport, and other processes, there is often no linear relationship between mRNA expression and protein expression. Therefore, a change in mRNA does not necessarily mean a change in the amount of protein expressed. It should also be noted that in certain pathways, such as the EGF/EGFR pathway, cells can regulate this pathway by altering the degree of protein phosphorylation (regulating protein activity) while maintaining the same amount of protein. Therefore, the results of the chip data pathway analysis need to be supported by following protein function experiments, such as Western blot/ELISA, IHC (immunohistochemistry), overexpression, RNAi (RNA interference), knockout (gene knockout), transgene (genetically modified), and so on.

5.3.3 Timing Analysis The gene regulatory network is a continuous and complex dynamic system. The regulation between genes is a dynamic event that changes with time and environment. The genomic DNA microarray provides researchers with a good tool for understanding the regulatory network. The time-series gene expression data contains abundant gene regulation information. It is conceivable that the changes in genes located upstream of the gene regulatory network should be in front of the downstream genes. When the expression of upstream genes (such as transcription factor TF) changes, this change will spread along with the gene regulatory network. When the expression level of each gene at different times is measured, important information about the order of regulation between genes and the regulatory targets can be derived from such time-series data. In general, time-series gene expression data contain a larger amount of information that is derived from the gene regulatory network than static gene expression data of the same size. Time series analysis is a method of analyzing the development process, direction, and trend of time series and predicting the possible future targets in the time domain. This method uses the principle and technique of time series analysis in probability and statistics, uses the data correlation of the time series system, establishes the corresponding mathematical model, and describes the timing state of the system to predict the future. For genomics, on the one hand, time series analysis is a method of gene clustering. For example, for transcriptome data analysis, finding genes that are significant at different points is a frequently used clustering method. Commonly used analytical methods include Short Time-series

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Expression Miner (STEM), which is a clustering method different from the expression change. It is characterized by the ability to derive important information about the order of regulation between genes and the regulatory targets. It is generally believed that the time-series gene expression data contains a larger amount of information for deriving the gene regulatory network than the same size of static gene expression data; on the other hand, the time series analysis, due to the addition of time factors, can change over time. Changes in gene expression in the life system are also dynamic and related to the surrounding time points. And this relationship can be pre-calculated (called the expected value), and then compared with the expected value of the experimental data.

5.3.4 Gene Co-expression Network Analysis Gene co-expression network analysis is a network diagram based on the similarity of gene expression data, with different colored dots representing genes with similar expression profiles. Genes are linked to form a network. The construction of the co-expression network is conceptually simple and intuitive. Through the similarity of gene expression, the possible interactions of gene products can be analyzed, so as to understand the inter-gene interactions and find the core genes. The core gene is an important hub and plays a key role in the network module. Weighted gene co-expression network analysis (WGCNA) is a commonly used method for co-expression network analysis. It first assumes that the gene network obeys the scale-­ free distribution and defines the adjacency function with the gene co-expression correlation matrix and gene network structure. Then it computes the distinct coefficients of the different nodes and builds a system clustering tree accordingly. The different branches of the cluster tree represent different gene modules, and the degree of co-expression of genes in the module is high, while the degree of co-­expression of genes belonging to different modules is low. If certain genes always have similar expression changes in a physiological process or different tissues, then we have reason to believe that these genes are functionally related and can be defined as a module. When the gene module is defined, we can use these results to do a lot of further work, such as screening the core genes of the module, correlating traits, modeling metabolic pathways, and establishing genetic interaction networks. WGCNA can quickly extract gene co-­ expression modules associated with sample features from complex data (N multi-packets) for subsequent analysis. Simply put, it calculates the expression correlation between genes, clusters genes with expression correlation into a module, and then analyzes the module and sample characteristics (including clinical features, surgical methods, treatment

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methods, etc.). Between the correlations, WGCNA built a bridge between sample characteristics and changes in gene expression [3].

5.3.5 Analysis of Transcriptional Regulation The regulation of gene expression in eukaryotes is an extremely complex process that requires a precise interaction of a series of transcriptional regulators and DNA sequences. If these non-coding transcriptional regulatory abnormalities such as mutations can cause many diseases, screening analysis and functional exploration of these transcriptional elements are necessary for gene expression regulation and biological function research. A commonly used analysis software is TRRUST (https://www.grnpedia.org/trrust/), which is a database that records the regulatory relationships of transcription factors, including not only the target genes corresponding to transcription factors but also the regulatory relationships between transcription factors. Currently, the database only stores regulatory information related to humans and mice. There are also many software programs such as AnimalTFDB, HumanBase (https://hb.flatironinstitute.org/), and Dire (https://dire.dcode.org/).

5.4

Protein Structure Analysis

5.4.1 Protein Structure Prediction Protein is the embodiment of life activities, and its structure determines its function. Proteins composed of linear amino acids need to be folded into specific spatial structures to have corresponding physiological activities and biological functions. The analysis of the spatial structure of proteins is important for understanding the function of proteins, the execution of functions, the interaction between biological macromolecules, and the development of medicine and pharmacy (such as the design of drug targets). In order to understand protein function more quickly, you can’t just wait for the results of protein determination, especially before researching unknown proteins. There are obvious advantages by predicting protein structure first. The study of protein structure and function has a long history. Due to its complexity, the prediction of its structure and function is complicated both in methodology and in basic theory. Statistical methods have been successfully applied to the prediction of protein secondary structure. The empirical parameter method proposed by Chou and Fasman is the most prominent example. The method statistically analyzed the secondary structure distribution characteristics of various amino acids, and obtained corresponding parameters (Pα, Pβ, and Pt) and used for prediction. In general, the

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structure of a protein is divided into four levels: 1 primary structure: protein sequence; 2 secondary structure: α-helix and β-sheets mode; 3 tertiary structure: residues in space layout; 4 four-level structure: interaction between proteins. In recent years, another layer of protein structure between the secondary and tertiary structures, the so-called protein fold, has proven to be very useful. “fold” describes a hybrid combination of secondary structural elements. The technique for predicting protein secondary structure based on sequence or multiple sequence alignment has been relatively mature, but the prediction of tertiary structure is quite difficult. Often for tertiary structure predictions, this can only be done by homology alignment with known structural protein sequences. A number of related databases have been established for protein structure prediction. This method is the most accurate method for predicting the tertiary structure. But this method does not always work, because about 80% of known protein sequences cannot find similar protein sequences of known structure. Protein structure prediction mainly includes the following aspects: (1) protein physicochemical properties and primary structure analysis – analysis of protein PI, MW, amino acid composition, extinction coefficient, and stability coefficient often uses online analysis software such as Expasy (https://Web.expasy.org/protparam/). Expasy is also commonly used to analyze the hydrophilicity and hydrophobicity of proteins; the Tmpred method is commonly used to analyze the transmembrane region of proteins. It is based on the statistical analysis of the Tmbase database to predict protein transmembrane regions and transmembrane direction (http://www.ch.embnet.org/software/TMPRED_form.html); signal peptide prediction method SignalP (http://www.cbs.dtu.dk/services/ SignalP), which is a prediction of the secretory signal peptide, rather than the protein involved in intracellular signaling, is more than 90% accurate. (2) Common methods for protein secondary structure prediction include CFSSP (http://cho-­fas.sourceforge.net/) and PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/). (3) Protein domain prediction  – common methods include InterPro (http://www.ebi.ac.uk/ interpro/scan.html) and Pfam (http://pfam.xfam.org/). (4) Commonly used methods for protein tertiary structure prediction include two methods. The rationale for the protein structure homology modeling (also known as homology modeling) is that the tertiary structure of the protein is more conservative than the primary structure of the protein. The other is folding recognition. The basic principle is to identify the similar folding type from the protein structure database and the sequence to be tested, and then to realize the spatial structure prediction of the sequence to be tested. The number of protein folding types in nature is limited. Many proteins, although enjoying low sequence similarity, may still have the same folding type, which is the theoretical basis for folding recognition. The commonly used software

C. He and Y. Duan

is pGenThreader (http://bioinf.cs.ucl.ac.uk/psipred/) and Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/html/page. cgi?id = index). With the development of technology, it is believed that there will be more and more accurate new methods for the prediction of protein structure.

5.4.2 Protein-Protein Interaction The completion of the sequencing of the human genome marks the advent of a new era of biological research, the post-genomic era. The genome-wide sequence information is not enough to explain and speculate on various life phenomena of cells. Protein is the ultimate performer of cell activity and function. The complex interaction between proteins determines the complexity of organisms. The hottest research topics have gone back to the protein. Interacting proteins in the body’s cells can reveal the function of proteins at the molecular level and are essential for the regulation of growth, development, differentiation, apoptosis, and understanding of biological regulation mechanisms, providing important theoretical basis for exploring the mechanisms of major diseases treatment, prevention, and new drug ­development. Therefore, understanding and elucidating the mechanism of interaction between proteins is of great significance, and it is a hotspot in the cross-research of life sciences and other disciplines in the post-genome era. Common analysis software includes STRING (https://string-­db.org/) and BioGrid (https://thebiogrid.org/).

5.4.3 Protein Function Prediction Proteins are the most essential and versatile macromolecules in the body, and the understanding of their function is critical to the development of medical science. With the development of the post-genome era, a large number of protein sequences with unknown structures and functions have emerged in the NCBI database, and these protein sequences have already become a research hotspot. Commonly used protein function prediction software is Discovery Studio (http://www.discoverystudio.net/), a software for professional life science molecular model of a new generation of molecular modeling and simulation environment. It is mainly used for protein structure-function research and drug discovery. The main functions of DS include protein characterization (including protein-protein interaction), homology modeling, etc. DS can be applied to life science research fields, bioinformatics, structural biology, new drug discovery, etc. In the past 10  years, protein function prediction methods have been continuously improved, and it is believed that future protein function predictions will become more and more accurate.

5 Bioinformatics

5.5

Bioinformatics and Precision Medicine

With the rapid development of genomics, bioinformatics, and precision medicine, bioinformatics and medical informatics have grown more and more cross-integrated. Data analysis of multiple molecular groups must be combined with cells, tissues, organs, individuals, and even populations [4]. Information can accurately predict the occurrence and development of complex diseases and predict the applicable population of drug therapy, radiotherapy, chemotherapy, and immunotherapy. Therefore, the field of research and application of bioinformatics technology become even broader.

5.5.1 B  ioinformatics and Precision Medical Diagnosis Precision medicine is a very systematic project that analyzes the state of human disease and then combines clinical treatment to provide people with the best diagnosis and treatment. Nowadays, people’s awareness of health is improved. Therefore, the Chinese government is more dedicated to the prevention and treatment of major diseases. Various medical reforms are also underway to improve the understanding of risk mechanisms, and research and treatment programs for many diseases are underway. Big data combines digital imaging, systems biology, and information science to provide patients with a good treatment basis for each patient. The molecular profile and the information characteristics of the mutation are analyzed to develop a personalized treatment plan [5].

5.5.1.1 Genetic Testing A number of researchers such as Xiaoqiang Qiu [6] and Feng Shen [7] suggest that genetic testing can prevent diseases through genetic testing and genetic screening. This technology has a huge impact on human health. In many cases, the occurrence and development of human diseases are strongly related to gene mutations and have a strong connection with gene expression. In human genes, it contains a lot of chemical information, so the genetic testing of the human body can improve health management to a certain extent. Related studies have shown that there are about 4000 diseases that are closely related to genetic factors. In patients with heart disease, diabetes, and cancer, their genes can change. Related research and analysis show that there are three main reasons for the occurrence of a disease caused by genetic changes. First, under the influence of the environment, people can have genetic mutations, which will produce cancer cells under the interference of chemical substances. The second is the congenital defect of the gene. Under this defect, people’s

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genes will undergo local mutations. For example, some patients can have thalassemia and albinism at birth because these patients are born with a lack of enzymes. The third is that patients can have acquired genetic mutations, such as patients with hypertension and diabetes. These patients have found a wide range of genes in the process of genetic testing and found a large number of genes in the process of gene sequencing. Single nucleotides exhibit a polymorphic distribution. Nowadays, there are some new genomics indicators that can provide people with diagnostic information, such as genomic DNA sequences, proteomics, enzymology, etc. These substances involve omics information, so they can accurately and widely reflect the nature of the disease. Features can help medical staff to find out the cause in time to improve the accuracy of the drug in drug treatment.

5.5.1.2 Detection of Pathogenic Microorganisms Identification of Bacteria  At present, there are mainly two identification methods in the identification of bacteria, namely, phenotypic identification method and molecular genetic identification method [8]. However, with the continuous development of bioinformatics technology, bioinformatics technology can have a new identification ­ method, which has higher resolution and is called multi-site sequence analysis. This method is more convenient and faster than the two traditional methods of bacterial identification with the help of bioinformatics techniques for DNA sequencing. Another method of identification is called matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, which is a novel microbial identification method developed by the application of bioinformatics technology. It mainly identifies bacteria by the characteristics of bacterial protein fingerprints. In the process of identifying bacteria, the bacteria were identified by analyzing the pristine data of bacteria by Spectra Bank database and SPECLUST cluster analysis software in bioinformatics technology. Identification of Virus  A new type of virus identification technology developed by bioinformatics technology is called genomic barcode technology. The genomic barcode technology mainly uses a segment of the DNA of the virus, and then quickly and accurately identifies the species through this fragment. A new type of virus identification technology, genomic barcode technology can identify and classify various viruses. Compared with traditional single-gene sequence technology, the new genomic barcode technology can display information that cannot be expressed by single gene sequences. Another technique for the identification of viruses is high throughput sequencing, which enables the sequencing of millions of DNA molecules at a time. In the identification of viruses, GLC Genomic Workbench software is

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commonly used to analyze high throughput sequencing and generated DNA data. Meanwhile, some strategy for novel gene function of pathogen will rise subsequently [9]. Identification of Fungi  The application of bioinformatics technology in fungal identification is mainly realized by gene chip technology. Gene chip technology analyzes the information of these genes efficiently and accurately. Therefore, gene chip technology has a very good prospect in the identification of pathogenic organisms. The most commonly used analysis software in gene chip technology is Arraypro. Arraypro analysis software can analyze not only the image of genes but also process various data of genes. In the process of research and identification of human skin pathogenic fungi, biologists have identified the fungus by PCR microarray method with bioinformatics, and then analyzed the gene data using Arraypro software and Prism software. The accuracy rate is as high as 90% [10]. Identification of Parasites  The identification of parasites in pathogenic biology can be characterized by proteomics in bioinformatics to identify the protein composition of various life stages of parasites. The protein map of parasites in different life stages is important components for parasites identification. Biologists identified multiple new genes by proteomic analysis during the proteome analysis of Leishmania donovani. Through the research of bioinformatics technology, the protein profiles of Leishmania donovani at different developmental stages can be studied. Through this analysis, the drug targets against Leishmania donovani can be studied, and corresponding resistant drugs can be studied, promoting the development of biopharmaceutics. With the wide application of bioinformatics technology in multidisciplinary fields, bioinformatics technology has been applied in many fields of pathogenic biology research. In the future, bioinformatics will inevitably be in the field of pathogenic biology research and play an important role. But it requires constant exploration of this field. The application of bioinformatics technology in pathogenic biology can not only promote the development of pathogenic biology but also improve the early diagnosis rate of difficult diseases caused by rare pathogen infections and provide more reliable data for an accurate diagnosis. It has important significance for the survival and development of the human population.

5.5.2 B  ioinformatics and Precision Medical Prevention and Treatment Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases  In recent years, the number of patients with cerebrovascular diseases in China has increased year by year, and the morbidity and mortality of cerebrovas-

C. He and Y. Duan

cular diseases are very high, becoming the first killer of human health. According to related statistics, there are more than 70 million stroke patients in China, and more than 90% of them need lifelong treatment. Moreover, cardiovascular and cerebrovascular diseases can lead to various complications, such as elevated blood pressure, which can lead to death and disability. In the prevention of stroke, it is generally necessary to pay attention to the diagnosis of H-type hypertension, i.e., hypertension with elevated homocysteine (≥10 μmol/L). In the process of human cell metabolism, a kind of intermediate product is produced. If the product is very high in the blood of the human body, it will cause the blood pressure of the human body to rise. Under the influence of genetic factors, the level of folic acid in patients decreases, which directly leads to the development of hypertension and stroke. According to relevant research, there are 300 million hypertensive patients in China, and two-thirds of patients have H-type hypertension. In the primary prevention study of stroke in China, H-type hypertension was deeply explored, and the big data method was adopted to enable patients to use antihypertensive drugs accurately. Cancer Treatment  Cancer is a common type of disease. Related studies have shown that the cause of cancer is the mutations of cells. The gene imprinting and tumor markers of cancer patients can be analyzed by means of big data. In the process of genetic testing, risk can be larger, and genes are directly identified to interfere with the susceptibility of individual tumors. Big data has been comprehensively applied in the treatment, chemotherapy, targeting of diseases, and biological treatment. It has also begun to make bold attempts in the treatment of cancer, to a certain extent. To reduce mortality, related studies have also analyzed patients with advanced cancer, the patient’s expected life span is extended, and the patient’s life cycle is extended to give patients new hope that they can wait for new drug research to cure their disease.

5.5.3 Bioinformatics and the Future of Precision Medicine With the development of biotechnology such as high throughput sequencing technology, genome, transcriptome, epigenetic group, proteome, metabolome, microbiome, and other multi-omics sequencing data can be quickly produced within a few days and widely applied to the research and clinical applications of precision medicine; these large and complex data not only constitute the key information needed for precision medicine, but also they grow faster than any other types of data in the field. At present, the precision medicine program has been launched in many countries. Scientists from all over the world are working to collect

5 Bioinformatics

genomic information of 100,000 and millions of people to build an authoritative and new population health big data knowledge system. In this era, as a populous country, China’s precision medicine urgently needs to establish a genetic database belonging to China. In 2016, the Ministry of Science and Technology of China listed “Accurate Medical Research” as a major research project. The Chinese Academy of Sciences also launched the Chinese Population Accurate Medical Research Program in 2016. It is expected to complete the DNA samples of 4000 volunteers and the collection of various phenotypic data within 4 years. In this regard, we can foresee that with the development of large cohort research, multi-group big data represented by genomes will continue to be generated. But these big data itself cannot produce value. Its value lies in the disease phenotype and other information that can be further applied to medical and health decisions with means such as association rule mining, data mining, and knowledge discovery. Therefore, promoting the transformation of big data into knowledge and building an integrated platform system for the research and application of big gene data is a critical step in the field of precision medicine to the clinic. It can be applied to early screening, molecular typing, and individual diseases. It also involves multiple clinical decision-making scenarios, such as chemotherapy, efficacy prediction, and monitoring. The development and research of multi-group precision medicine big data also faces challenges such as data sharing and security privacy issues, integrated analysis of multidimensional omics data, standardization of biometric data analysis methods, and interpretation of standardization. Therefore, more national policy support, scholars in the fields of IT and artificial intelligence, and experts in various fields are needed to develop industry standards and ultimately promote the development of precision medicine to automation, intelligence, standardization, and simplification. Around 2008, the international academic community proposed the concept of translational bioinformatics, which aims to apply the data, models, and software developed in bioinformatics to the issues of translational medicine. Furthermore, Chowkwanyun et  al. [11] also published a paper promoting Precision Medicine in public health. Conceptually, bioinformatics is mainly a new discipline formed by the development of the human genome project and multi-molecular omics technology, while traditional biomedical informatics is much broader in connotation than bioinformatics. It is not only the molecular level, but also the image data analysis at the cell and tissue level, the clinical electronic medical record analysis at the individual level of the patient, and the epidemiological data analysis at the pop-

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ulation level. Therefore, the translational biomedical informatics is particularly important in the context of today’s big data and precision medicine and also gives birth to the more advanced technology such as next-generation gene sequencing technology (NGS) mentioned in Morganti’s published articles [12]. The new paradigm for translational biomedical informatics will fully integrate molecular data and clinical medical data, especially the fine clinical phenotypic information, to predict disease development and individualized therapeutic effects through precision and modeling. It will lead to the development of medicine with surprising results. In the face of the advent of the aging society, we saw the trend of turning from clinical treatment to health management. The application of translational biomedical informatics will play an important role in predicting the trend of diseases. In short, we believe that the integration and transformation of bioinformatics will make precision medicine become more mature and perfect in revealing the mysteries of life. It will play an increasingly important role to promote biology and medicine to enter a new realm.

References 1. Pan S.  Clinical molecular diagnostics. Beijing: People’s Medical Publishing House; 2013. 2. Pertea M, Kim D, Pertea GM, et  al. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11:1650–67. 3. Wang M, Ji Z, Wang S, et  al. Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies. Bioinformatics (Oxford, England). 2017;33:3716–25. 4. Ma W.  Medical molecular biology. Beijing: Higher Education Press; 2008. 5. Chen J, Lin Y, Shen B.  Informatics for precision medicine and healthcare. Adv Exp Med Biol. 2017;1005:1–20. 6. Qiu X. The change of clinical research thinking in the era of big data and precision medicine. Chin J Cancer Prev Control. 2017;9:85–9. 7. Shen F, Cheng Z. Clinical research for hepatocellular carcinoma: opportunities and challenges in the age of precision medicine and big data. Chin J Pract Surg. 2016;36:599–602. 8. Wang J, Cheng Y, Chen C. Application of bioinformatics in hospital infection. Chin J Nosocomiol. 2014;16:4158–60. 9. Du X, Huang J. Strategy for pathogen novel gene function research. Chin J Zoonoses. 2016;32:665–9. 10. Wu J, Wang P, Liu Y. Metagenome and detection of animal microbial pathogens. Chin J Comp Med. 2014;9:72–7. 11. Chowkwanyun M, Bayer R, Galea S. “Precision” public health-­ between novelty and hype. N Engl J Med. 2018;379(15):1398–400. 12. Morganti S, Tarantino P, Ferraro E, et  al. Complexity of genome sequencing and reporting: next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol. 2019;133:171–82.

6

Report and Consultation Yongqing Tong

6.1

Overview

Accurate test results depend on high-quality specimens. The collection of specimens plays a very important role in ensuring the accuracy of test results. Without qualified specimens, accurate results cannot be detected. Therefore, the first step in comprehensive quality control is to ensure high-quality specimens. In the quality control work before molecular diagnostic analysis, proper processing during specimen collection is the key to ensuring sample integrity, checking qualitative detection of nucleic acids, and accurate quantitative detection. Improper handling may result in degradation of the nucleic acid, or false negatives or low levels of detection (in quantitative testing). In addition, specimen collection must also comply with the requirements of the appropriate biosafety guidelines. This chapter focuses on pre-acquisition considerations and acquisition procedures for common types of specimens in clinical molecular diagnostics.

6.2

Genetic Variation and Description

Genetics and variation [1] are the basis of species formation and biological evolution. The genetic material deoxyribonucleic acid (DNA) is transmitted to the offspring through the parent, so that the offspring present similar traits to the previous generation, keeping the species relatively stable. At the same time, there are differences between the parents and the offspring and offspring, which are not exactly the same. This phenomenon is called variation. Variants are classified into heritable and nongenetic variations [2]. Genetically mutated is a variation caused by changes in hereditary material, including genetic mutations, genetic recombination, and chromosomal variation (Fig.  6.1). Among them, genetic mutation is the fundamental source of new biological genes and the Y. Tong (*) Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China

fundamental source of biodiversity. Nongenetic variation is caused by the environment and mainly includes variations caused by external factors such as light and water sources. The mutations in the human genome are mainly divided into three categories: the first type of variation is single nucleotide polymorphism (SNP); the second type of variation is a small deletion or insertion (Indel), which refers to the insertion or deletion of a small fragment sequence occurring at a certain position in the genome, and the length is usually less than 50 bp; the third type of variation is a large structural variation, which is more than one type, including insertion or deletion of long fragment sequences of 50  bp or longer, chromosomal inversion, sequence translocation within chromosomes or between chromosomes, copy number variation, And some more complex forms of variation, etc. In order to distinguish from SNP mutations, the second and third types of variation are often referred to as genomic structural variations (SV). Compared with SNPs, SV has a greater impact on the genome and can be used to explain the characteristics of human population diversity. Some rare and identical structural mutations are often associated with the occurrence of diseases (including some cancers) and even pathogenic causes. Genetic variation is an academic naming of genetically altered sequence variations in DNA nucleotide sequences in cells. The Human Genome Variation Society (HGVS) has established a systematic method for naming gene mutations. Specific gene mutation naming methods can be found at http:// www.HGVS.org/varnomen. HGVS gene mutation naming guidelines are constantly updated as needed. This article is based on the v15.11 version updated in February 2016 [3]. The naming of human genes mainly includes gene names and gene symbols. According to the Human Gene Nomenclature Committee (HGNC) guidelines for human gene naming (updated 2016), the naming of human genes should follow six basic principles: first, the uniqueness of each gene’s symbol; second, the gene symbol is an ­abbreviation of the gene name, generally no more than 6 letters; third, the gene symbol should be the Latin alphabet or it

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_6

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Y. Tong Inherited

Father has mutation in all cells and transmits it to his child. Child is heterozygous in every cell.

Father has mosatic mutation that affects germline and somatic cells. Child is heterozygous in every cell.

Father has germline mosatic mutation. Child is heterozygous in every cell.

De novo

Father has mutation in a single sperm cell and transmits it to the child. Child is heterozygous in every cell.

Mutation occurs in zygote within first few cell divisions. Child is heterozygous in every cell.

Somatic

Child has mosatic mutation that occurs early in postzygotic development and is present in a percentage of his cells.

Child has mosatic mutation that occurs later in development and affects fewer cells(e.g skin cells).

Fig. 6.1  Inherited diagram of some of the ways mutations

is combined with Arabic numerals; fourth, the gene symbol should not contain punctuation; fifth, the gene symbol should not end with G; sixth, the gene symbol does not involve other species. Gene full name naming rules: ① the beginning of the name applies lowercase letters, but there are three exceptions, that is, the name of the disease, the phenotype, or the initials of the initials; ② if there is an alias, it should be included in the name, and brackets; ③ if it is the name of another species, it must be written at the end and marked with brackets. Gene symbol naming rules: ① human gene symbols are upper-

case Latin letters or their combination with Arabic numerals (except C#, ORF# symbols). No Roman numerals are used (the Roman numbers used in the past are to be changed to equivalent Arabic numerals); ② gene symbols are applied in italics when writing. But in the directory exception; ③ Greek letters are not used as gene symbols. All Greek letters used in the past should be converted to Latin letters; ④ gene names prefixed with Greek letters should be converted to equivalent Latin letters and placed at the end of the gene symbol, genes with similar properties can be arranged in alphabetical order; ⑤ ­punctuation not be used (except for HIJA immunoglobulin

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and T cell receptor gene symbols can be used with sub-words); ⑥ gene symbols usually do not represent selective transcripts, but when a group has multiple small coding sequences to form a variety of different large gene products, these small coding sequences can be represented by different symbols; ⑦ the representation of tissue specificity or molecular weight should be avoided. ⑧ Certain letters or combinations of letters should be avoided as the pre- and post-suffix of the gene symbol to try to give a specific meaning; ⑨ the symbol of the oncogene corresponds to the retrovirus homologous oncogene, but the gene symbol does not have a “v-” or “c-” prefix. In addition, for some customary gene names, the previous forms, such as survivin and p53, can be used in lowercase, italic represent genes, standard bodies represent proteins; BCL-2 or Bcl-2, c-Myc, mixed case, italics represent genes and standard bodies represent proteins.

ber or the gene symbol recommended by the Human Genism Organization (HUGO) Gene Nomenclature Committee, the location of the mutation, and the type of variation. For example, “NG_007938.1:g.12083G>A”, “NG_007938.1” is the nucleic acid sequence acceptance number and version, “g.12083” indicates the position in the nucleic acid sequence, and “G > A” indicates that the original base is G The variant base is A. The HUGO gene symbol is used to describe, for example, “GJB2:c.76A>C”. Such a naming method facilitates finding the location in the gene sequence in which it is located. If the mutation occurs only in a sequence or gene, the nucleic acid sequence or gene symbol can be omitted after the first occurrence, but if there are different sequences or genes that mutate, each description needs to be written.

6.2.1 The Level of Sequence Variation

To avoid confusion you need to indicate the sequence type when describing sequence variations. g represents the genomic sequence, c represents the coding DNA, m represents the mitochondrial sequence, r represents the RNA sequence, and p represents the protein sequence. For example, g.476A>T, c.76A>T, m.8993T>C, r.76a>u, p.Lys76Asn.

The goal of HGVS’s norm is to make all variant descriptions unique, stable, meaningful, memorable, and unambiguous. All mutations occur eventually with changes in DNA levels that cause changes in the corresponding RNA or protein levels. Therefore, the most basic rule in describing the mutation is that when the first description of the mutation occurs, the DNA level variation must be written, and the corresponding RNA and protein variations can be described in parentheses, for example, “c.78G>C(p. Trp26Cys).” The four bases AGCT involved in DNA variation need to be capitalized, while the agcu in RNA needs to be lowercase. Amino acids in protein level are recommended with a three-letter abbreviation because single-letter abbreviations are ambiguous (e.g., Ala, Arg, Asn, and Asp, all of which begin with the A letter, Gln, Glu, and GLy, all of which begin with the G letter). When several variations are expressed at the same time, they should be listed. Variations are clearly expressed at the three levels of DNA, RNA, and protein, and changes in RNA and protein levels should be clearly demonstrated experimentally or theoretically. When the mutation occurs in patients with recessive genetic disease, it should also indicate whether the mutation is homozygous or heterozygous.

6.2.2 Content of the Variant Description Human Genome Variation Society (HGVS), is a nongovernmental academic organization whose website is available at http://www.hgvs.org/. The rule for the HGVS nomenclature SNP method is tantamount to indicate the referenced nucleic acid sequence number (Reference Sequence, RefSeq) and the position of the SNP in the nucleic acid sequence. The description of the nucleic acid sequence variation includes three parts, the cited nucleic acid sequence num-

6.2.3 Types of Variant Sequences

6.2.4 E  xpression of Variant Types Specific Abbreviations Are Used to Describe Different Types of Sequence Variations ① “>” indicates base substitution (DNA and RNA levels); e.g., g.123456G>A, r.123c>u. Substitution at the protein level is described as p.Ser321Arg. ② “_” is used to define the range of the variant base, e.g., “c.76_78delACT” indicates the deletion of the 76–78 base (ACT) of the coding DNA. ③ “del” represents a base deletion, such as “c.135_137delTTA” indicating a deletion of the coding DNA 135–137 base (TTA). “ins” represents a base insertion. For example, “c.76_77insG” indicates that the base G is inserted between 76 and 77 bases of the coding DNA. ④ “dup” represents a repeat of the same base (repetitive insertion is described as a repeat, but cannot be expressed by an insertion variant, e.g., the sequence ACTTTGTGCC mutation to ACTTTGTGGCC cannot be described as c.8_9insG, but should be described as c.8dupG). ⑤ “delins” or deletion/insertions (indels) represent insertional deletions, e.g., p.Cys28_Lys29delinsTrp represents 3 bases at codon 28 (encoding cysteine Cys) and codon 29 (encoding lysine Lys). Deletion of the base results in the replacement of these two amino acids by tryptophan. ⑥ “inv” stands for inversion, such as c.203_506inv, indicating that a total of 304 nucleotides have been inverted from 203 to 506.

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⑦ “con” stands for conversion, e.g., g.123_678con NG_012232.1:g.9456_10011, indicating that the 9546 to 10011 nucleotide sequence in the reference sequence NG_012232.1 in Genbank replaces the nucleotide sequence of 123 to 678 in the reference sequence. ⑧ “fs” means frame shift; e.g., p.Arg456GlyfsTer17 (or p.Arg456Glyfs*17). ⑨ “[]” represents an allele, e.g., c. [76A>C; 83G>C] indicates that one gene in a chromosome has both c.76A>C and c.83G>C mutations. c. [76A>C]; [83G>C] indicates that one variation originates from the mother, and one variation originates from the father, i.e., 76A>C mutation occurs in one chromosome, and 83G>C mutation occurs in the other chromosome. The specific position of “()” for the occurrence of mutation is uncertain, and the possible range is indicated in parentheses. For example, c.(67_70)insG represents the insertion of a base G at a position from 67 to 70 bases. ➉ “ext” indicates elongation, p.*110Glnext*17 (or p.*110Qext*17), indicating that the stop codon variation of 110 is Gln, and the amino acid sequence is extended by 17 amino acids (including Gln).

6.2.5 Reference Sequence The reference sequence code is authoritative and unique included in the National Center for Biotechnology Information (NCBI). Wherein the prefix “NM_” is denoted as an mRNA sequence, “NP_” denotes a polypeptide sequence, and “NG_” denotes a genomic sequence. The genomic reference sequence should list the complete gene sequence, including the 5′ and 3′ untranslated regions (UTRs). When a segment of a coding DNA reference sequence is used to describe a mutation, the appropriate transcript should be selected and the starting transcription point of the transcript should be clear, such as selecting the most common transcript, or the largest known transcript, or having tissue. Specific editing of transcripts. When a reference sequence has multiple transcriptions, the most comprehensive version of the NCBI database is selected.

6.3

Naming Rules

Sequence variations can occur at the level of DNA, RNA, or protein, and we need to describe the specific rules of variation from these three levels.

6.3.1 Specific Rules for DNA Levels 6.3.1.1 The numbering of nucleotides in DNA (Table 6.1) relates to the precise localization of the mutated DNA and is critical in the description of the variation.

Table 6.1  Common nucleotide representation information table Symbol Meaning A A B C, G or T C C D A, G or T G G H A, C or T K G or T M A or C N A, C, G or T R A or G S G or C T T V A, C or G W A or T Y C or T Used in alignments only X A, C, G or T – None

Description Adenine Not-A (B follows A in alphabet) Cytosine Not-C (D follows C in alphabet) Guanine Not-G (H follows G in alphabet) Keto aMino aNy purine Strong interaction (3 H-bonds) Thymine Not-T/not-U (V follows U in alphabet) Weak interaction (2 H-bonds) pYrimidine Masked nucleotide Gap of indeterminate length

6.3.1.2 The Representation of the Amino Acid Sequence Follows the Following Rules  1. Genomic reference sequence: The nucleotide number of the genomic reference sequence is completely random, and the first base of the reference sequence stored in the database is programmed as l, and then pushed backward, without “+”, “−” and other prefixes. The sequence should cover all nucleotides of the sequence of interest (gene), starting with the 5′ promoter region of the gene (Fig. 6.2). 2. Coding DNA reference sequence: The code starts at 1, does not start at 0. The number 1 corresponds to the base A in the translation start codon ATG (T is 2, G is 3, and the translation sequence is pushed backward). The translation start codon ATG upstream (5′ end) base number is −l, −2, and is pushed forward. The base number downstream of the translation stop codon (3′ end) is *l, *2, which is pushed down in sequence. The intron number is numbered from the two sides to the middle of the adjacent exon number plus (upstream) or minus (downstream) intron relative to the position of the exon. For example, the l intron in Fig. 6.1 is located between the 1st exon (base number 1–12) and the 2nd exon (base number 13–88), and the intron number is 12+1, 13-1, 12+2, 13-2, push in the middle (Fig. 6.3). 3. Base substitution: The replacement of a single nucleotide is indicated by the symbol “>”. The presentation format is “prefix” “position_substituted” “reference_nucleotide” > “new_nucleotide”. “prefix” indicates the sequence type, such as g for the genomic sequence and c for the coding DNA. “position_substituted” is the position where the base substitution occurs. “reference_nucleotide” reference base. “>” indicates replacement. “new_nucleotide” is a newly mutated base. For example, c.85G>C describes the mutation of nucleotide 85 at the 85th posi-

Last Exon 3’ UTR

5’ UTR .catgcatgc agt.//.gaccATGGACG.//.CAG 1

271

Poly A-addition site

Transcription stop

Intron 2/ .../intron 4

Exon 2

3’ gene flanking

Intron 1

First exon

Splice acceptor site

Splice donor site

Splice donor site

Splice acceptor site

65

Transcription start

Transcription start (cap-site)

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301

gtgagt.//.tttcag

GCATT.//.TCG

313

413

gtgagt...........//..........tttcag 489

GCATG.//.CATGAcaatca.//.atgc atgcatgc.. 1631 1851 2000

1601

genomic reference sequence coding DNA reference sequence

–300

–31 –30

–1 1

12 12+1....

13–1 13

88

301–1 301

88+1

330

*320 *321

*1

*470

protein reference sequence

5

1

30

42

101

110

coding sequence

Fig. 6.2  Schematic diagram of nucleotide naming in the genome reference sequence Fig. 6.3  Schematic diagram of nucleotide naming in the coding sequence of coding DNA and non-coding DNA

Coding DNA reference –93

351

1

–45

–45 –44

*96

gtgag..//..ttctag

gtgag..//..tttcag 187+1 187+2

–44–1 –44–2

–45+1 –45+2 –45+3

–44

187+3

–44–3

188–1 188–2 188–3

*223

*97

gtgag..//..ctttag *96+1

*97–1 *97–2 *97–3

*96+2 *96+3

Noncoding DNA reference 1

49

667

50

280 281

gtgag..//..tttcag 49+1 49+2 49+3

50–1 50–2 50–3

540

gtgag..//..ttctag 280+1 280+2

tion of the coding DNA to C; and c.-14A>C indicates the 14th position before the 5′ end of the start codon ATG of the coding DNA. A>C variation occurs at the position of the nucleotide; likewise, c.89-2A>C indicates that A

280+3

281–1 281–2 281–3

541

gtgag..//..ctttag 540+1 540+2 540+3

541–1 541–2 541–3

to C mutation occurs in the intron region of nucleotides 88–89 of the coding DNA (variation point), two base positions upstream of the 89th nucleotide; c.*46T>A indicates that a substitution of T  >  A occurred at the

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base of the 3′ end of the translation stop codon. Two or more consecutive base substitutions are indicated by the symbol “delins”. For example, c.112_117delinsTG (or c.112_117delAGGTCAinsTG) indicates that the nucleotides 112–117 (AGGTCA) of the coding DNA were replaced by TG. 4. Nucleotide deletion: Adding the symbol “del” after the start and end of the deletion indicates a nucleotide deletion in the format “prefix” “position(s)_deleted” “del”, “position(s)_deleted” indicates the occurrence of the deleted nucleotide Location or range. For example, c.7_8del (or c.7_8delTG) indicates the deletion of the nucleotide TG at the 7th and 8th nucleotides of the coding DNA (sequence ACTTTGGCCC becomes ACTTTGCC); c.88_?_923+?del indicates the 88th nucleotide at the coding DNA A deletion occurs from an unknown position in the 5′ end intron to an unknown position in the 3′ end of the nucleotide at position 923; it is worth noting that the alignment of the nucleotide sequence is from an exon (or intron) the largest alignment between the two ends, the sequence is missing, e.g., ACTTTGTGCC becomes ACTTGCC, it must be described as c.5_7del (c.5_7delTGT, instead of c.4_6delTTG); the sequence TCACTGTCTGCGGTAATC becomes TCACTG CGGTAATC C.7_10del (c.7_10delTCTG) instead of c.4_7del (c.4_7delCTGT); AAAGAAGAGGAG becomes AAAG GAG is described as c.5_9del (or c.5_9delAAGAG) instead of c.3_7delAGAAG; intron sequence is deleted, e.g., ctttagGCATG It becomes cttagGCATG described as c.301-3delT instead of c.301-5delT. 5. Nucleotide insertion: The symbol “ins” may indi cate the insertion of a nucleotide in the format “prefix” “positions_flanking” “ins” “inserted_sequence”, and “positions_flanking” indicates the position of two nucleotides flanking the insertion site. For example, c.56_57insG indicates insertion of G between nucleotides 56 and 57 of the coding DNA; c.123+54_123+55 ins AB012345.2:g.76_420 indicates intron c.123+54  in the coding DNA A 345 nucleotide sequence was inserted into 123+55, and the sequence was nucleotide sequence 76–420 of the gDNA sequence in the reference sequence AB012345.2 in GenBank. 6. Nucleotide repeat: The symbol “dup” may indicate repeated insertion of nucleotides in the format “prefix” “position(s)_duplicated” “dup”, and “position(s)_duplicated” indicates the position of a repeating base or base cluster. For example, the sequence ACTCTGTGTCC becomes ACTCTTGTGTCC, described as g.5dupT (or g.5dup, but not g.5_6insT), indicating that a repeat or insertion occurs at nucleotide position 5 of the genomic reference sequence. AGACTTTGTGCC becomes AGACTTTTGTGCC, described as g.7dupT (or g.7dup, instead of g.5dupT or g.7_8insT), indicating a repeat

Y. Tong

or insertion of nucleotide 7 at the genomic reference sequence. ACTTTGTGCC becomes ACTTTGTGTGCC, described as g.7_8dup (or g.7_8dupTG, instead of g.5_6dup, or g.8_9insTG), indicating that TG repeats occur in the TG tandem sequence. ACTTTGTGCC becomes ACTTTGTGTGTGCC, described as g.7_8[4] (or g.5_6[4], or g.5TG[4], instead of g.7_10dup), indicating an additional second TG in the TG repeat sequence variation region. C.123+74TG[3_6] (or c.123+74_123+75[3_6]) indicates that the TG dinucleotide repeat occurs at position 74 of the intron 74 after encoding the DNA for 3 to 6 times. 7. Chromosomal translocation: The expression of trans location is seen in cytogenetics, such as t(X; 4) (p21.2; q35), i.e., the equilibrium translocation occurs between x chromosome and chromosome 4, and the short crack points are Xp21.2 and 4q35. At the molecular level, a specific sequence is added to make the breakpoint more precise. These translocation breakpoints should be submitted to the nucleic acid sequence database with their serial number and version number. The database includes the Genbank, European Molecular Biology Laboratory (EMBL), and DNA Data Bank of Japan (DDJB). For example, t(X; 4)(p21.2; q35) (c.857+101_857+102) indicates that p21.2 of the X chromosome and translocation of q35 of chromosome 4 occur. The cleavage site occurs between the 101 intron and the 102 intron after the 857 nucleotide encoding the DNA.

6.3.2 Detailed Rules for RNA Levels The variation in RNA levels is the same as the variation in protein levels, which is due to changes in DNA levels. Therefore, RNA level variation or protein level variation may be the result of experimental analysis or may be derived by deriving from DNA level variation. If the variation is derived from the experimental analysis, the description is basically the same as the DNA variation description. Use the symbol “r” and lowercase bases to represent the RNA sequence (Table 6.2). The format is “prefix” “position_substituted” “reference_nucleotide”  >  “new_nucleotide”, and “prefix” refers to the sequence type. “r” represents the RNA sequence, and “position_substituted” is the position at which base substitution occurs. “reference_nucleotide” refers to a reference base. “>” indicates replacement. “new_nucleotide” refers to a newly mutated base. For example, r.78u>a represents the 78th nucleotide U becomes A.  However, DNA variation can affect the transcription process, resulting in two or more transcripts. In this case, you can separate each transcript with “,” in “[]”. For example, r.[=, 73_88del], indicating that c.76A>C results in two RNA molecules, one is the normal transcript (r.=) and

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Table 6.2  Table of nucleotide information in RNA Symbol a b c d g h k m n r s u v w y

Meaning A c, g or u C a, g or u G a, c or u g or u a or c a, c, g or u a or g g or c U a, c or g a or u c or u

Description Adenosine Not-a (b follows a in alphabet) Cytidine Not-c (d follows c in alphabet) Guanosine Not-g (h follows g in alphabet) Keto Amino Any Purine Strong interaction (3 H-bonds) Uridine Not-u (v follows u in alphabet) Weak interaction (2 H-bonds) Pyrimidine

the other is the nucleotide deletion from 73 to 88, r.[76a>c, 73_88del], indicating that c.76A>C in the coding DNA sequence results in two RNA molecules, one carrying a 76a>c variant and one being a nucleotide deletion from 73 to 88.,r.[=,88_89ins88+l_88+l0;88+2u>c], indicating that the mutation at the coding DNA level c.88+2T>C results in two RNA molecules, one being a normal transcript (r.=) and the other is a nucleotide insertion from the intron 88+l to 88+10 with r.88+2t>c mutation.

6.3.3 Detailed Rules of Protein Levels Sequence variations in protein levels are described by the symbol “p” and amino acids in a three-letter abbreviation (initial capitalization) (Table 6.3), each amino acid encoded by three nucleotides (Table 6.4). Because sequence variation at the protein level may be the result of experimental analysis or may be derived from DNA level variation, this should be pointed out in the description at first. If it is an experimentally observed protein change, its protein sequence variation is directly described, rather than trying to mix it with DNA level variation.

6.3.3.1 Amino Acid Coding The protein sequence should be the original translation product (including the signal peptide sequence) rather than the posttranslationally modified mature protein sequence. The translation initiation codon methionine (metheine) is coded as l, written as Met l (or Ml), not as metl or Metl). The amino acids upstream of the translation initiation point, such as nucleotides, are numbered −1, −2, −3⋯⋯, such as..., Gln-2, Thr-1; the amino acid downstream of the translation termination point is *1,* 2, *3⋯⋯, such as Gln*1, Ser*2, ...; mutation causes intron translation, numbered with nucleotides, such as Val4+1, Ser4+2, ..., Phe5-2, and Gln5-1.

6.3.3.2 Silent Changes Silent changes do not cause changes in protein levels. The expression p. (Leu54Leu) or p. (L54L) is wrong and should contain a representation of the level of DNA, as expressed as c.162C>G (p.(Leu54=), if expressed as p. (Leu54=), then indicates that there is no expected effect on protein levels. 6.3.3.3 Substitutions, Missense Changes Substitutions, missense changes, indicating that an amino acid is replaced by another amino acid, represented by “prefix,” “amino_acid,” “position,” “new_amino_acid.” “prefix” refers to the sequence type. p represents a protein sequence. Such as p.Arg54Ser. “amino_acid” is a reference amino acid. “position” refers to the location at which an amino acid substitution occurs. “new_amino_acid” refers to a newly mutated amino acid. As for p.Trp26Cys, the “>” symbol used in DNA or RNA sequences is not used in the expression. 6.3.3.4 Amino Acid Deletion Like the nucleotide deletion at the DNA level, it is represented by the symbol “del” in the format of “prefix,” “amino_acid(s) + position(s)_deleted” “del”. For example, p.Lys2del indicates that the second amino acid Lys(K) of the protein sequence MKMGHQ is deleted (the sequence becomes MMGHQ), while p.Cys28_Met30del indicates the deletion of the third amino acid at positions 28 to 30; if the deletion is accompanied by the insertion, the symbol “delins” is used. Indicates that the format is “prefix,” “amino_acid(s)  +  position(s)_deleted,” “delins,” “inserted_ sequence.” For example, p.Cys28delinsTrpVal indicates that the 28th amino acid Cys is deleted and two amino acids of TrpVal are inserted at this position. p.Cys28_Lys29delinsTrp indicates that two amino acids Cys28 and Ly29 at positions 28 to 29 are deleted and an amino acid Trp is inserted. p. (Pro578_Lys579delinsLeuTer) indicates that the two amino acids Pro578 and Lys579 at positions 578–579 are deleted and two amino acid Leu and Ter deletions are inserted and an amino acid Trp is inserted. 6.3.3.5 Frameshift Mutation The frameshift mutation causes codon shift due to mutation at the DNA level, and is represented by the first amino acid after the addition of the symbol “fs” or “fsX#”, the latter description being more specific, indicating the length of the stop codon, the format is “prefix” “amino_acid” position “new_amino_acid” “fs” “Ter” “position_termination_ site.” For example, p.Arg97ProfsTer23 (also abbreviated as p.Arg97fs, indicating that the amino acid Arg at position 97 as the first amino acid affected by the frameshift mutation becomes GIy, generating a new reading frame until the termination password is reached after 15 amino acids. Sub-(X16). It can be seen that the description does not specify the specific amino acid change (deletion, insertion) from

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Table 6.3  Amino acids common information One letter code A B C

Three letter code Amino acid Ala Alanine Asx Aspartic acid or asparagine Cys Cysteine

D E F

Asp Glu Phe

Aspartic acid Glutamic acid Phenylalanine

GAC, GAT GAA, GAG TTC, TTT

G

Gly

Glycine

H

His

Histidine

GGA, GGC, GGG, GGT CAC, CAT

Possible codons GCA, GCC, GCG, GCT AAC, AAT, GAC, GAT

Systemic name 2-Aminopropanoic acid

Formula CH3-CH(NH2)-COOH

TGC, TGT

2-Amino-3mercaptopropanoic acid 2-Aminobutanedioic acid 2-Aminopentanedioic acid 2-Amino-3phenylpropanoic acid Aminoethanoic acid

HS-CH2-CH(NH2)-COOH HOOC-CH2-CH(NH2)-COOH HOOC-[CH2]2-CH(NH2)-COOH C6H5-CH2-CH(NH2)-COOH CH2(NH2)-COOH

2-Amino-3-(1H-imidazol4-yl)-propanoic acid

CH2-CH(NH2)-COOH N

HN

I

Ile

Isoleucine

ATA, ATC, ATT

K L

Lys Leu

Lysine Leucine

M

Met

Methionine

N

Asn

Asparagine

AAA, AAG CTA, CTC, CTG, CTT, TTA, TTG ATG(translation initiation) AAC, AAT

P

Pro

Proline

CCA, CCC, CCG, CCT

2-Amino-3methylpentanoic acid 2,6-Diaminohexanoic acid 2-Amino-4methylpentanoic acid 2-Amino-4-(methylthio) butanoic acid 2-Amino-3carbamoylpropanoic acid Pyrrolidine-2-carboxylic acid

C2H5-CH(CH3)-CH(NH2)-COOH H2N-[CH2]4-CH(NH2)-COOH (CH3)2CH-CH2-CH(NH2)-COOH CH3-S-[CH2]2-CH(NH2)-COOH H2N-CO-CH2-CH(NH2)-COOH

N H

Q

Gln

Glutamine

CAA, CAG

R

Arg

Arginine

S

Ser

Serine

T

Thr

Threonine

AGA, AGG, CGA, CGC, CGG, CGT AGC, AGT, TCA, TCC, TCG, TCT ACA, ACC, ACG, ACT

U V

Sec Val

Selenocysteine Valine

TGA GTA, GTC, GTG, GTT

W

Trp

Tryptophan

TGG

2-Amino-4carbamoylbutanoic acid 2-Amino-5guanidinopentanoic acid 2-Amino-3hydroxypropanoic acid 2-Amino-3hydroxybutanoic acid 2-Amino-3-methylbutanoic acid 2-Amino-3-(lH-indol-3yl)-propanoic acid

COOH

H2N-CO-[CH2]2-­CH(NH2)-COOH H2N-C(=NH)-NH-[CH2]3-­CH(NH2)COOH HO-CH2-CH(NH2)-COOH CH3-CH(OH)-CH(NH2)-COOH H2N-CH(COOH)--CH2-­SeH (CH3)2CH-CH(NH2)-COOH CH2-CH(NH2)-COOH

N H

X

Xaa

Y

Tyr

Z *

Glx *(Ter)

Unknown or other Tyrosine

Glutamic acid Termination

Used in alignments only – – Gap of indeterminate length

NNN TAC, TAT

TAA, TAG, TGA(translation termination)

2-Amino-3-(4hydroxyphenyl)-propanoic acid

HGVS addition (V2.0)

HO

CH2-CH(NH2)-COOH

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Table 6.4 Amino acid codon table

Nucleotide position in codon first

second T

C

A

third G

TTT-Phe

TCT-Ser

TAT-Tyr

TGT-Cys

T

TTC-Phe

TCC-Ser

TAC-Tyr

TGC-Cys

C

TTA-Leu

TCA-Ser

TAA-Ter

TGA-Ter

A

TTG-Leu

TCG-Ser

TAG-Ter

TGG-Trp

G

CTT-Leu

CCT-Pro

CAT-His

CGT-Arg

T

CTC-Leu

CCC-Pro

CAC-His

CGC-Arg

C

CTA-Leu

CCA-Pro

CAA-Gln

CGA-Arg

A

CTG-Leu

CCG-Pro

CAG-Gln

CGG-Arg

G

ATT-Ile

ACT-Thr

AAT-Asn

AGT-Ser

T

ATC-Ile

ACC-Thr

AAC -Asn

AGC-Ser

C

ATA-Ile

ACA-Thr

AAA-Lys

AGA-Arg

A

ATG-Met

ACG-Thr

AAG-Lys

AGG-Arg

G

GTT-Val

GCT-Ala

GAT-Asp

GGT-Gly

T

GTC-Val

GCC-Ala

GAC-Asp

GGC-Gly

C

GTA-Val

GCA-Ala

GAA-Glu

GGA-Gly

A

GTG-Val

GCG-Ala

GAG-Glu

GGG-Gly

G

T

C

A

G

the frameshift mutation to the stop codon. For example, a sequence, from the 30th amino acid Leu to the 42th amino acid Cys, results in the frameshift mutation, described as p.Leu30fs or p.Leu30SerfsX3, is not described as p.Leu30_ Cys42delinsSerfsX3. For the above mentioned, do not try to mix protein sequence variation and DNA level variation, for example, CTCAGAACGATATAG (Leu-Arg-Thr-Ile-X) becomes CTAGAACGATATAG (Leu-Glu-Arg-X), and the DNA level variation should be described as c.3delC. If the DNA level is combined, the protein level variation should occur in the first amino acid. It is easy to misreport as p.Leu1LeufsX4, but regardless of DNA level variation, in fact, the amino acid change is from the second amino acid, described as p.Arg2GlufsX3.

6.3.3.6 Amino Acid Insertion, Repeat The description rules for amino acid repeats and insertions are basically the same as those for DNA-level nucleotides. The format of amino acid repeats is “prefix” “amino_

acid(s)+position_repeat_unit” “[""copy_number""]”, where “amino_acid(s)+position_repeat_unit” represents the first repeated copy of the amino acid position or range, e.g., p.Arg65_Ser67 [12]. The format of the amino acid repeat and insertion description is p.Lys2_Met3insGlnSerLys, p.Cys28delinsTrpVal or p.Gly4_Gln6dup.

6.3.4 G  ene Pharmacology Genotype Terminology The most widely used nomenclature describes genetic pharmacological genotypes that differ from other genetic tests, with the metabolic-related gene cytochrome p450 family as an example, the Human Cytochrome P450 (CYP) Allele Nomenclature Committee (http://www.Cypalleles. ki.se) is recommended to be named with “*”, as shown in Fig. 6.4. In this system, the most common allele is designated as “* 1”.

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Y. Tong 1 Cytochrome P450

2 3 4

5

2 D 6

*4

1

Superfamily

2

Family

3

Subfamily Isoenzyme

4

Allele variant

5

Fig. 6.4  Naming convention for cytochrome P450 CYP2D6 allele

6.3.5 Other For more complex mutations, refer to the HGVS recommended naming conventions to address the naming of other complex mutations.

6.4

 pplication of Gene Mutation A in Disease Diagnosis

6.4.1 Genetic Variation and Genetic Diagnosis Genetic variation refers to the changes in the genetic material of an organism that can be passed on to the next generation. It is this variation that causes organisms to embody genetic diversity at different levels. Genetic diversity is the material basis for the survival and development of human society. The study of genetic diversity is important for both the conservation of biodiversity and the sustainable use of biological resources, as well as the food supply of the future world [4]. There are many ways of genetic variation in the genome, including microscopically visible chromosome inversions to single nucleotide mutations. With the development of genomics research, genetic variation information has become more comprehensive, including single nucleotide polymorphism (SNP), insert and deletion of small fragments (Indel), structural variation (SV), copy number variation (CNV), and transposon variation. The development of molecular biology technology has laid a foundation for people to understand diseases from the level of genetic material base nucleic acid molecules. The role of genetic factors in disease development has been paid more and more attention. With the rapid development of molecular biology and molecular genetics, people gradually understand the mechanism of disease occurrence at the

genetic level and promote the new development of diagnosis and prevention technology. The analysis of endogenous gene variation and the study of exogenous gene invasion has led to the development of technical methods for diagnosing diseases at the molecular level. Through continuous development and improvement, this method has played an important role in the diagnosis of infectious diseases, genetic diseases, and chronic diseases as well as tumors. Genetic diagnosis is the application of molecular biology techniques to the diagnosis of human conditions and diseases at the DNA or RNA level by detecting the presence of a causative gene (endogenous or exogenous), genetic defects, or abnormal expression. The essence of its theoretical basis is to apply molecular biology and molecular genetics theory and technology to examine and analyze whether a certain or a certain group of genes in a subject is normal or not, so as to diagnose the subject. After decades of development and improvement, the gene diagnosis technology based on the detection of nucleic acid molecules has become a systematic molecular level detection technology platform more and more widely used in biomedical researches and clinical fields. Among them, high throughput sequencing technology, also known as next-­ generation sequencing (NGS), can obtain a large amount of genetic variation information about disease populations and the general population, and explain the etiology and development of human diseases from the overall genetic level. Exploring effective prevention and treatment measures is of great significance, and it also brings clinical medicine to the era of individual genome sequencing and personalized medicine. Genetic testing has increasingly been used in clinical decision-making, such as preventive mastectomy, cardiac defibrillator implantation, oncology, and prenatal diagnosis. Misinterpretation of the meaning of sequence variation can have a serious impact on patients. The rational and effective use of large genetic information will greatly promote the development of human health. However, incorrect determination of the pathogenicity of sequence variation can also have serious consequences for patients, leading to misjudgment in treatment, prognosis, or reproductive counseling. Given the potentially huge impact of sequence variation detection techniques on medicine, it is necessary to distinguish true pathogenicity-related variability from numerous non-pathogenically relevant potential functional variants from the variability of the human genome [1].

6.4.2 Application of Genetic Variation Detection in Disease Diagnosis 6.4.2.1 Molecular Diagnosis of Mendelian Genetic Disease Mendelian genetic disease is usually a relatively rare disease with a strong genotype and phenotype correlation, and a high rate of disability. Most Mendelian genetic diseases

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are identified and diagnosed only by clinical features, and no relevant candidate genes and mutant genes have been found. Linkage analysis and candidate gene screening are traditional methods for studying the pathogenesis of Mendelian genetic disease. They are usually time-consuming and laborintensive, have low success rates, and are incapable of diagnosis of incomplete penetrance, new mutations, sporadic, and rare diseases. In addition, traditional methods have limited research capacity for Mendelian genetic diseases with genetic heterogeneity (cause mutations occur in different genes) and phenotypic heterogeneity (difficult clinical classification and phenotypic differentiation) [4].

6.4.2.2 Prenatal Diagnosis and Prenatal and Postnatal Care Prenatal diagnosis and screening can effectively reduce birth defects [5]. Traditional prenatal diagnosis is mostly invasive. Early detection of fetal chromosomal abnormalities and other genetic abnormalities by separation of maternal blood nucleated cells. Recently, studies have found that not only fetal nucleated cells, but also fetal free DNA (cffDNA) exist in peripheral blood of pregnant women, thus promoting the emergence of noninvasive prenatal tests (NIPT) screening technology characterized by cffDNA detection. In addition, sequence variation detection techniques are also used in assisted reproductive medicine. The meiosis, chromosome recombination and aneuploidy status of human single sperm cells were analyzed by multiple annealing and looping based amplification cycles (MALBAC)-based sequencing techniques. For the techniques of routine prenatal diagnosis, the method of cell culture and karyotyping of villus tissue has high requirements on specimens and high failure rate; the detection range of probes by fluorescence in situ hybridization is limited. The NGS technology has the characteristics of high throughput, wide detection range, fast detection speed, noninvasiveness, etc. It is widely used in prenatal diagnosis. However, there are still many problems, such as the difficulty in successfully applying twins, multiple tires, expensive testing costs, and difficult to interpret the results, which limits the application of high throughput sequencing. At present, the prenatal diagnosis of NGS is basically limited to diseases with abnormal chromosome multiples, such as 21-trisomy syndrome. Only a small part of the peripheral blood-free DNA of pregnant women comes from fetal DNA, and the content is very low. Therefore, high sensitivity and specific detection are needed to obtain fetal whole genome data by increasing the depth of sequencing [6].

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wide association study (GWAS) is one of the important components of human complex disease research, which is to detect the genetic association between genome-wide genetic variation and observed traits at the population level. The ­traditional GWAS has achieved a lot of results by relying on array technology and found some common variations of genetic susceptibility. However, the variation studied by this method usually has a slight effect on the phenotype. The heterogeneity of the results of different studies on some traits is weak, and the function of the significantly associated genetic variation site is difficult to explain, and clinical diagnosis and risk stratification are almost impossible. Those limits its clinical application. NGS technology provides high throughput sequencing data, making it possible to study complex diseases for rare mutations, low-frequency mutations, epigenetic abnormalities, etc.

6.4.2.4 Diagnosis and Treatment of Tumors The tumor genome is significantly different from the normal cell genome. The tumor genome is usually highly heterogeneous and can contain multiple clones of the genomic type. Comparison of tissue and cancer tissue is critical to identifying somatic mutations. NGS technology has achieved genome-wide, exome, and transcriptome sequencing of tumor samples, which greatly promoted the study of acquired mutations in tumor cells. Tumor sequencing often has a large amount of data and a wide variety of mutations. A noninvasive method called liquid biopsy has emerged in recent years. The method uses blood circulating DNA instead of tissue biopsy DNA for molecular detection of tumors. Studies have found that circulating system-free tumor DNA is present in serum/plasma of cancer patients and fluctuates with changes in solid tumors and chemotherapy responses, suggesting the feasibility of monitoring cancer status by circulating DNA. Detection of circulating tumor DNA can be used to assist in imaging examinations, with low cost and low side effects. Sequence variation detection can be used to find therapeutic targets, drug resistance genes, molecular markers, etc. However, there are still many problems in this method, for example, the amount of circulating tumor DNA will change in different disease stages, different types, different metastases and different treatments, and a small amount of circulating tumor DNA cannot guarantee the sensitivity of detection. The types of mutations detected by various types of sequence variation detection techniques are still limited. Sequence variation detection also provides an effective means for the research and detection of drug targets, providing a possibility for individualized medical treatment of patients. For 6.4.2.3 Molecular Genetic Testing of Complex example, drug-targeted therapy for patients with non-small Diseases cell lung cancer, for a subset of NSCLC patients with epiThe heritability of complex diseases is low, and is affected by dermal growth factor positive (about 15%), EGFR-targeted genetic susceptibility and environmental factors. Genome-­ tyrosine kinase inhibitors (TKIs) can effectively improve

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EGFR progression-­free survival in patients with mutations; and crizotinib has anticancer effects in patients with positive ALK gene rearrangement.

6.4.2.5 Detection of Pathogenic Microorganisms Infectious diseases are caused by the invasion of exogenous pathogens into the body, and pathogen diagnosis is generally performed by pathogen culture or serological methods. These traditional methods have obvious deficiencies in the speed, specificity, sensitivity, and correctness. The early diagnosis of the detection and the methods of identification are limited by many factors. For example, it takes a certain period of time for the pathogen to produce antibodies after the infection. It is difficult to make a timely diagnosis by serological methods, and serological tests can only determine whether or not the pathogen has been contacted, and it is not possible to determine whether there is an existing infection. In addition, the ability to identify mixed infections and unknown pathogenic microorganisms is limited. In recent years, a large number of analytical work have been carried out on the gene sequences of various pathogens, and it has been possible to design specific probes for pathogen-­specific nucleotide sequences for molecular hybridization, or to amplify conserved sequences of pathogen genes by PCR, and to grasp pathogenic microorganisms as a whole. Genomic sequencing of clinical isolates allows early, clear pathogen diagnosis of most infectious diseases, detection of carriers and potential infections, and classification and typing of pathogens. In addition, sequence compilation testing can also be used for antibiotic resistance monitoring and infection control, as well as for drug development and clinical trials. At present, sequence mutation detection technology has also been applied to the detection of viral diseases, bacterial diseases, and parasitic diseases, such as human immunodeficiency virus (HIV), hepatitis C virus (HCV), and influenza virus. In addition, the new sequence mutation detection technology is more efficient and convenient, and provides a more effective means for detecting virus mutation. In addition, genetic diagnosis can be used for the detection of bacteria such as Neisseria gonorrhoeae, Helicobacter pylori, and Neisseria meningitidis, and can also be used for the diagnosis of various parasitic diseases such as spirochetes, malaria parasites, and toxoplasma.

6.5

 hat Is Involved Before the Clinical W Report

The test results are issued in the form of test report sheets. A paper version test report is required. The conditional test laboratory can issue reports in electronic form and establish a network query system. The check doctors can log into the website to check the test results.

6.5.1 Content Covered by the Clinical Report Molecular testing of genetic variation is a laboratory test based on genetic counseling. The laboratory test results must also be reasonably explained by the clinical consultant in combination with the patient’s clinical symptoms. Laboratory tests for genetic diseases should also be consistent with existing or established laboratory test reports.

6.5.1.1 The Test Report Should Include the Following 1. The name of the medical institution (and department), the name of the project, the unique number of the sample, the contact information of the hospital and department, and the inspection unit. 2. Identification information of the test sample includes the patient’s name, gender, date of birth of the patient (prenatal diagnosis should also list current age and gestational age), specimen collection time, sample reception time, experimental code, specimen type, doctor for examination, the time for the diagnosis of the disease, the report of the test result, etc. 3. Sample processing process, detection method, detec tion process, test results, and related charts; if necessary, include the normal range of detection, positive judgment value (Cut-off). 4. Clearly describe and explain the detected results, clinical significance and recommendations, detection limitations, references, and limitations of genetic testing for the purpose of genetic testing (such as limitations of experimental techniques and clinical effectiveness, and nonparentality) should be clearly explained and described. 5. In the results report of the estimated risk rate, the information and data used to calculate the risk rate should be clearly described. 6. The test results must be signed by the test personnel, the result interpreter, and the report reviewer. For different detection methods, corresponding test results judgment and diagnostic criteria should be established in the laboratory. In particular, the selection of test methods, the selection of test markers (markers for linkage analysis), detection systems, positive markers (such as the length of PCR products), etc., should be evaluated for clinical validity, sensitivity, and specificity. The report of molecular genetic testing should include the cause of the experimental/disease test, the test method used, the target site of the test, the genotype of the individual, the detected mutation site, the interpretation of the results (clinical significance), and the need for detailed information on follow-up recommendations, genetic counseling recommendations, etc. If the detection method is a linkage analysis method, the family report, and genotype information should

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be included in the test report. It should be noted that any genetic testing report should take care to protect the privacy of patients and other family members. Therefore, it is recommended that the testing laboratory provides experimental tests for different versions and different information for clinicians and probands. In the test report, the testing laboratory can provide a professional experimental report interpretation for the patient or clinician according to different testing methods to help the clinician make a correct diagnosis. However, it should be noted that the interpretation of the test report is not a substitute for the clinician for diagnosis. All test results should be interpreted independently by more than two individuals, one of whom must be the laboratory director or laboratory manager or other qualified people. All results with inconsistencies or data inconsistencies must be judged by qualified personnel with additional supplementary experimental analysis. The test results can be analyzed and explained by referring to the members of the family using known genotypes as a control. Since the reliability of the quantitative analysis results is significantly less than the qualitative analysis results, an internal control must be applied to ensure the accuracy of the test. For PCR analysis, the possibility of different amplification products needs to be considered.

6.5.1.2 DNA Sequencing Reports and Explanations Should Include For clinical reports of sequencing results, the following information is important for understanding and interpreting test results. When designing the final report template, it should include as much as possible: 1. The genes and/or chromosomal regions analyzed by sequencing should be clearly indicated by the HGNC gene naming rules. Specific regions of the analyzed gene and/or chromosome should be noted, such as coding regions, exons, cleavage sites, and the like. 2. The reference sequence used (RefSeq access number) should be noted. 3. For the sequence variations and exact sites found by sequencing, the HGVS nomenclature rules should be specified. As indicated in the report, the position and change of the reference sequence of the single nucleotide in the Genebank, and the corresponding standard position of the protein change. 4. The relationship between the predicted base variation and the known gene structure and other data, and the influence of base variation on the gene. Changes in amino acid coding due to nucleic acid variation, and possible changes in protein function, should also be based on HGVS rules and detailed references. The missense variation of the base needs to indicate whether it represents a mutation, a polymorphism, or a rare muta-

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tion. For each genetic disease, the laboratory should first use the corresponding database as a reference. If the detected mutation is a new mutation, the nature and significance of the mutation may not be clear at the moment and should be indicated in the report. 5. The clinical relevance of the test results should be noted. Because information about the pathogenicity of the mutation may change over time, it should also be noted if the detected variation may have a potential clinical effect. 6. The technical limitations of the test method and the limitations of the interpretation of the test results should also be clearly stated. For example, when the test result is negative, the reason for the possibility of the negative result should be explained and described. For example, because sequencing is limited to the coding region of the gene, the mutation may be in the uncovered intron or promoter region, and the pathogenic mutation in the other parts of the genome cannot be excluded, the sensitivity of the detection is 99% fetal genetic information from free DNA in maternal plasma Monosomy X (Turner syndrome) 92.9% to detect whether the fetus has three major chromosomal Triploidy/vanishing twin detection Detectable diseases. Male >99% Clinically, this test is mainly targeted at high-precision Female >99% 22q11.2 deletion syndrome (DiGeorge 95.7% screening, deep screening, screening close to diagnosis, syndrome) primary screening of high-risk groups, etc. Although the 1p36 deletion syndrome/Angelman syndrome/ 93.8 to >99% names are different and there are certain limitations in Cri-du-chat syndrome/Prader-Willi syndrome clinical application, experts from various countries. The level of NIPT’s high sensitivity and high specificity has age, because the weight, race, serum markers, smoking, and been fully recognized, and it is hoped that the test will be chromosomes of pregnant women Karyotype is also a factor more suitable for the actual clinical situation in the counaffecting the fetal ratio, and thus directly or indirectly affects try. Because NIPT is the target disease for precise prenathe sensitivity and specificity of NIPT detection. The half-­ tal screening techniques, it is currently difficult to replace life of the cell-free fetal DNA fragment is only 16.3 min, and existing prenatal screening diagnostic techniques. ACMG no free fetal DNA fragments of this pregnancy can be found (American Society of Medical Genetics and Genomics) 2 h after the mother’s production. The detection of cff DNA updated its consensus on noninvasive prenatal screening in the pregnancy is not affected by the last pregnancy. for fetal aneuploidy in July 2016 (Genet Med. 2016 Jul In addition, because the primary source of fetal-free DNA 28. https://doi.org/10.1038/gim.2016.97.) Replace the verfragments in the NIPT test is the placenta; in theory, this test sion released in 2013. ACMG’s technical positioning based should be similar to the villus sampling in prenatal diagno- on fetal aneuploid noninvasive prenatal screening does not sis, so karyotype false positives similar to villus sampling use the generic term NIPT (T is the testing abbreviation, ie may also occur (False positive) report, or the result of a false detection), but directly uses NIPS (S means screening, ie negative report. screening). For the first time, a professional association has issued a statement recommending to all pregnant women that the target is aneuploidy (Down’s syndrome, Edward’s 6.6.3.2 Noninvasive Prenatal Screening Method and Test Project for Clinical Molecular syndrome, Padua’s syndrome) for traditional screening Diagnosis [21-trisomy, respectively), 18-trisomy, 13-trisomy, NIPS is The development of clinical applications is inseparable from the most sensitive screening method. the support of industry experts, including the American College of Obstetricians and Gynecologists (ACOG), the For Example National Association of Genetic Counselors (NSGC), the Sample International Association for Prenatal Diagnosis (ISPD), The American College of Obstetricians and Gynecologists and international maternity. Many professional organiza- (ACOG), the National Association of Genetic Counselors tions, including the International Ultrasound Association (NSGC), the International Association for Prenatal Diagnosis (ISUOG), provide guidance on the clinical application of (ISPD), the International Association of Obstetrics and NIPT. The issuance of these statements and guidance reflects Gynecology Ultrasound (ISUOG), and our pre-diagnostic the importance of the test in the international medical field expert group The agency issues guidance on the clinical and the positive role and good results of NIPT in prenatal application of NIPT. These statements and guidance indicate testing. The industry guide helps medical workers under- that the source of noninvasive DNA prenatal testing techstand NIPT at a deeper level and know how to apply it in the nology is EDTA anticoagulation about 5–10 ml for pregnant field of prenatal testing, making NIPT a healthier and more women aged 12–22+6 weeks. Hemolysis should be avoided standardized direction. when collecting maternal blood, stored at 4  °C, separated within 4 h. Plasma was stored at −20 °C. Storage at −20 °C 6.6.3.3 High Throughput Sequencing should not exceed 1 week, and −80 °C for long-term storage Flux sequencing technology, also known as “next generation” (Table 6.11). At present, maternal blood fetal-free DNA has sequencing technology, is a revolutionary change to traditional been used for the detection of fetal sex, Rh blood type, and sequencing. It is epoch making for sequencing hundreds of fetal chromosome aneuploidy.

6  Report and Consultation Table 6.11  The samples requirements for NIPT Sampling type Pregnant woman peripheral blood

Sampling requirements 5–12 ml of peripheral blood of single-pregnant pregnant women at 12–22+6 weeks of gestation, stored in EDTA-treated blood collection tubes. Hemolysis should be avoided during maternal blood collection, stored at 4 °C, plasma separated within 4 h, and stored at −20 °C.

Data Analysis Abnormal fetal chromosome numbers cause a small change in free DNA content in maternal plasma. Deep sequencing by a new generation of DNA sequencing technology combined with bioinformatics analysis can detect this change, which is the theoretical basis for noninvasive DNA prenatal testing. Taking the most common Down syndrome (T21) in birth defects as an example, the feasibility of cffDNA for prenatal testing of fetal chromosomal diseases was analyzed by mathematical model. Normal 21 and normal mothers have 2 chromosomes 21, and those with Down syndrome have 3 chromosomes. It is assumed that the fetal free DNA content in the peripheral blood of the mother is 20%, and the mother’s own free DNA accounts for 80%. For convenience of explanation, it is assumed that there are 10 copies of DNA. The free DNA of chromosome 21 in the peripheral plasma of pregnant women with normal fetus is 10, 2 from the fetus and 8 from the mother. For pregnant women with Down’s syndrome, it is not difficult to calculate 11 copies of the free DNA of chromosome 21 in peripheral blood, 3 from the fetus, and 8 from the mother. The proportion of free DNA on chromosome 21 in maternal peri-week plasma with Down syndrome and normal fetus is 11:10. Similarly, for a case where the fetal-free DNA content is 5%, the ratio of free DNA of chromosome 21 in the maternal peri-week plasma of Down’s syndrome fetus and normal fetus is 10.25:10. Therefore, there is no need to isolate fetal-derived free DNA, and the total content of free DNA of chromosome 21 in the peripheral plasma of pregnant women with Down’s syndrome is generally increased slightly. In theory, by distinguishing this small difference, we can achieve prenatal detection of fetal chromosomal diseases using cffDNA. After the high throughput sequencing is completed, the data is quality-evaluated and effectively filtered, and the sequencing data is returned to a single sample according to the label, and the sequencing result of each sample independently generates a corresponding data file (.bin file), and Align to known human genome sequences. Use the universal function to compare and filter the unique matching Reads number of each sample, that is, the unique reads number, and perform multistep data correction on the data, such as GC correction and RUN correction to achieve data normalization, and calculate each sample. The number of unique reads

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per chromosome is %chrN of the percentage of all autosomal unique reads in the sample, i.e., the Reads ratio value. The Z value of each chromosome is calculated again, and the detection result of the sample is judged based on the Z value of each sample. By comparing the large sample negative pregnant plasma samples with the positive pregnant plasma samples, the percentage of unique reads of large samples of negative samples was analyzed by the Kolmogorov-Smirnov test and shapiro test, and the results showed that chromosomes 21, 18, and 13. The unique reads percentage satisfies the standard normal distribution (as shown in Fig. 6.9), so the Z test can be used to verify whether there is a significant difference between the positive and negative samples. Result Report The clinical application of NIPT should be targeted at “prenatal diagnosis level” and “target disease pointing to precise” prenatal screening techniques. Although the technique has been successfully validated for 21-trisomy, 18-trisomy, and 13-trisomy prenatal screening, it has been effectively validated, but 21-trisomy, 18-trisomy, 13-three False-negative cases and detection failures (2–5%) of body and sex chromosome abnormalities still exist, and the coincidence rate of noninvasive diagnosis of sex chromosome abnormalities is relatively low (about 25%). In addition, due to the limitations of existing library construction and bioinformatics analysis, approximately 5% of cases with chromosomal abnormalities in twin or multiple births, chimeras, and parents are not suitable for NIPT analysis. At this stage, the technique is abnormal for other chromosomes. There is still no effective screening indication for structural abnormalities and microdeletion syndrome. Therefore, only the detection report of 21, 18, 13-trisomy syndrome is issued in the clinic, and other chromosome aneuploidy abnormalities will be analyzed at the same time. The written report is not issued at present. This method is not suitable for detecting abnormal ­chromosome structure, and is not suitable for detecting twins. For the above multiple births, it is not possible to use this test result as the basis for whether or not to terminate the pregnancy. The test result is “high risk” pregnant women, and further prenatal diagnosis is recommended.

6.6.4 Genetic Disease Diagnosis Report Each gene has a specific biological function that can be encoded into different proteins to maintain the structure and function in normal humans. Once a gene has a structural change in base pair composition or sequence, we call it Gene mutation. If the mutation of the gene causes the function of the encoded protein to be abnormal, or the number of chro-

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Normal, Bell-shaped curve

0.13%

2.14%

13.59%

34.13%

34.13%

13.59%

2.14%

0.13%

Percentage of cases in 8 portions of the curve Standard Deviations Cumulative Percentages

–4α

–3α

–2α

–1α

0.1%

2.3%

15.9%

–3.0

–2.0

–1.0

0

20

30

40

50

+1α

0 50%

+2α

+3α

97.7%

99.9%

+1.0

+2.0

+3.0

60

70

80

84.1%

+4α

Percentiles Z scores T scores Standard Nine (stanines) Percentage in stanine

–4.0

1

2

3

4

4%

7%

12%

17%

5 20%

6 17%

7 12%

8

9

7%

4%

+4.0

Fig. 6.9  The percentage of unique reads of chromosomes 21, 18, and 13 meets the standard normal distribution

mosomes or structural variation will lead to disease in the body, called genetic disease, it is often characterized by vertical transmission and lifelongness. According to the changes of genetic material, genetic diseases can be divided into five categories: chromosomal diseases, monogenic diseases, polygenic diseases, mitochondrial diseases, and somatic genetic diseases. Currently, there are more than 500 kinds of chromosomal diseases, and there are many natural abortion fetuses. 20–50% are caused by chromosomal abnormalities, and the incidence of chromosomal abnormalities in new life infants is also 0.5–1%. There are about 7000 single-gene genetic diseases confirmed in the world, and more than 3000 kinds of therapeutic genes are included in the OMIM database, such as color blindness, hemophilia, and thalassemia, all of which are monogenic diseases (Table 6.12). Cleft lip, cleft palate, hypertension, diabetes, manic depression, rheumatoid arthritis, and congenital heart disease all have multiple genetic basis and each has its heritability. There are 117 somatic genetic diseases in the OMIM database. The most common somatic genetic diseases are tumors, such as retinoblastoma, hereditary breast cancer, and familial adenomatous polyps. At present, molecular diagnosis in developed countries has been systematically and universally applied to the diagnosis and genetic counseling of single-gene genetic diseases. By analyzing a specific gene (DNA) or its transcript (mRNA) of a subject, it can not only patients can also be diagnosed by

screening for pathogenic gene carriers or high-risk individuals in the patient’s family, effectively reducing the incidence of these diseases. Genetic counseling and prenatal guidance for patients with fertility requirements and their families can prenatal diagnosis of the fetus through amniotic fluid or chorionic cells, which plays an important role in the early intervention of genetic diseases. Thus, genetic diseases are diseases caused by changes in genetic material, including chromosomal aberrations and genetic mutations that are invisible at the chromosomal level. At present, most genetic diseases do not have a good ­treatment method. Therefore, it is necessary to carry out targeted individualized medical tests based on the molecular basis of the disease to achieve molecular diagnosis of the disease. Below we will use the clinical molecular diagnosis of G6PD deficiency as an example to describe the interpretation of the molecular diagnosis results of genetic diseases.

6.6.4.1 Clinical Molecular Diagnosis of G6PD Deficiency Introduction to G6PD Deficiency G6PD deficiency is the most common X-linked incomplete dominant hereditary enzyme deficiency syndrome in the world [7]. About 400 million people worldwide are affected, and patients are eating broad beans. The cause of G6PD deficiency is due to mutation of G6PD, which catalyzes the dehy-

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Table 6.12  Common genetic diseases and their corresponding genes Number 1 2 3 4 5 6

7

8 9 10 11 12 13 14

15 16

17 18 19 20 21 22

23 24 25 26 27 28 29

30 31 32 33

Single gene disease Alpha-thalassemia Beta-thalassemia Congenital adrenal hyperplasia Hemophilia A Duchenne muscular dystrophy Adult autosomal dominant polycystic kidney type I Adult autosomal dominant polycystic kidney type II Phenylketonuria Spinal atrophy Lieber’s congenital cataract Congenital painless with no sweat Congenital contracture Congenital aniridia HLA matching

Wiskott-Aldrich syndrome Ornithine carbamoyltransferase deficiency Hepatolenticular degeneration Hereditary non-­ syndromic hearing loss X-linked ichthyosis Neurofibromatosis Osteogenesis Less sweat or no sweat-type ectodermal hypoplasia Spinal epiphyseal dysplasia Viscous lipid storage disease Methylmalonic aciduria X-linked chronic granuloma Severe combined immunodeficiency Huntington’s disease X-linked lymphocytic proliferative disease type II Ichthyosis vulgaris Cartilage hypoplasia Citrullineemia type II Spinocerebellar ataxia SCA3

Chromosome chr16 chr11 chr6

Gene HBA1,HBA2 HBB CYP21A2

chrX chrX

F8 DMD

chr16

PKD1

chr4

PKD2

chr12 chr5 chr17

PAH SMN1 AIPL1

chr1

NTRK1

chr5 chr11 chr6

chrX

FBN2 PAX6 HLA-A, HLA-B, HLA-DRA, HLA-DQB1 WAS

chrX

OTC

chr13

ATP7B

chr7

SLC26A4

chrX chr17 chr7 chrX

STS NF1 COL1A2 EDA

chr12

COL2A1

chr12

GNPTAB

chr6 chrX

MUT CYBB

chrX

IL2RG

chr4 chrX

HTT XIAP

chr1 chr4 chr7 chr14

FLG FGFR3 SLC25A13 ATXN3

Table 6.12 (continued) Number Single gene disease 34 Marfan syndrome 35 ADA type severe combined immunodeficiency disease 36 Limb-type muscular dystrophy (LGMD2B) 37 Rett syndrome 38 Spherocytosis

Chromosome Gene chr15 FBN1 chr20 ADA

chr2

DYSF

chrX chr14

MECP2 SPTB

drogenation of glucose phosphate to maintain the reducibility of the antioxidant glutathione (GSH), eliminate the toxicity of intracellular peroxides, and protect hemoglobin and cell membrane thiol protein while maintaining the stability of red blood cell structure and function. The G6PD gene mutation leads to a decrease in its enzymatic activity, and the peroxide formed in the red blood cells is easily damaged, and the red blood cells cannot be destroyed by oxidative damage, causing hemolytic anemia. The underlying cause is the insufficient production of NADPH and thus the low functional loss of GSH and the lack of oxidative vulnerability of Cat and GSHPX antioxidant dysfunction (Fig. 6.10). It is currently believed that G6PD deficiency is the result of natural selection of malaria, mainly distributed in the tropics, the Middle East, Southeast Asia, South America, the Mediterranean coast, and southern China, with an incidence rate of 5% to 25% (Table 6.13). China is one of the high-­incidence areas of this disease, showing a distribution of high and low north, with a prevalence of 0.2–44.8%. It is mainly distributed in the provinces south of the Yangtze River, with high levels in Hainan, Guangdong, Guangxi, Yunnan, Guizhou, and Sichuan provinces. Since most of the disease is usually free of clinical symptoms, early diagnosis and early intervention are very important for the prevention and control of the disease. If necessary precautions are taken, the severity of the clinical manifestations of the deficiency can be greatly reduced or prevented. G6PD deficiency is an X-linked incomplete dominant inheritance. The manifestations of enzyme deficiency are different, so the clinical manifestations vary and vary greatly, from asymptomatic to neonatal jaundice, drug-induced hemolysis, acute hemolysis caused by infection, etc. Severe jaundice in neonatal period causes death or permanent nerve damage. WHO classifies broad bean disease into five grades based on G6PD activity (Table 6.14). Because most of the disease is usually without clinical symptoms, early diagnosis and early intervention are very important for the prevention and control of the disease. If necessary precautions are taken, the clinical manifestations of G6PD deficiency can be greatly reduced or prevented.

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Fig. 6.10  Schematic diagram of the molecular mechanism of faba bean disease

Parasite plasma membrane

L-glutamate + L-cysteine γ - GCS

MRP

ne io th te a ut ga Gl nju Co

NADPH g -L-glutamyl-L-cysteine glycine

T

GS

GS

Toxic nucleophile GSH Reduction of hydrogen or lipid peroxides

NADPH GR

GSSG

Table 6.13  Incidence of G6PD deficiency Region Africa Middle East Asia Europe Americas Pacific

Total prevalence estimate Prevalence estimate for males 7.5% (7.1–7.9) 8.5% (7.9–9.1) 6% (5.7–6.4) 7.2% (6.6–7.7) 4.7% (4.4–4.9) 3.9% (3.5–4.2) 3.4% (3.0–3.8) 2.9% (2.4–3.4)

Pentose phosphat pathway:G6PD and 6-phosphoglucona dehydrogenase

5.2% (4.7–5.6) 3.8% (2.9–4.7) 5.2% (4.7–5.8) 3.4% (2.7–4.1)

Methods and Test Items for Clinical Molecular Diagnosis of G6PD Deficiency The gene that causes faba bean disease is the G6PD gene, and the molecular diagnosis is to detect the G6PD gene mutation. The detection of G6PD gene mutation can not only provide direct evidence of the pathogenesis of the disease at the genetic level, but also make an etiological diagnosis for the patient, and can make prenatal diagnosis for the fetus. Different mutation types have different effects on G6PD activity, so detecting G6PD gene mutations is also useful for determining prognosis and guiding treatment. Source of G6PD Deficiency Molecular Testing Project The cause of G6PD deficiency has been described in detail in the geneticist’s reference because of a decrease in the activity or inactivation of its encoded protein due to mutations in the G6PD gene. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has published a relationship between genotypes of G6PD deficiency and drugs such as rallizyme, and clearly indicates that G6PD gene status must be detected in patients with G6PD deficiency. The World Health Organization (WHO) also published a report on the relationship between G6PD deficiency and drugs, clearly indicating

NADP+

Table 6.14  G6PD variant WHO class and associated G6PD deficiency phenotype Class Class I Class II Class III Class IV Class V

Enzyme activity Serious lack of enzyme activity Serious lack of enzyme activity Moderate or slightly reduced enzyme activity Normal enzyme activity Increased enzyme activity

Enzyme activity ratio 1250 pg/mL, (4 AM) 893 pg/mL; PTH: 10.10 pg/mL; After ACTH excitation, cortisol rhythm: 8 AM 2.5 μg/dL, 4 PM 2.26 μg/dL, 0 AM 2.58 μg/dL. Supplementary Examination  There was no abnormality in pituitary MRI; no abnormality in bilateral adrenal CT plain scan; calcification of abdominal aorta, high-density shadow in left renal sinus parenchyma, and calcified renal artery can be seen. Treatment  ACTH prednisone.

intravenous

injection

and

oral

Treatment Outcome  Symptoms of pigmentation basically disappeared, and cortisol rhythm and ACTH rhythm basically returned to normal. This case was from the First Affiliated Hospital of Dalian Medical University.

40.4.5 Pheochromocytoma 40.4.5.1 Overview Pheochromocytoma originates from the adrenal medulla, sympathetic ganglion, or other chromaffin tissues. It can continuously or intermittently release large amounts of catecholamines, causing persistent paroxysmal hypertension and multiple organ dysfunction. Recent years, it has proved that the occurrence of pheochromocytoma is related to the mutations of pathogenic genes. These genes can be divided into two groups according to the use in different signal pathways: The first group is used in hypoxia pathway, which stimulates the expression of hypoxia-related growth factors by activating hypoxia-inducible factors, thus stimulating the growth of tumors, including VHL, SDHx (SDHA, SDHB, SDHC, SDHD, SDHAF2), HIF2A, IDH1, PHD2, KIF1B, and MDH2. The second group promotes tumor growth by activating MAPK and/or mTOR signal pathways, including NF1, RET, MAX, and TMEM127 [84–87]. The specific clinical manifestations are as follows: Unstable blood pressure, paroxysmal hypertension, increased

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blood sugar, decreased glucose tolerance, accelerated fat decomposition, constipation, intestinal dilatation, etc.

40.4.5.2 Laboratory Diagnosis of Pheochromocytoma Blood and Urine Catecholamines Norepinephrine and epinephrine in pheochromocytoma patients are increased, often more than twice the normal value. Urine VMA, MN and NMN These three are metabolites of catecholamine, so the level in pheochromocytoma patients also significantly increased, among which MN and NMN have better sensitivity and specificity.

H. Yuan et al.

3 . Adrenal incidentaloma with or without hypertension. 4. Patients with a family history of pheochromocytoma or related genetic syndrome. 5. Patients with a history of pheochromocytoma. Patients with family history or syndromic lesions should get specific genetic testing. For patients who have nonsyndromic lesions or family history, SDHB can be tested first, the positive result may lead to the diagnosis of malignant tumor, and it also can help in the localization and biochemical phenotype of the tumor.

Glucagon Provocation Test This test can be carried out during the intermittent period of paroxysmal hypertension.

40.4.5.4 Typical Medical Case Clinical Background  A 24-year-old female had paroxysmal palpitation, fatigue for more than 1 month, and space-­ occupying was found in right adrenal gland 20 days ago. No paroxysmal headache, pale face, no full moon face, buffalo back, centripetal obesity, acne and mental retardation, and weight loss was 5 kg in 4 months.

40.4.5.3 Genetic Testing Five kinds of people are recommended to get genetic testing [88], and the tests used commonly can be seen in Fig. 40.20.

Physical Examination  T37  °C, R20 times/min, P138 times/min, BP 125/86 mmHg, height 156 cm, weight 43.2 kg, BMI 17.8/m2, being confined to a chair.

1. Genetic testing in patients with known or suspected familial syndrome. 2. Patients whose symptoms of pheochromocytoma are caused by the use of DAD2 receptor antagonists, opioid, or 5-serotonin reuptake inhibitors.

Laboratory Examination  Thyroid function: TT4 106.4  nmol/L, TT3 1.86  nmol/L, FT3 4.73  pmol/L, FT4 17.05 pmol/L, TSH 1.13 mU/L, TG-Ab, TPO-Ab(–). Urine NE: 719.95  μg/24  h; Urine E: 84.88  μg/24  h; Urine DA: 467.36  μg/24  h; AFP: 96.75  μg/LCEA: 5.10  μg/L.  The

Fig. 40.20  Genetic testing for pheochromocytoma

40  Endocrine and Metabolic Diseases

rhythm of ACTH was normal with no abnormality in captopril test. Genetic testing—RET, VHL, SDHB, SDHC, and SDHD—were normal. Supplementary Examination  Abdominal ultrasound: Solid heterogeneous mass (6.4 × 3.9 cm) was found in the right adrenal gland near the retroperitoneum, with clear boundary and uneven internal echoes. No obvious abnormality was found in the left adrenal gland. Abdominal CT plain scan  A low circular density shadow was seen in the head of pancreas, with uneven density and multiple spots and strips of calcification. There was no obvious abnormality in thyroid ultrasound. Treatment  The patient was given antihypertensive drugs and had an operation on pheochromocytoma, and the pathological result was consistent with the diagnosis of pheochromocytoma. Treatment Outcome  The patient was cured. This case was from the First Affiliated Hospital of Dalian Medical University.

40.4.6 Congenital Adrenocortical Hyperplasia 40.4.6.1 Overview Congenital adrenal hyperplasia(CAH) is a disease caused by the mutations of genes and causes the deficiency of the enzymes which mediate the production of glucocorticoids and mineralocorticoids. So the common clinical manifestation of CAH is the symptoms that lack these hormones. Classical CAH: T21-hydroxylase deficiency (21-OHD) CPY21A2 gene mutation can lead to 21-hydroxylase deletion and masculinization. It is the most common type in classical CAH. According to the related clinical manifestations, 21-OHD can be divided into three types: salt-wasting phenotype, simple virilizing type, and nonclassical type. The most serious type is salt-wasting phenotype which lost 21-­hydroxylase completely. Neonates are susceptible groups. Besides the masculine manifestations, there are also serious digestive tract symptoms and dehydration. Simple virilizing type is caused by partial 21-hydroxylase deficiency. Female infants often have masculinization such as clitoral hypertrophy, while male infants have sexual precocity such as penile enlargement and muscular development after several months of birth. Nonclassical type is a syndrome caused by mild 21-hydroxylase deficiency, and its clinical manifestations are not obvious.

709

Nonclassical CAH This type is milder than classic CAH, and the symptoms can be irregular or absent menstrual periods, severe acne, and early appearance of pubic hair.

40.4.6.2 Laboratory Examination CAH can cause the decrease in serum cortisol concentration; serum 17-OHP and ACTH increased. And α1-24 ACTH stimulation test is the gold standard for identifying 21-hydroxylase deficiency and other steroid synthase deficiency. 40.4.6.3 Genetic Testing Genetic testing is helpful in the diagnosis of CAH, but if there are typical clinical symptoms and laboratory results, gene testing is not necessary. In recent years, some genes have been shown to be closely related to the occurrence of CAH and are currently being used: CYP21A2, CYP11B1, CYP17A1, HSD3B2, POR [89], STAR [90], PRKAR1A [91], ARMC5 [92], and CYP11A1 [93]. Genes and its associated phenotypes of congenital adrenocortical hyperplasia are summarized in Table 40.11. 40.4.6.4 Typical Medical Case Clinical Background  An elevated 17-hydroxyprogen was found in a 1-year-old male during neonatal screening. The 17-OHP level was detected at birth, 9 months, 10 months, and 1 year of age, respectively. The results increased gradually from 79 nmol/L to 225 nmol/L.

Table 40.11  Genes and its associated phenotypes of congenital adrenocortical hyperplasia Gene ARMC5 CYP11A1 CYP11B1 CYP17A1 CYP21A2

HSD3B2 POR

PRKAR1A STAR

Associated phenotypes ACTH-independent macronodular adrenal hyperplasia 2 Adrenal insufficiency, congenital, with 46, XY sex reversal Adrenal hyperplasia, congenital, due to 11-beta-hydroxylase deficiency Adrenal hyperplasia, congenital, due to 17α-hydroxylase deficiency Adrenal hyperplasia, congenital, due to 21-hydroxylase deficiency Hyperandrogenism, nonclassic, due to 21-hydroxylase deficiency 3-Beta-hydroxysteroid dehydrogenase, II deficiency Disordered steroidogenesis due to cytochrome p450 oxidoreductase deficiency, Antley–Bixler syndrome Pigmented nodular adrenocortical disease Lipoid adrenal hyperplasia

Inheritance AD AD/AR AD/AR AR AR

AR AR

AD AR

AD autosomal dominant inheritance, AR autosomal recessive inheritance

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Physical Examination  Height 78  cm, weight 10.5  kg, blood pressure 76/40  mmHg, penis 3  cm  ×  1  cm, testis 1.5 mL, scrotum pigmentless. Supplement Examination  No bilateral adrenal enlargement was found by B-mode ultrasonography, and the bone age was in accordance with the actual age. Family Gene Mutation Detection  CYP21 gene detection indicated that I172N and E3△8 mutations—I172N mutations originated from mother and E3△8 deletion originated from father—conformed to the clinical diagnosis of CAH. Diagnosis  Congenital adrenocortical hyperplasia (simple masculinization). Treatment  Hydrocortisone acetate was given, and the dosage was increased at any time according to the clinical manifestations. Treatment outcome  The patients’ height, weight, 17-OHP, and other hormone levels were normal, and the bone age was consistent with the actual age. This case was from the First Affiliated Hospital of Dalian Medical University.

40.5 Gout

Uric acid crystals

Fig. 40.21  Gout: Uric acid crystals deposited in joints

hypertension, and cardiocerebrovascular diseases, which pose a serious threat to health. The annual incidence of gout is 2.68 per 1000 people. There is a steady increase in the prevalence of gout with age, and it occurs 2–6 times more for men than for women due to estrogen resulting in urate loss in the urine. Ethnicity also strongly affects the gout prevalence and the prevalence rates of gout in Pacific Islander, New Zealand Maori, and Taiwanese groups is much higher [94]. The incidence of global gout increases persistently due to impropriate diet habits, lack of exercise, and a higher incidence of obesity and metabolic syndrome.

40.5.2 Clinical Appearance

Hong Yuan and Jingyuan Zhao Gout is an inflammatory arthritis caused by an innate immune response to monosodium urate (MSU) crystals deposited in synovial fluid. This chapter stocks up information about gout, methods of detection, and use of laboratory test results in recognizing and characterizing gout.

40.5.1 Overview Gout is a chronic urate crystal deposition disease, which is caused by acute or chronic inflammation and tissue damage caused by deposition of monosodium urate (MSU) (Fig.  40.21). Due to elevated blood uric acid levels, urate crystals are deposited in tissues, joints, and kidneys. In severe cases, joint damage and impaired renal function can occur. Without effective management, gout may turn into a chronic stage in some people, accompanied by the development of tophi (organized by urate and immune cells), permanent bone erosion, and process of disability. Patients with gout often accompanied by comorbidities like diabetes,

Gout is characterized by deposition of monosodium urate crystals in joints and tissues, which usually presents with intermittent painful attacks followed by remission in a long period. Acute gout attacks are usually monoarthritis, which can cause severe inflammation within a few hours, with major features of inflammation, including redness, fever, tenderness, swelling, and dysfunction, that is quickly relieved by NSAIDs or colchicine. Hyperuricemia may be associated, but blood uric acid levels are normal in some patients. Tophi and renal stones are late presentations. Tophi is a mass formed by a large amount of accumulated MSU crystals, which a characteristic clinical manifestation of gout. It is found in the auricle, subcutaneous tissue, and skin joints. About 10–25% of gout patients have uric acid stones in the kidney. The smaller ones are discharged into the gravel with urine without obvious symptoms. The larger one may cause renal colic, hematuria, dysuria, hydronephrosis, pyelonephritis, or periarteritis. Gout is also comorbid with other metabolic-based conditions, such as cardiovascular and kidney disease and type 2 diabetes, although the mechanism is not fully understood [95].

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40.5.3 Laboratory Diagnosis

rate. Blood cell count may be used to distinguish between septic arthritis and gout by determining whether there is an abnormal increase in the number of leukocytoses.

40.5.3.1 Blood Uric Acid Adult males and postmenopausal females with uric acid >420  μmol/L (7.0  mg/dL) and premenopausal women >350 μmol/L (5.8 mg/dL) can be diagnosed with hyperuricemia. Note that since large fluctuations of blood uric acid are common, blood testing should be repeated. There is a significant dose–effect relationship between the incidence and blood uric acid levels. When the blood uric acid level achieved 7.0–7.9  mg/dL, 8.0–8.9  mg/dL, ≥9.0  mg/dL, the incidence of gout was 10.8%, 27.7%, and 61.1%, respectively. Although hyperuricemia is a major characteristic feature of gout, not all patients with hyperuricemia showed gout symptom or UA crystals formation; it also should be noted that SUA may return to normal level during gout flare. If a diagnosis of gout is made, uric acid testing may be performed regularly to monitor levels. 40.5.3.2 Urine Uric Acid The 24-h urinary uric acid monitoring can help detect every cause of hyperuricemia. In the absence of a low-lying diet or drugs which may affect uric acid excretion, the normal level is 1.2–2.4 mmol (200–400 mg), and uric acid over 3.6 mmol (600 mg) indicates inner uric acid production increase. Renal function tests should be performed on such patients regularly due to their high risk of renal calculus formation. It may also be done to monitor people with gout and help them to choose the appropriate medicine to lower the uric acid level in the blood. 40.5.3.3 Synovial Fluid Analysis The gold standard for gout diagnosis is the identification of MSU crystals in the synovial fluid under polarized light microscopy. Using a compensator may be helpful to improve diagnostic efficiency. All the synovial fluid samples obtained from arthrocentesis and undiagnosed inflammatory joints require crystallization examination. MSU crystals can be found in the synovial fluid at every stage of the disease course almost, no matter during the flare, the critical stage or chronic stage. The leukocytic count, chemistry, culture, and sensitivity of synovial fluid are the further analysis [96]. In acute gout, the white blood cell count of synovial fluid may exceed 50,000  cells/mL (most of which are polymorphic). Glucose levels in the synovial fluid are often normal that is different from septic arthritis which glucose levels may be lower. Yet these two may be present in the one joint simultaneously; hence, bacteriology and Gram staining are also indispensable [97]. 40.5.3.4 Other Blood Tests Other blood tests usually include complete blood cell count, electrolytes, renal function, and erythrocyte sedimentation

40.5.3.5 Gene Testing In fact, the diagnosis of gout after the onset of disease leaves much to be satisfied in clinical practice. For preonset diagnosis, the genetic diagnosis has been the research focus these years. Recently, with in-depth knowledge of genome-wide association studies (GWAS) associated with blood uric acid levels, researchers demonstrated that most gout-related genetic variation mainly affects renal urate transport and reported multiple genetic sites to contribute to hyperuricemia and gout genetic susceptibility. Evidence has shown that the urate-related gene is situated on chromosome 15. GWAS found 28 sites related to uric acid, including SLC2A9/ GLUT9, ABCG2, LC22A11/OAT4, SLC22A12/URAT1, SLC17A1/NPT1, and cofactor PDZK1. They are all located on genes encoding urate transporters or other functional proteins. SLC22A12, SLC2A9, and ABCG2 are recognized genes that significantly affect uric acid levels. At present, genetic testing can help clinical diagnosis and decisions making on some certain circumstances, like whether a patient with hyperuricemia will develop into gout, or whether a patient with gout has potential flare risk and other adverse progression like tophi formation and joints damage. As we know, serum urate levels are the most important variables in predicting the risk of gout. However, only serum urate levels do not reliably enough to predict disease progression. SLC22A12 gene exon sequencing of gout patients found that 23% of patients had SLC22A12 mutations, suggesting that SLC22A12 is one of the main responsible mutant sites of gout [98]. SLC2A9, which is the most significant serum urate genetic factor, accounting for approximately 3% of urate level variations and complete loss of GLUT9 results in a more severe urate excretion disorder than URAT1 [99]. GWAS also discovered that the missense ABCG2 rs2231142 (Q141K) variant was concerned with serum urate concentration of European and East Asian ancestry. Evidence suggests that the 141K allele also has a positive correlation with increased uric acid and gout morbidity [100]. It was useful to create grades of ABCG2 dysfunction based on Q141 K and Q126X genotype combinations, and individuals with the dysfunctional variants 126X and 141K positive having the highest serum urate concentrations and highest risk for gout [101, 102]. Major genetic variations in ABCG2 and impact on phenotype are shown in Table 40.12. On the other hand, risk stratification involving genetic testing can provide more personalized intervention decisions. Genetic testing to identify genetic variants that predict no response to allopurinol and uric acid excretion drugs can provide the possibility of personalized selection of ULT.

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Table 40.12  Major genetic variations in ABCG2 and impact on phenotype Variant ABCG2 Q126X (rs72552713) ABCG2 Q141K (rs2231142) ABCG2 V12M (rs2231137) rs10011796

Gout risk Increases

Urate —

Allopurinol response —

Increases

Increases

Resistance

Decreases

Decreases

—

Increases

Increases

No association

Allopurinol is the most widely used ULT agent. GWAS has determined that ABCG2 141 K allele is related to the adverse allopurinol response [103]. In addition, human leukocyte antigen (HLA) variant HLA-B* 5801 is another important risk factor for severe allopurinol allergy syndrome (AHS), so it is recommended to perform this variant test before allopurinol usage for high-risk populations (Han, other Asian people) [104]. As profound development has been made in understanding the genetic basis of hyperuricemia and gout, as well as pharmacogenetics of lowering urate, it is of great significance to improve the clinical diagnosis of gout, to individualize the prognosis, to predict ULT response, and to provide potential new therapeutic targets.

40.5.3.6 Others Some other tests, such as basic metabolic panel, may be used to evaluate and monitor kidney function; antibodies like RF (rheumatoid factor) or ANA (antinuclear antibody) may be ordered to exclude other causes of arthritis symptoms. Blood culture and/or synovial fluid culture also should be ordered to differentiate septic arthritis. Magnetic resonance imaging (MRI), ultrasound, CT and dual-energy CT can well indicate urate crystals, lesions and surrounding soft tissue damage, and provide favorable evidence for the diagnosis of gout, thereby improving the sensitivity and specificity of the diagnosis of gout.

40.5.4 Management Keeping SUA levels below sedimentation threshold by dietary adjustment and uric acid-lowering drug use is the main goal of gout management. Patients education is fundamental to help patients develop a reasonable diet habit and lifestyle. Drink more water (no less than 1500–2000  mL/ day) to alkalinize urine to maintain urine pH at 6.2–6.9, which is conducive to the dissolution and discharge of urate crystals. At the same time, maintain a low-purine diet, exercise regularly to keep fitness, and quit smoking. A wholesome lifestyle not only helps reduce blood uric acid levels but also reduces gout flare. Patients with gout need to take medication for a lifetime to control symptoms.

However, treatment failure is also common, usually due to poor compliance of regular medications, which emphasizes the importance of patient education. When diagnosed as gout, patients need to be informed by the physician about the relevant knowledge of gout, including introduction of the uric acid reduction treatment plan, the long term and importance of treatment and the possible risks. For the physician, the start time of urate-lowering therapy needs to be negotiated with patient together, and individualization should be emphasized due to consideration of the patient’s comorbidities and medicine selection.

40.5.5 Conclusion Gout is a chronic urate crystal deposition disease, which is caused by acute and chronic inflammation and tissue damage caused by deposition of monosodium urate (MSU), which is directly related to hyperuricemia caused by sputum metabolism disorder or uric acid excretion. Although hyperuricemia is a major cause of gout, many patients with hyperuricemia do not develop into gout or form UA crystals. Risk factors for gout include gender, age, ethnicity, and genetic factors, and their interactions increase the incidence of gout. The diagnosis and evaluation of gout are multifaceted, joint puncture, and MSU crystal detection are still the basis for gout diagnosis. Gout diagnosis is usually based on clinical manifestations, age, comorbidities, symptoms, clinical symptoms, and laboratory results. In 2015, the American College of Rheumatology (ACR) and the European Union of Rheumatology (EULAR) jointly launched a new version of the gout diagnostic criteria, which considers the “swelling pain or tenderness of a joint or mucous sac” as a condition for entering the diagnostic process. The appearance of uric acid crystals or tophi in the symptomatic joint or mucous sac can be used as a sufficient condition for the diagnosis of gout. The diagnosis process is shown in Fig.  40.22. If this condition is not met, the results will be scored by clinical symptoms, laboratory tests, imaging examinations, etc., and ≥8 points will be diagnosed as gout. Gout classification criteria are shown in Table 40.13. The diagnosis of gout requires a close combination of epidemiology, etiology, and diagnostics. It is necessary to familiarize with gout itself and potential susceptibility factor and improve the diagnostic efficiency of gout [105].

40.5.6 Typical Medical Case Clinical Background  A 69-year-old male patient with multiple joint swelling and pain, limited mobility for 10 years.

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713

Fig. 40.22 Diagnosis procedure of gout

Table 40.13  The ACR/EULAR gout classification criteria Criteria Pattern of joint/bursa involvement during symptomatic episodes(s) ever

Characteristics of symptomatic episode(s) ever: • Erythema overlying affected joint (patient-reported or physician-observed) • Cannot bear touch or pressure to affected joint • Great difficulty with walking or inability to use affected joint Time course of episode(s) ever: Presence (ever) of ≥2, irrespective of anti-­inflammatory treatment: • Time to maximal pain 4 weeks from the start of an episode (i.e., during intercritical period); if practicable, retest under those conditions. The highest value irrespective of timing should be scored Synovial fluid analysis of a symptomatic (ever) joint or bursa: (should be assessed by a trained observer) Imaging evidence of urate deposition in symptomatic (ever) joint or bursa: ultrasound evidence of double-contour sign or DECT demonstrating urate deposition Imaging evidence of gout-­related joint damage: conventional radiography of the hands and/or feet demonstrates at least one erosion

Categories Ankle or midfoot (as part of monoarticular or oligoarticular episode without involvement of the first metatarsophalangeal joint Involvement of the first metatarsophalangeal joint (as part of monoarticular or oligoarticular episode) No characteristic One characteristic Two characteristics Three characteristics No typical episode One typical episode Recurrent typical episodes

Score 1

0 1 2 3 0 1 2

Absent Present

0 4

 T by Sanger sequencing

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41  Neurological Disease trophy: workshop 9th June 2010, LUMC, Leiden, The Netherlands. Neuromuscul Disord. 2012;22:463–70. 57. Richard P, Trollet C, Stojkovic T, et  al. Correlation between PABPN1 genotype and disease severity in oculopharyngeal muscular dystrophy. Neurology. 2017;88:359–65. 58. Udd B. Distal myopathies. Curr Neurol Neurosci Rep. 2014;14:434. 59. Arnold WD, Kassar D, Kissel JT. Spinal muscular atrophy: diagnosis and management in a new therapeutic era. Muscle Nerve. 2015;51:157–67. 60. St Louis EK, Cascino GD. Diagnosis of epilepsy and related episodic disorders. Continuum. 2016;22:15–37. 61. Fisher RS, Cross JH, D’Souza C, et al. Instruction manual for the ILAE 2017 operational classification of seizure types. Epilepsia. 2017;58:531–42. 62. Noh GJ, Asher YJT, Graham JM. Clinical review of genetic epileptic encephalopathies. Eur J Med Genet. 2012;55:281–98. 63. Nolan D, Fink J. Genetics of epilepsy. Neurogenetics, part II. Handb Clin Neurol. 2018;148:467–91. 64. Poduri A, Sheidley BR, Shostak S, et  al. Genetic testing in the epilepsies–developments and dilemmas. Nat Rev Neurol. 2014;10:293–9. 65. Srivastava S, Cohen JS, Vernon H, et  al. Clinical whole exome sequencing in child neurology practice. Ann Neurol. 2014;76:473–83. 66. Mctague A, Howell K, Cross J, et  al. The genetic landscape of the epileptic encephalopathies of infancy and childhood. Lancet Neurol. 2016;15:304–16.

749 67. Wirrell E.  Infantile, childhood, and adolescent epilepsies. Continuum. 2016;22:60–93. 68. Mangold S, Blau N, Opladen T, et  al. Cerebral folate deficiency: a neurometabolic syndrome? Mol Genet Metab. 2011;104:369–72. 69. Segal EB, Grinspan ZM, Mandel AM, et  al. Biomarkers aiding diagnosis of atypical presentation of pyridoxine-dependent epilepsy. Pediatr Neurol. 2011;44:289–91. 70. Helbig I, Heinzen EL, Mefford HC.  Primer part 1—the building blocks of epilepsy genetics. Epilepsia. 2016;57:861–8. 71. Ottman R, Hirose S, Jain S, et  al. Genetic testing in the epilepsies—report of the ILAE Genetics Commission. Epilepsia. 2010;51:655–70. 72. Mefford HC.  CNVs in epilepsy. Curr Genet Med Rep. 2014;28:162–7. 73. Dibbens LM, Tarpey PS, Hynes K, et  al. X-linked protocadherin 19mutations cause female-limited epilepsy and cognitive impairment. Nat Genet. 2008;40:776–81. 74. Scheffer IE, Turner SJ, Dibbens LM, et  al. Epilepsy and mental retardation limited to females: an underrecognized disorder. Brain. 2008;131:918–27. 75. Morimoto M, Mazaki E, Nishimura A, et  al. SCN1A mutation mosaicism in a family with severe myoclonic epilepsy in infancy. Epilepsia. 2006;47:1732–6.

Reproductive Organ Cancer

42

Jinhai Tang, Xiangjun Cheng, Jieshi Xie, Zheng Cao, Yanhong Zhai, and Boyan Song

42.1 Breast Cancer Jinhai Tang and Xiangjun Cheng

42.1.1 Overview Breast cancer is the most commonly diagnosed cancer in women and the leading cause of cancer death. About one million new cases are diagnosed each year in China [1]. According to the latest data released by the World Health Organization in 2017, the number of breast cancer deaths in China reached 49,011, accounting for 0.52% of the total deaths. The age-adjusted mortality rate is 5.76 deaths per 100,000 people, ranking China 179 globally. Worldwide, there will be an estimated 2.1 million newly diagnosed breast cancer cases for women in 2018, accounting for a quarter of all cancers in women. And there will be approximately 0.63 million breast cancer death in 2018 [2]. Recently, the incidence of this cancer is increasing in developing countries. Breast cancer is the most frequently diagnosed cancer in most countries (154 of 185 countries) and is also the leading cause of cancer deaths in more than 100 countries; the main exceptions are Australia/New Zealand, Northern Europe, North America (where lung cancer is the top one), and many countries in sub-Saharan Africa (due to the increased inciJ. Tang (*) · Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China e-mail: [email protected] X. Cheng Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China

dence of cervical cancer [2]). The incidence of breast cancer is the highest in countries such as Australia/New Zealand, Northern Europe (e.g., UK, Sweden, Finland, and Denmark), Western Europe (Belgium, Netherlands, and France), Southern Europe (Italy), and North America [2]. About 5–10% of breast cancer cases are related to hereditary and genetic factors, including a personal or family history of breast or ovarian cancer, and genetic mutations (BRCA1, BRCA2, and other genes susceptible to breast cancer). Studies on immigration have shown that nonhereditary factors are the main driver of observed differences in international and interethnic incidence. A comparison of migrating low-risk populations to high-risk populations shows that the incidence of breast cancer has increased for successive generations. However, it is limited to understanding geographic or temporal changes in the incidence associated with a specific cause. Over the past few decades, the incidence of breast cancer has been rising in most countries in transition, such as South America, Africa, and Asia. These trends may reflect a combination of demographic factors related to social and economic development, including delayed births and fewer children, higher levels of obesity and lack of physical activity, and increased breast cancer screening and awareness. In some developed countries, including the United States, Canada, the United Kingdom, France, and Australia, the decline of incidence in the early 2000s was partly due to the reduced use of postmenopausal hormone therapy after the publication of the Women’s Health Initiative trial link Posthormonal use to increase the risk of breast cancer [3]. The main risk factors for breast cancer are not easily changed because they result from long-term endogenous hormone exposure; although prevention by promoting breastfeeding, especially for longer durations, may be beneficial.

J. Xie · Z. Cao (*) · Y. Zhai (*) · B. Song Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China © People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_42

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Fig. 42.1  Anatomy of the breast Second rib

Sebaceous gland Montgomery Lactiferous tubercle duct

Pectoralis major muscle Pectoralis minor muscle Intercostal muscles

Retromammary fat

Areola Nipple

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42.1.2 Anatomy of the Breast

42.1.3 Histological Classification

The breasts are modified skin gland that are located at the front of the chest and also partially outside the chest. The upper part of each breast extends to the second rib, the lower part extends to the sixth rib cartilage, the middle extends to the sternum, and the lateral extends to the midaxillary line. It illustrates the lobular system of the breast and the anatomic location of some common pathological changes (Fig. 42.1). The catheter system contains numerous leaflets with acinar cells. Each leaflet is fed into a terminal catheter, which in turn is fed into a segmented catheter. The segmented catheter eventually enters the collection catheter, where about 15–20 converge under the areola through a separate orifices and reach the nipple surface. The three most common causes of female breast mass are cysts, fibroadenomas, and cancer. Cysts and fibroadenomas come from lobule lesions, and carcinomas developed in the terminal ducts. Nipple adenomas also come from segmental ducts, near the openings in the nipple. Paget’s disease of the breast is the shedding of skin in the nipple-areola complex. This usually indicates the presence of potential breast cancer. Ductal carcinoma in situ and invasive carcinoma each account for about half.

The classification of breast tumor was confirmed by the World Health Organization (Table 42.1). All types of breast cancer are classified based on their histological and/or cytological appearance. Regardless of the type of cancer, some general findings should be recorded, including the location, size, shape, consistency, color, overall appearance of the margins, and the adjacent breasts (skin, nipples) and extramammary structures (fascia, muscle), and the number of malignant lesions.

42.1.4 Surveillance and Diagnosis 42.1.4.1 Imaging Technologies and Applications in Early Diagnosis and Prognosis for Breast Cancer There is growing interest in developing imaging tests to screen for breast cancer, especially in high-risk groups where traditional techniques are inadequate. Mammography is considered to be the most effective method for early detection of breast cancer. The limitations of this approach are the impetus for efforts to improve existing mammography techniques and develop new technologies that improve breast cancer detection capabilities. Ultrasound is expected to be a method

42  Reproductive Organ Cancer Table 42.1  Histological classification of carcinoma of breast [adapted from WHO] Invasive ductal carcinoma, not otherwise specified (NOS) 8500/3 Mixed type carcinoma Pleomorphic carcinoma 8022/3 Carcinoma with osteoclastic giant cells 8035/3 Invasive lobular carcinoma 8520/3 Tubular carcinoma 8211/3 Invasive cribriform carcinoma 8201/3 Medullary carcinoma 8510/3 Mucinous carcinoma and other tumours with abundant mucin Mucinous carcinoma 8480/3 Cystadenocarcinoma and columnar cell mucinous 8480/3 carcinoma Signet ring cell carcinoma 8490/3 Invasive papillary carcinoma 8503/3 Invasive micropapillary carcinoma 8507/3 Apocrine carcinoma 8401/3 Metaplastic carcinomas 8575/3 Pure epithelial metaplastic carcinomas 8575/3 Mixed epithelial/mesenchymal metaplastic carcinomas 8575/3 Lipid-rich carcinoma 8314/3 Adenoid cystic carcinoma 8200/3 Acinic cell carcinoma 8550/3 Glycogen-rich clear cell carcinoma 8315/3 Inflammatory carcinoma 8530/3 Lobular carcinoma in situ 8520/2 Ductal carcinoma in situ 8500/2 Microinvasive carcinoma

for detecting cancer in women with dense breast tissue, which is often problematic for conventional film mammography. Ultrasound also plays an important role in breast imaging. As an aid to diagnostic mammography, it can be used for biopsy guidance, assessment of accessible masses, and continuous assessment of benign masses. Magnetic resonance imaging (MRI) is an accepted diagnostic method for many breast-related indications and is sensitive to tumors. On the contrary, its specificity is poor. If the area does show enhancement, it may or may not be a tumor. To address this, further imaging or biopsy may be required. Digital mammography systems use digital detectors to convert X-ray photons into digital signals for display on a high-resolution monitor. These systems provide features that conventional film X-ray mammography cannot provide. PET-CT plays an important role in detecting local disease recurrence and distant metastases in patients with breast cancer. Without a perfect screening program, women’s risk of breast cancer varies widely, and screening can lead to unnecessary procedures and alerts, so ideally screening should be tailored to the individual’s cancer risk.

42.1.4.2 Mammography There are two types of mammograms: screening and diagnosis. Screening mammograms are X-rays of the breasts per-

753 Table 42.2  BI-RADS mammography categories according to the American College of Radiology BI-RADS Assessment categories Category 0 Need additional imaging evaluation Category 1 Negative. Keep screening Category 2 Benign finding. Keep screening Category 3 Probably benign finding. Short interval of follow-up is suggested Category 4 Suspicious abnormality. Biopsy should be considered Category 5 Highly suggestive of malignancy. Appropriate action should be taken

formed by women when they are asymptomatic, which means they are in the preclinical stage. This is the purpose of screening mammograms, and the goal is to be able to detect any abnormalities with higher sensitivity (Table 42.2). Diagnostic mammography is an X-ray examination of women who have breast discomfort (e.g., a lump or nipple discharge) or found abnormalities during mammography screening. Diagnostic mammography is more complicated and time-consuming than screening mammography, and can be used to determine the exact size and location of breast abnormalities and to image surrounding tissue and lymph nodes. In general, several other views of the breast are imaged and interpreted during a mammogram diagnosis, especially in women with breast implants or a personal history of breast cancer. Although mammography is still the gold standard, it does have limitations, especially in women with dense breasts. New imaging technologies are emerging to overcome these limitations and enhance cancer detection capabilities and improve patient prognosis.

42.1.4.3 Ultrasound Ultrasound has become the main diagnostic method after mammography. Its diagnostic and guidance role has expanded and developed, and recently, we have begun to reevaluate the process of ultrasound as an auxiliary examination tool for women at high risk, dense breasts, or both after mammography. Ultrasound: As a detection program for breast cancer, ultrasound cannot replace mammograms for breast cancer screening. One of the advances in medical and imaging research over the past two decades has been the significant expansion of breast ultrasound’s ability to assess breast disease. Ultrasound has become an essential component of breast cancer diagnosis and prognosis. 42.1.4.4 Magnetic Resonance Imaging MRI is one of the most relevant breast cancer diagnostic tools today (Table 42.3). It is widely used to screen patients with an increased risk of breast cancer, such as BRCA-­ positive patients; it is also widely used to select the best treatment. MRI has the highest sensitivity in breast cancer imaging, but its low specificity remains its biggest drawback.

754 Table 42.3  Uses of MRI in breast cancer MRI in breast cancer MRI screening BRCA carriers Untested first-degree relatives of BRCA carriers Individuals with more than 20% lifetime risk of breast cancer Extent of disease evaluation Risk of change to a more extensive treatment due to false additional disease MRI-guided biopsy is recommended prior to changing the treatment Use in selected patients Study of the contralateral breast Risk of change to a more extensive treatment due to false additional disease MRI-guided biopsy is recommended prior to changing the treatment Low positive predictive value Evaluation of axillary metastasis High sensitivity and specificity in detecting axillary node metastases UPSIO-enhanced MRI has the highest sensitivity and specificity Not a replacement for SLNB Evaluation after neoadjuvant chemotherapy Best imaging technique in correlation between the preoperative measurements and the pathological findings High specificity and a low sensitivity in predicting pathological complete remission Suitable for selecting patients for neoadjuvant chemotherapy

Detection of breast cancer by MRI is based on tumor angiogenesis. In tumors, the capillaries of the tumor and surrounding stroma uncontrollably proliferate, forming abnormal blood vessels with increased permeability. The increase in permeability leads to the rapid penetration of contrast agent into the interstitial space, which leads to an increase in the signal in MRI, which can describe the shape and nature of the tumor. Due to this tumor angiogenesis, contrast-­enhanced breast MRI is the most sensitive imaging technique. It is currently used to detect invasive breast malignancies.

42.1.4.5 B  reast Cancer Biomarkers for Risk Assessment, Screening, Detection, Diagnosis, and Prognosis Early detection of disease can prevent breast cancer mortality. Over the past few decades, progress has been made in identifying invasive and noninvasive biomarkers. Genetic biomarkers, based on mutations and single nucleotide polymorphisms (SNPs) associated with breast cancer, have potential uses in screening high-risk populations to identify individuals who may have the disease. Among epigenetic markers, hypermethylation and specific microRNA (miR) analysis of selected genes can be used for cancer detection, diagnosis, and prognosis. Some new methods will be introduced that make currently applicable techniques and analytical methods suitable for clinical use. The ultimate goal of the test is to identify (a) biomarkers that can be analyzed in noninvasively collected samples, (b) cheap analytical methods, and (c) biomarkers that show high sensitivity and specificity.

J. Tang et al.

42.1.4.6 Genomic Biomarkers BRCA1 was the first gene that has been shown to be susceptible to hereditary breast cancer. Subsequently, BRCA1 (located at 17q21) has also been confirmed to also indicate ovarian cancer [4, 5]. Many cohorts with exposure data and other participant details have been used to identify genetic biomarkers associated with breast cancer [6]. The Cooperative Oncology Genetic Environment Study (COGS) is a large-­ scale genotyping study funded by the European Commission. More than 150,000 samples have been genotyped in this study. A family-based high-permeability susceptible gene was identified through association studies, and then a low-­ permeability gene was identified. Carriers of such genes and single nucleotide polymorphisms (SNPs) are susceptible to breast cancer. Pharoah and Caldas found that a set of 70 genes can predict the prognosis of breast cancer. Genomic markers include SNPs, mutations, additions and deletions, recombination, and copy number changes. Different research groups of M.  Verma and D.  Barh 395 have performed genome-wide association studies (GWAS) to identify breast cancer susceptibility genes that may be useful for breast cancer screening in high-risk groups [7–9]. In another study, 2702 women with invasive breast cancer of European descent and 5726 controls were conducted [10]. The SNPs identified in this study were mainly located in 1p11.2, 2q35, 3p, 5p12, 8q24, 10q23, 13, 14q24.1, and 16q regions. The affected by these SNPs are involved in actin cytoskeleton regulation, glycan degradation, alpha-linolenic acid metabolism, circadian rhythm regulation, and drug metabolism. 42.1.4.7 Epigenomic Biomarkers and Methylation Biomarkers The term epigenome is used to define the overall epigenetic state. The basic biological characteristics of a DNA fragment (such as gene density, replication time, and recombination) are related to its GC content. The promoter region is rich in CpG content. A genomic region of about 0.4 kb with a GC content of about 50% is called a CpG island. In mammals, CpG islands are 200–300 bp. Promoters of tissue-specific genes located in CpG islands are usually unmethylated. However, during breast cancer development, these CpG sites begin to methylate. Cytosine methylation can regulate gene expression by preventing the association of certain transcription factors with their associated DNA recognition sequences. Methyl CpG binding protein (MBP) can bind to methylated cytosines and mediate inhibitory signals, or MBP can interact with chromatin-forming proteins to modify the surrounding chromatin, thus linking DNA methylation to chromatin modification. DNA methylation at cytosine 5 is performed by DNA methyltransferase (DNMT). These enzymes are necessary to initiate and maintain methylation. Cancer cells accumulate abnormal patterns of DNA methylation, leading to a malignant breast cancer phenotype. The distribution of methylated genomes is not very clear, and

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many GWAS identifications have been performed to identify biomarkers associated with breast cancer risk [8, 11, 12]. Using methylated DNA immunoprecipitation combined with high-throughput sequencing (MeDIP-seq), methylation levels were compared in normal and breast cancer cell samples, and in breast cancer samples, especially in the CpGrich region, overall are insufficiently methylated. The location of these CpG-rich regions is independent of the transcription start sites of various genes. Using this method, the pattern of methylation during epithelial-to-mesenchymal transition was also evaluated and used for disease stratification. Methyl receptivity in malignant breast tissue is approximately 2–3 times higher than in matched controls. However, methyl acceptability varies widely among patients. Quantitative analysis of 5meC levels showed a significant decrease compared to normal tissues. BRCA1 and BRCA2 cancers have slightly lower levels of hypomethylation, but are significant. Genome-wide hypomethylation is associated with satellite sequence hypomethylation. Defined area (Sa2 encoding) chromosome 1 and satalpha are specifically hypomethylated. On chromosome 5, the region containing the SATr-1 coding sequence also showed insufficient methylation.

42.1.4.8 miR Biomarkers miR is a key regulator of many gene expression regulators. Tissue-specific miRs have been reported in different groups [13]. These RNAs are small and have a unique stem-loop structure. Many miRs can be cyclically separated. Due to their smaller size and stability (due to the secondary structure), these circulating miRs provide a rich source of diagnostic biomarkers for breast cancer. The relationship between more than 300 miRs and breast cancer has been evaluated in inflammatory breast cancer cells [14]. The most promising miRs are miR-29a, miR-30b, miR-342-5p, and miR-520a-5p. Analysis of these functions miRs reveals their role in cell proliferation and signal transduction pathways. Whenever a subtype analysis of breast cancer cells is required, these markers can be used to identify inflammatory breast cancer cells. The promoter region of the miR coding region was evaluated by combining 5-methylcytosine immunoprecipitation with miR slice microarray analysis. Several miR promoters were found to be hypermethylated, particularly miR-31, miR-130a, miRlet7a-3/let 7-b, miR-155, and miR-­137 [13]. Mitchell and colleagues have demonstrated the advantages of using miR to detect cancer due to the stability of miR even in fixed tissues. miR-155 predicts the prognosis of triple-negative breast cancer (higher miR-155 expression is associated with higher angiogenesis and aggressiveness) [15]. In summary, miR can be used for breast cancer screening and risk assessment before the disease develops. Furthermore, a group of miRs can be used for the detection and diagnosis of breast cancer. To follow up on breast cancer treatment, miR analysis can also be applied on breast cancer prognosis and survival assessment.

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42.1.4.9 B  reast Circulating Tumor Cells Potential Biomarkers for Breast Cancer Diagnosis and Prognosis Evaluation Circulating tumor cells (CTC) are considered as an indicator of tumor invasion. CTCs have recently been detected in breast cancer patients and they have become targets for assessing breast cancer progression, prognosis, and diagnosis. CTCs are heterogeneous population with a phenotype from epithelium to mesenchyme. CTCs express various markers according to the stage of epithelial-mesenchymal transition, including epithelial cell adhesion molecules, cytokeratin, and MUC-1. CTCs are usually detected and confirmed in two steps, including enrichment and identification. These methods have become powerful tools for diagnosing and predicting response to systemic therapies. CTCs have been shown to have prognostic and diagnostic effects in breast cancer patients and are associated with PFS, DFS, and OS. Early breast cancer patients with CTCs have a high risk of metastasis. Recent results have also demonstrated a correlation between the presence of CTCs and the histological grade difference of the primary tumor. Evaluation of CTC during treatment can provide information on the efficacy of the treatment and the risk of relapse. In addition, analysis of the molecular characteristics of CTCs can provide information on therapeutic and chemoresistance target proteins. However, before CTCs can be used as a powerful tool for breast cancer diagnosis and prognosis, further development is needed, including identifying specific markers for breast cancer CTCs, developing high sensitivity and specificity methods for detecting CTCs, and exploring the molecular characterization of CTCs. In terms of CTCs markers related to cancer progression, recurrence, and metastasis. However, the rapid increase in breast cancer CTCs research will make CTCs a powerful tool for breast cancer diagnosis and prognosis in the near future. 42.1.4.10 Tumor-Specific Protein 70 (SP70) Owing to the lack of accuracy, most current tumor markers are limited in clinical. Some candidate markers have been reported in recent years. SP70 was reported to be a new protein mainly expressed in nonsmall cell lung cancer (NSCLC) with the relative molecular mass (Mr) of 70 kDa. It could be a sensitive biomarker for monitoring timely response to chemotherapy in patients with advanced NSCLC. Not only that, it was found to be highly expressed in breast cancer tissues, and significantly associated with the tumor stage, metastasis, early recurrence, and viral load. We continuously compared the interplay between the common clinical serum tumor markers and tumor size, classification, and lymphatic metastasis. CEA is only associated with tumor stages (P  =  0.01), while CA15-3 is associated with tumor sizes (P = 0.025). CA125 showed no obviously associations with these clinical characteristics in breast cancer patients. However, levels of SP70 were positively related to advanced tumor stages (III–IV), lager tumor sizes, and

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Fig. 42.2  Comparison of serum biomarkers levels among breast cancer Pathological parameters; N0: No Lymphnode metastasis; N1: Lymphnode metastasis; T1: Maximum diameter of tumor ≤ 20 mm; T2: Maximum diameter of tumor >20 mm; Tumor staging: I–II, III–IV; *P   35  U/mL is indicative of potential malignancy. The postoperative serum concentration of CA125 > 65 U/ mL is associated with worse 5-year survival. As a serum biomarker of ovarian cancer, CA125 can be detected increased in half of the patients at early stage of disease, but almost 90% patients in late-stage ovarian cancer. Overall, almost 80% of ovarian diseased tissues abnormal express CA125 observably, and the amount of CA125 expression varies with histotype. CA125 expression is the

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most in serous carcinomas (85%) and the least in mucinous cancers (12%). In papillary, endometrioid, clear cell, and undifferentiated adenocarcinomas, the abnormal expression of CA125 is 68%, 65%, 40%, and 38%, respectively. Although the abnormal expression of CA125 can be detected in most ovarian tissues, the sensitivity of CA125 is limited. On the other hand, the specificity of CA125 is unsatisfactory. In menopausal women, the specificity of CA125 assay is about 99%, but it does not attain 99.6% for specificity required to achieve a positive predictive value of 10%. In premenopausal women, many benign diseases (peritonitis, liver cirrhosis, menstruation, pregnancy, endometriosis, adenomyosis, and salpingitis) can induce CA125 expression increased. Benign ovarian cysts and tumors, uterine fibroids, inflammation of the pleura, peritoneum, or pericardium can also promote the increase of CA125 regardless of age. The base value of CA125 usually exceeds 35  U/mL in some women without any recognizable disease. Besides, other cancers, such as breast cancer and lung cancer, can also lead to a high expression of CA125, which complicates the diagnosis for ovarian cancer [21]. From the above, CA125 is not sufficiently specific for early diagnosis of ovarian carcinoma. Nowadays, to elevate the diagnostic accuracy of ovarian cancer, CA125 assay is usually combined with ultrasonography or other biomarkers. Human Epididymis 4 (HE4) HE4 is a whey-acidic-protein (WAP) expressed in different organ tissues, found by Kirchhoff in 1991. It has been confirmed that HE4 exhibits high expression in endometrial cancer, pulmonary cancer, breast adenocarcinomas, and mesotheliomas. As HE4 expression in ovarian cancer is significantly higher than normal ovarian tissues, it is considered as a potential biomarker of ovarian cancer. Normally, the serum concentration of HE4 is increasing steadily with age, from 41.1 to 82.1,  pmol/mL HE4 is reduced during pregnancy which is different from CA125 [22]. Lu and colleagues demonstrated that HE4 was associated with ovarian carcinoma cell maturation and played an important role in tumor cell adhesion and motility [23]. Moore and colleagues implied over-expressed HE4 can promote ovarian cancer growth and chemoresistance [24]. Lin and colleagues considered HE4 could distinguish malignant ovarian cancer from benign ovarian diseases effectively [25]. FDA has authorized HE4 as ovarian cancer biomarker to monitor ovarian cancer disease progression and recurrence. All of the endometrioid ovarian cancer and 93% serous ovarian cancer overexpress HE4. Diagnostic sensitivity of HE4 is 82.5% in serum from ovarian cancer, higher than CA125, with 95% specificity. In urine sample, HE4 sensitivity is up to 86.6% and 89.0% with 94.4% specificity for stage I/II and stage III/IV, respectively. Generally, both HE4 and CA125

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are complementary. Moore and colleagues suggested that simultaneous detection of HE4 and CA125 is better than either alone in ovarian cancer diagnosis [24]. When combined with CA125, HE4 sensitivity can elevate from 72.9% to 76.5% at 95% specificity in urine and serum samples [26]. Therefore, Combined HE4 and CA125 assays can improve the accuracy of ovarian cancer diagnosis, exclude other benign ovarian diseases. Generally, both HE4 and CA125 are abnormally increased in serum of ovarian cancer; in benign ovarian tumors or other benign diseases, the serum concentration of CA125 elevates alone usually; high concentration of HE4 and normal CA125 suggest the presence of epithelial ovarian cancer or other types of cancer (e.g., endometrial cancer).

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filtration rate was increased by 42% in early-stage and 75% in late-stage in urine samples from the same donors. It was implied that urine may be the better sample for MSLN detection [21].

Osteopontin Osteopontin is an acidic calcium binding glycoprotein synthesized by osteoblast and vascular endothelial cells. It is an important component of the extracellular matrix. Osteopontin has been identified as a potential biomarker of ovarian cancer by the high-throughput cDNA microarray system. Highly expression of osteopontin was observed in epithelial ovarian cancer and the amount of osteopontin in borderline and invasive ovarian cancer was more than benign tumors. Osteopontin takes effect on tumor cells metastasis and tumor Mesothelin progression in a stressful environment. Besides, it enhances Mesothelin (MSLN) is a glycosylphosphatidylinositol-­ tumor cells survival by increasing Akt activation and HIF-1 anchored membrane glycoprotein which is located on the alpha expression. Osteopontin is also complementary to cell surface of mesothelial cells lining the pericardium, other ovarian cancer biomarkers. The sensitivity of osteopleura, and peritoneum in normal physiological conditions. pontin alone yields to 81.3%, but the value enhances to Human MSLN is composed of 16 exons and produces three 93.8% in combination with CA125, at 33.7% specificity. predominant variants (variant 1, variant 2, and variant 3) by Detecting osteopontin, insulin-like growth factor, prolactin, alternative splicing. It has been provided by Hellstrom that and leptin simultaneously raises diagnostic sensitivity to MSLN1 primarily expresses on the cell surface and soluble 96% at a specificity of 94% [21]. Further, superpose macroMSLN derived from cleavaged variant 1 can be released to phage inhibitory factor and CA125 test can achieve a sensibody fluid in some patients suffering from several tumor tivity of 95.3% at 99.4% specificity. The histological types, types [18, 27]. Normal mesothelin cells express MSLN in grades or stages of ovarian cancer barely affect the amount of trace amount. While the expression of MSLN increases osteopontin. At cut-off value of 252 ng/mL, the osteopontin markedly in human cancers (ovarian cancer, pancreatic ade- specificity is 80.4%, and the sensitivities of early and late nocarcinoma, and mesotheliomas). Especially, almost all of stages ovarian cancer are 80.4% and 85.4%, respectively serous borderline and cyst adenocarcinoma display the high [26]. So far, more data of early-stage cases are needed to concentration of MSLN in urine samples. Therefore, MSLN evaluate the application of multiple marker combinations. has been identified as a tumor-associated marker. Notably, a fragment derived from osteopontin has been Furthermore, MSLN can combine with CA125 and plays an found in ovarian cancer patients’ urine. important role in tumor cells adhesion, chemoresistance, and tumor progression. As tumor marker, MSLN shows the Kallikreins oncogenic properties, promotes ovarian cancer cells invasion Human kallikrein family consists of 15 members all of which and adhesion through MAPK/ERK and JNK pathways, and have serine proteases activity and are involved in tumor cells induces drug resistance through MAPK/ERK and PI3K/ growth, apoptosis, metastasis, and angiogenesis in many AKT pathways [18]. cancers. In ovarian cancer, there are 12 kallikreins overexScholler and colleagues found 77% of serum samples pression on mRNA and/or protein level. Kallikrein 4 is from advantage stage ovarian cancer exhibited elevated closely related to ovarian cancer progression, usually expression of MSLN at a specificity of 100% [28]. Synthetic detected in the late stage of serous ovarian cancer. Kallikrein data including early and late stages revealed that MSLN 7 mRNA elevates in 66.7–78.1% of malignant tumor cells expression increased in 60% of ovarian cancer sera at 98% generally. Kallikrein 11 overexpresses in 70% serum sample specificity [26]. Importantly, MSLN and serum CA125 are from ovarian cancer patients at 95% specificity. Kallikrein complementary; the combination of the two tumor biomark- 10 expression is also up-regulated obviously in some subers can improve the detected fraction of ovarian cancer com- types of ovarian carcinoma [26]. In Rosen’s study, the tissue pared with either one alone. In a recent study focusing on arrays were used to identify potential biomarkers which ovarian cancer by Donna, serum MSLN was elevated by could complement CA125. Kallikrein 10 and kallikrein 6 12% in early-stage (stage I/II) and 48% in late-stage (stage expressions were observed in the ovarian cancers lacked III/IV) ovarian cancer respectively, at 95% specificity. CA125 expression. Although several normal tissues can However, MSLN expression normalized by the glomerular express kallikreins, kallikrein 10  in 56% ovarian cancer

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serum is higher than healthy women and serum kallikrein 11 overexpresses in 70% ovarian cancer, at a specificity of 95%. Therefore, kallikrein 6, kallikrein 10, and kallikrein 11 have been identified as the potential biomarkers of ovarian cancer [21, 26]. B7-H4 B7-H4 expression is located in the membranous and cytoplasmic in serous ovarian cancer, endometriod carcinomas, and clear cell cancer. Positive signals of stained B7-H4 are observed in 60% of stage I and 90% of stage II ovarian tissues. It was confirmed that 45% early-stage patients and 67% late-stage patients demonstrated high expression of B7-H4 at 97% specificity. B7-H4 and CA125 are complementary. B7-H4 can be detected in the patients whose CA125 expression is low. When B7-H4 is combined with CA125, the sensitivity of diagnosis rises to 65% which is higher than either B7-H4 (45%) or CA125 (52%) alone. B7-H4 is considered as a common biomarker for ovarian cancer of early stage [21, 26]. Interleukins Interleukins play a role in a variety of cancers. IL-6 exhibits high expression in half of the primary ovarian cancer patients. The concentrations of IL-6 and IL-8 antibodies increase in ovarian cancer serum. Detecting levels of the antibodies of IL-8 (IgG) in stage I/II ovarian cancer patients and logistic regression analysis of the data can diagnose the ovarian cancer at early stage with 65.5% sensitivity at a specificity of 98% [26]. IL-6 and IL-8 are cytokines secreted abundantly by omental adipocytes which mediate the metastasis of ovarian tumor cells to omentum. Th17 cell, the primary source of IL-17, has been observed in cancer tissue. IL-6 can promote the differentiation of Th17 cells. To improve the diagnostic performance of early biomarkers of ovarian cancer, IL-6 and IL-8 are combined with CA125 and three other markers. The diagnostic sensitivity for early stage is 84%, at 95% specificity. A combination of serum IL-8, IL-8 antibodies, and CA125 makes early stage ovarian cancer diagnostic sensitivity rise to 88% achieved at 98% specificity [21]. Vascular Endothelial Growth Factor (VEGF) VEGF has been identified as an important factor for tumor angiogenesis in cancers. VEGF has been detected in ovarian cancer patients’ ascites, serum, and tumor tissues. Compared with normal controls or benign lesion, serum VEGF of ovarian cancer patients was elevated significantly. To predict ovarian cancer better, VEGF was included in a panel of five markers by Gorelik, and the sensitivity turned out to be 84%, at 95% specificity. In addition, VEGF exists in 81% ovarian cancer tissues lacking CA125, implying that VEGF and CA125 can be used as a combination to improve the diagnosis of ovarian cancer [21].

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MicroRNAs MicroRNA (miRNA) demonstrates different expression levels in different ovarian cancer histological types. The difference also exists between normal and tumor tissues. Up-regulated miR-21, miR-141, miR-200, miR-203, and miR-205 were detected in serous and endometrioid subtypes. Both miR-145 and miR-222 expressions are downregulated in clear cell carcinomas. In ovarian cancer serum, up-­regulated miRNA panel includes miR-21, miR-29, miR92a, miR-93, and miR-126, whereas miR-99, miR-127, and miR-­ 155 expressions are reduced. Some miRNAs have been identified as markers for ovarian cancer diagnosis, such as miR-21, miR-141, miR-203, miR-205, miR-214, and let-7f. The functions of miRNAs in cancers are complicated. For example, miR-148b promotes tumor progression; miR-200a, miR-200b, and miR-429 are associated with relapse; miR-­200 and miR-182 are connected with prognosis. Given that a large number of miRNAs are involved in complex processes of ovarian cancer, further studies are needed urgently [26]. Exosomes Exosomes are small extracellular membrane vesicles deriving from endosomal cellular. They are considered as messengers between cellular and transfer functional contents including proteins, DNAs, mRNAs, metabolites, and so on. Exosomes derived from ovarian cancer cells support tumor growth and progression through binding to stroma cells. In this process, they primarily promote angiogenesis, immunosuppression and marrow progenitors’ recruitment. It has been identified that exosomes-derived ovarian cancer contain a large number of functional proteins, such as major histocompatibility complex I, tetraspanins CD63 and CD81, lysosomal- associated membrane proteins 1 and 2, and tumor susceptibility gene 101 protein. Besides, the specific proteins and nucleic acids of exosomes can help determine the donor cell. Therefore, miRNA, proteins, and metabolites contained in exosomes may be potential biomarkers of tumors. The detection of serum exosome inclusions may contribute to improving the diagnostic accuracy of ovarian cancer and other tumor types [26, 28]. Circulating Cell-Free DNA Noninvasive cell-free DNA quantitative analysis has been used as a clinical tool for ovarian cancer. Serum cell-free DNA content elevates markedly in ovarian cancer patients compared with healthy women and patients with benign ovarian tumors. However, we still cannot confirm the correlation between plasma cell-free DNA concentration and ovarian tumor size, stage, and sites at present. Surely, the serum cell-free DNA concentration in stage III and IV ovarian cancer is higher than stage I and II ovarian cancer. Data

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from nine studies were used for meta-analysis to evaluate the diagnostic accuracy of cell-free DNA in ovarian cancer. The results revealed that the sensitivity was 70% and the specificity was 90%. The cell-free DNA diagnostic sensitivity was unsatisfactory, though the specificity was acceptable. It is suggested that serum cell-free DNA detection can be used as an adjuvant diagnosis to improve the diagnostic specificity of ovarian cancer [29].

42.2.3.2 Genetic Test Genetic Mutation The genetic factor is still one of the strongest risk factors for ovarian cancer. Studies implemented in twins suggested the effect of inherited genetics on ovarian cancer was more crucial than lifestyle and environmental factors. The famous susceptibility genes of ovarian cancer are BRCA1 and BRCA2 gene mutations. About 15% advantage serous epithelial ovarian cancer can be detected gremlin mutation in BRCA genes, and almost all of the women with BRCA1 mutation will develop the serous histologic disease. By the age of 70, the risk rates for ovarian cancer are 39–46% for the women with BRCA1 mutation and 10–27% for BRCA2 mutation carriers. In addition, some intermediate-risk susceptibility genes have been identified including RAD51C, RAD51D, FANCM, BRIP1, and DNA mismatch repair genes. The risk evaluation of DNA mismatch repair genes for ovarian cancer exhibited the risk is 4–20% in MLH1 carriers, 7.5–20% and 13.5% in MSH2 and MSH6 carriers, respectively [17]. Currently, identified susceptibility alleles are limited. There are still more alleles and mutations to be discovered. Epigenetic Regulation Besides genetic mutation, gene methylation is another inherited risk factor for ovarian cancer. A prospective study implied abnormal methylation in BRCA promoter could be the strongest risk factor for ovarian cancer except for BRCA gene mutation. Several technologies have been developed to evaluate DNA methylation in genome-wide or at a specific site. It was implied that DNA methylation changes could be served as a marker for ovarian cancer. Studies have reported that hypermethylation of CpG island gene promoter is detected in ovarian cancer frequently. OPCML, tumor suppressor gene, also displays hypermethylation in its promoter. Importantly, some specific gene promoter methylation has been matched to ovarian cancer clinical symptoms. The methylation of SOX1, SFRP, and PAX1 gene promoters in malignant ovarian cancer is higher compared with benign and borderline ovarian cancer. Other investigators reported some gene promoter hypomethylation was associated with ovarian cancer, such as Claudin-4, trag3, IGF-2, and

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MCJ.  Although the specificity of single methylated gene detection is insufficient, a panel of methylation genes detection may meet ovarian cancer diagnostic requirement. Excitingly, Seeber and Van Diest have illustrated some single methylated genes (FBXO32, IGFBP-3, and HOXA11) which play an important role in ovarian cancer prognosis are associated with disease progression, poor prognosis, and high mortality rate [28]. In addition to gene methylation, the alterations of histone modifications and its related enzymes are also observed in ovarian cancer, which suggested the anomalous modifications of histone may play a crucial role in antioncogenes silence.

42.2.4 Management In summary, many new markers are under investigation for the diagnosis of ovarian cancer. Some newly developed markers are promising, but further validations are still required. At present, serum CA125 detection combined with pelvic ultrasound is mainly used to screen ovarian cancer. However, there is no evidence-based ovarian cancer screening program for the general population. Genetic counseling and genetic detection are significative for ovarian cancer in high-risk populations. The women, with a family history of ovarian cancer, require genetic counseling and BRCA genetic detection. The National Comprehensive Cancer Network (NCCN) recommends prophylactic double-­ adnexectomy to reduce the risk of ovarian cancer after completion of pregnancy for those with identified genetic mutations. Continued efforts are needed to discover and validate effective test used to early diagnosis and general population screening of ovarian cancer.

42.2.5 Conclusion Transvaginal ultrasonography and CA125 are the traditional screening procedures for ovarian cancer. Currently, ovarian cancer early diagnosis is still the most effective measure to reduce the mortality rate. In the last two decades, many serum biomarkers have been identified and evaluated for early stage ovarian cancer diagnosis. Current researches mainly focus on new biomarkers exploitation, which can be detected and diagnose disease before clinical symptoms appear without compromise on specificity and sensitivity. There is a large heterogeneity of ovarian cancer between different patients, so no single biomarker can be used for initial screen alone. So far, at least 29 serum tumor biomarkers have been used to combine with CA125, and the combination has increased the sensitivity and specificity. Among these bio-

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arterial phase

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venous phase

plain scan

Fig. 42.7  Abdominal CT scan of the patients

markers, HE4, KLK6, KLK7, and miRNA are prominent, worthy of further study. Most biomarkers of ovarian cancer are derived from serum.

42.2.6 Typical Medical Case Clinical Background  A 83-year-old woman, 40  years menopause. 11  days ago, there was no obvious cause of lower abdomen swelling pain, no nausea, vomiting and other discomfort, and no significant relief after exhaust or defecation. Abdominal Ultrasonography  A 11.4 cm pelvic mass was found. Abdominal CT Scan  Right pelvic capsule solid space occupation, mixed with intratumoral hemorrhage, see in Fig. 42.7 Laboratory Data  CA125 27.4  U/mL, CA199 1781.4  U/ mL, CEA 5.4 ng/mL, CA50 451.9 IU/mL, CA724 14.9 IU/ mL, CA242 93.20 IU/mL. Histopathological Examination  Mucinous cyctic carcinoma of the ovary. Immunohistochemical ER (25%1+), PR (−), CEA (1+), P53 (−), EMA (1+), WT (−), CA125 (−), SP70 (3+), see in Fig. 42.8. Treatment  Resection. Results with Interpretation  The diagnosis of ovarian cancer is currently mainly based on CT or MRI monitoring and confirmed by pathological examination. The purpose of pathological examination is to confirm the diagnosis and verify the preoperative diagnosis. Second, after the diagnosis is clear, the next treatment plan and prognosis can be deter-

Fig. 42.8  Histopathological examination of SP70

mined. Staining of undefined cancer tissues can be performed with markers, such as SP70, CA125, CEA, etc., to improve diagnostic accuracy. This case was from Nanjing First Hospital, Nanjing Medical University.

42.3 Cervical Cancer Yanhong Zhai and Boyan Song

42.3.1 Overview The cervix is located at the lower part of the uterus and approximately conical, about 2.5–3 cm in length. The upper end is connected to the uterus and the lower end is connected

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Mucinous adenocarcinoma is the most common type of cervical cancer which is mainly derived from the columnar mucous cells of the cervical canal. Adenocarcinoma can be divided into three differentiated classes-well, moderately, and poorly differentiated classes. Malignant adenoma is a highly differentiated mucosal adenocarcinoma of the cervical canal. For this disease, many cancerous glands are varying in size and morphology which protrude into the deep interstitial of the human cervix. The glandular epithelial cells have no atypicality and are often accompanied by lymph node metastasis. Adenosquamous carcinoma accounts for 3–5% of cervical cancer. The cancer tissues contain adenocarcinoma and squamous cell carcinoma, which are formed by the simultaneous differentiation of cells into glandular cells and squamous cells.

Fig. 42.9  Anatomical structure of the cervix

to the vagina (Fig.  42.9). The cervix contains glands that secrete mucus to regulate sperm entry into the uterus. Cervical cancer is a severe disease that causes death in women. There are approximately 530,000 new cases worldwide each year, resulting in the death of 270,000 patients [30]. The incidence of cervical cancer ranks the sixth and the second among women in developed countries and developing countries, respectively. In spite of the high incidence rate, cervical cancer can be diagnosed in the early stage by pathological and genomic techniques, to be treated effectively [31]. Cervical cancer has no obvious clinical symptoms in the early stage, while can occur vaginal bleeding, apocenosis, and other symptoms as the disease progresses. Risk factors of cervical cancer include human papillomavirus (HPV) infection, multiple sexual partners, younger age of first delivery, multiple pregnancies and deliveries, among which the sustained HPV infection is the leading cause of malignant transformation and increased risk of cancer [32]. In worldwide, 17.5% of women without cervical lesions are infected with HPV.

42.3.1.1 Staging and Classification Cervical cancer is mainly divided into squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma. According to the degree of histological differentiation, squamous cell carcinoma can be divided into three grades, where grade I refers to a well-differentiated squamous cell carcinoma; grade II is a moderately-differentiated squamous cell carcinoma; grade III is a poorly differentiated squamous cell carcinoma. Adenocarcinoma makes up 15–20% of the total number of cervical cancer and is mainly divided into mucinous adenocarcinoma and malignant adenoma [31].

42.3.1.2 HPV Introduction Papillomavirus DNA is about 8 kb in length, double-spiral, and surrounded by histones. It mainly encodes six early genes and two late genes [33]. Early genes include E1, E2, E4, E5, E6, and E7. Late genes include L1 and L2. Currently, more than 130 HPV genotypes have been identified and are divided into mucosal and skin types. Among the mucosal types, 40 subtypes are transmitted through sexual contact and can be further divided into high-risk and low-risk types. It is reported that almost all types of cervical cancer are associated with HPV infection, of which 70% of cervical cancers are associated with HPV-16 and HPV-18 infections [34]. The HPV virus is mainly transmitted through sexual contact while skin damage can also result in HPV infection. For most women under the age of 30 years old, HPV is cleared within 8 weeks and viral load is reduced to undetectable levels within 2  years [35]. In postmenopausal women, HPV infection rates range from 14 to 38%. Since the persistent infection of HPV, a high risk of cervical cancer, the frequency of screening should be related to the age of the patients. Despite of high-risk HPV infection, other factors are important for the malignant transformation of cells. For example, immunosuppression promoting persistent infection of HPV, and coinfection of HIV with HPV leading to malignant transformation of cells. Studies have found that women infected with HIV were 2–5 times more likely to be infected with HPV, and are 3–5 times more likely to develop CIN or cervical cancer [36]. Genetic susceptibility also plays an important role in the infection of HPV. Many studies have focused on the role of HLA in the persistent infection of HPV.  In a HLA-wide analysis, the DQ/DR gene was involved in the development of cervical cancer, while HLA-DRB1*13 played a role in the prevention from the development of cervical cancer [37, 38].

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HPV Infection Procedure in the Cervix HPV usually infects squamous epithelial basal cells. The E6 and E7 gene encoding products of the virus bind to and inhibit the function of intracellular tumor suppressor genes (such as P53, RB, etc.). These tumor suppressor genes are important for cell cycle regulation and apoptosis, inhibition of which will lead to malignant transformation of cells [39]. Infections of HPV often occur at damaged sites of endothelial cells, especially in the squamocolumnar junction. Infections are usually located in cells surrounding the wound, which last for a long time and are less noticeable [40]. HPV is unlikely to cause cell death or systemic viremia, and its proliferation is influenced by the differentiation of epithelial cells [41]. The HPV is mediated through the intracellular vesicles and L1 into the host cells. HPV-infected epithelial cells bind to heparan sulfate proteoglycan (HSPG) in the basement membrane through L1, which changes the shape of the viral capsid and exposes the amino terminus of L2, and thus internalizes the virus into the cells.

Aberrantly expression of E6 and E7 genes in high-risk HPV can immortalize keratinocytes and make genomes unstable. Oncogenesis is mainly due to unstable genomes and disorder of cell proliferation regulation caused by virion proto-oncogene expression.

Ingestion of the Virus and Delivery of the Genome to Nucleus After the virus enters into the cell, L2 leaded intracellular virus to enter into the nucleus. After L1 is degraded, L2 mediates viral genome to enter into the trans-Golgi apparatus [42, 43]. The entry of the L2 genomic complex into the nucleus depends on cell cycle progression and the cell membrane rupture phase of cell mitosis [44]. Then, L2 genomic complex binds to the medullary nucleus of the promyelocytes, and once it enters into the nucleus, the early replication of the virus starts [45, 46].

42.3.2 Clinical Appearance

Virus Transcription and Life Cycle In the early phase, E1 and E2 genes are involved in DNA replication in HPV, forming 50–100 copies per cell [46]. In the productive phase, the HPV has a unique spatiotemporal regulation mechanism with the differentiation of squamous epithelial keratinocytes. HPV switches to a high copy (>103per cell) mode as basal cells differentiate into the epidermis and detach from the basement membrane. Replication and expression of HPV virus assembly-associated genes (such as capsid-associated proteins) are only expressed in terminally differentiated epidermal cells, while the virus-­ loaded epidermal cells are detached. In order to utilize the DNA replication system of the host cells, HPV brings the host keratinocytes into the S phase, during which the protein E5, E5 stimulates epidermal growth factor receptor (EGFR) and causes cells to exit G0–G1 phase, E6 binds to and activates the proteasome, degrades P53 to promote cell survival and cell dedifferentiation, and up-regulates the expression of MYC and thus promotes cell survival, and E7 binds to a variety of proteins, including Rb, etc. to cross the restriction point [33].

Self-limiting and Persistent Infection of the Virus Most HPV infections are cleared in a short period time without causing cancer. It is still controversial whether the virus is eliminated or is kept at a low level. One hypothesis is that HPV infects cells in the vicinity of the basement membrane to continuously release HPV.  Another hypothesis is that HPV primarily infects keratinocytes with a higher degree of differentiation. Similarly, the location of HPV infection is also very important. Recent studies have shown that CK7 + p63-cells are located in the squamocolumnar part and provide a proper environment for cervical cancer development [47].

Patients suffering from cervical cancer usually feature abnormal leucorrhea, vaginal discharge, vaginal bleeding, foreign body in cervix uteri, and others. In the advanced stage of cancer, there are different secondary symptoms due to the extent of lesions, such as frequent urination, urgent urination, constipation, swelling of the lower limbs, etc. When the mass is compressed or involves the ureter, ureteral obstruction, hydronephrosis, and uremia can be induced, even resulting in symptoms of systemic failure such as anemia and cachexia in the advanced stage. Colposcopy showed no obvious lesions in microinvasive carcinoma. Polypoid and cauliflower-like neoplasms were spotted in exogenous cervical cancer. The endogenous type was characterized by hypertrophy of the cervix, hard mass, and swelling of the cervix. Tissue necrosis and detachment were found in the advanced stage of cancer, forming ulcers or cavities. When the vaginal wall was implicated, growth of neoplasms or hardening of the vaginal wall can be seen. In liquid-based cytology, squamous epithelial lesions or glandular epithelial abnormalities were detected.

42.3.3 Laboratory Diagnosis 42.3.3.1 Papanicolaou Test (Pap Test) The Pap test has been used to screen cervical cancer for more than 60 years. In the Pap test, sampled cells are smeared on a glass slide, followed by fixation with 95% alcohol, pasteurization, and observation morphology of cells under a microscope. Based on the Pap smear method, a Thin-Cytologic Test (TCT) has been developed. Although this method is

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simple and cheap, the interpretation of the results is subjective and relies on experienced physicians, which limits its sensitivity and accuracy [41].

42.3.3.2 Molecular Marker The sustained HPV infection is important for the occurrence and development of cervical cancer. Detection of HPV DNA is more sensitive than cytology and can be used to classify infected HPV, which includes fluorescent PCR, gene chip technology, and so on. Also besides, there are detection systems for HPV mRNA. Detecting E6 and E7 RNA can identify 14 high-risk HPVs [48]. Squamous Cell Carcinoma Antigen (SCCA) SCCA has been widely used in the screening and early diagnosis of cervical cancer. The serum SCC is significantly elevated in patients with cervical squamous cell carcinoma [49]. The level of SCCA in the serum of patients with cervical cancer is closely related to tumor stage, tumor size, depth of interstitial infiltration, and lymph node metastasis. SCCA plays an important role in the monitoring of disease during and after treatment of cervical cancer. If the SCCA value does not decrease or increase instead after the treatment, it means a poor curative effect. In addition, the pretreatment SCCA value can also serve as an important reference for the need for adjuvant therapy after surgery. The decrease of SCCA value after treatment is closely related to chemotherapy response, where a significant decrease of SCCA means a good prognosis. In most patients with recurrent cervical cancer, the first symptom is an increase in SCCA.  SCCA is an important marker for cervical cancer diagnosis and disease monitoring. Serum Fragments of Cytokeratin (CYFRA) Cytokeratin is a major component of the epidermal cell cytoskeleton. CYFRA is the content of soluble keratin 19 fragments in serum, which previously was used as a tumor marker for lung cancer [50, 51]. It was reported that 42–52% of patients with squamous cervical cancer showed a CYFRA increase in blood [52]. Compared with SCCA, CYFRA is more popular in assessing the prognosis and therapeutic effect for cervical cancer.

42.3.4 Management According to the patient’s clinical-stage, age, medical level, and equipment conditions, comprehensive consideration should be given to formulating individualized treatment schemes, including surgery, radiation therapy, chemotherapy, and others. Surgical treatment can preserve the ovarian and vaginal function of patients, mainly for early cervical cancer patients. Radiation therapy is mostly adapted for early to middle and advanced-stage patients, mainly including intracavity irradiation and external beam radiation.

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Chemotherapy is mainly applied in advanced-stage patients or those with recurrent metastasis. Chemotherapy drugs mainly include cisplatin, paclitaxel, etc.

42.3.5 Conclusion Cervical cancer is a major disease that threatens women’s health. Tumor markers for cervical cancer play a significant role in the diagnosis, prognosis, and detection of this disease. As the advancement of genomic and proteomic technology, more and more tumor markers for cervical cancer will be identified, which will significantly improve the diagnosis and therapeutic effects for cervical cancer.

42.3.6 Typical Medical Case Clinical Background  The patient was 38 years old, G2P1. She went to outpatient for over 2 months due to pruritus vulvae. The cervical diameter was 3 cm, smooth, showing contact bleeding. The corpus uteri were in a horizontal position, normal size, with no obvious tenderness. Colposcopy results showed that the cervical canal may be related to the low-­level glandular intraepithelial lesion (LG-CGIN) combined with a low-level squamous intraepithelial lesion (LSIL-­ CIN1). Higher risk of lesions may be caused, and it is recommended to perform diagnostic cervical conization. The patient refused surgical treatment because she had fertility willingness. The patient was informed for reexamination in 3–6 months. One year later, the patient returned to the hospital of reexamination due to repeated abnormal vaginal discharge. After examination, the cervix had less erosion, and a neoplasm with a size of 0.5 cm × 0.5 cm was spotted in the cervical canal. Colposcopy  When the patient was treated for the first time, colposcopy showed low-level squamous intraepithelial lesions of the cervix, developing chronic cervicitis; low-level glandular intraepithelial lesion (LG-CGIN) combined with a low-level squamous intraepithelial lesion (LSIL-CIN1) was considered for the cervical canal, with P16 (+), and Ki67 8%. When the patient was treated for the second time, cervical biopsy under colposcopy + cervical canal neoplasms excision + cervical canal curettage was performed, and tissues were sent for pathology. Pathological report of cervical adenocarcinoma suggested the high possibility of common type endometrial adenocarcinoma of the cervix; a small number of adenocarcinoma cell nests were seen in the neoplasms of the cervical canal; a small amount of dissociative adenocarcinoma cell tissues were occasionally spotted in multiple blood clots within the cervical canal. Laboratory values: The results of the TCT examination at the first treatment showed low-level squamous intraepithelial lesions. HPV

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classification: HPV16 positive, other 12 high-risk HPV positive. The results of TCT examination at the second treatment showed atypical glandular cells (AGC), with HPV16 and the other 12 high-risk HPV positive. HPV-DNA: 161.38 pg/mL. Results with Interpretation Guideline  Existing studies have shown that persistent infection of HPV-16 and HPV-18 can lead to cervical adenocarcinoma. In this case, persistent infection of HPV-16 caused continuous deterioration of the patient’s lesions, eventually resulting in cervical adenocarcinoma combined with high-level squamous epithelial lesions. This case suggested that detection of HPV infection is conducive to alerting cervical cancer and further risk of malignancy. Final Diagnosis  The patient received total laparoscopic hysterectomy with bilateral adnexectomy, plus pelvic cavity, and aorta abdominal lymphadenectomy. The pathological report suggested cervical adenocarcinoma combined with high-level squamous intraepithelial lesions (HSIL/CIN2). This case was from Jiangsu Women and Children Health Hospital.

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768 36. Ahdieh L, Klein RS, Burk R, et al. Prevalence, incidence, and type-­ specific persistence of human papillomavirus in human immunodeficiency virus (HIV)-positive and HIV-negative women. J Infect Dis. 2001;184:682–90. 37. Zoodsma M, Nolte IM, Schipper M, et  al. Analysis of the entire HLA region in susceptibility for cervical cancer: a comprehensive study. J Med Genet. 2005;42:e49. 38. Bernal-Silva S, Granados J, Gorodezky C, et  al. HLA-DRB1 Class II antigen level alleles are associated with persistent HPV infection in Mexican women; a pilot study. Infect Agent Cancer. 2013;8:31. 39. zur Hausen H. Papillomaviruses causing cancer: evasion from host-­ cell control in early events in carcinogenesis. J Natl Cancer Inst. 2000;92:690–8. 40. Weinberg CR. HPV screening for cervical cancer in rural India. N Engl J Med. 2009;361:305–6. author reply 306 41. Soost HJ, Lange HJ, Lehmacher W, et al. The validation of cervical cytology. Sensitivity, specificity and predictive values. Acta Cytol. 1991;35:8–14. 42. Day PM, Thompson CD, Schowalter RM, et al. Identification of a role for the trans-Golgi network in human papillomavirus 16 pseudovirus infection. J Virol. 2013;87:3862–70. 4 3. Lipovsky A, Popa A, Pimienta G, et  al. Genome-wide siRNA screen identifies the retromer as a cellular entry factor for human papillomavirus. Proc Natl Acad Sci U S A. 2013;110:7452–7. 44. Pyeon D, Pearce SM, Lank SM, et al. Establishment of human papillomavirus infection requires cell cycle progression. PLoS Pathog. 2009;5:e1000318.

J. Tang et al. 45. Day PM, Baker CC, Lowy DR, et  al. Establishment of papillomavirus infection is enhanced by promyelocytic leukemia protein (PML) expression. Proc Natl Acad Sci U S A. 2004;101:14252–7. 46. Bienkowska-Haba M, Luszczek W, Keiffer TR, et  al. Incoming human papillomavirus 16 genome is lost in PML protein-deficient HaCaT keratinocytes. Cell Microbiol. 2017;19:27860076. 47. Yang EJ, Quick MC, Hanamornroongruang S, et al. Microanatomy of the cervical and anorectal squamocolumnar junctions: a proposed model for anatomical differences in HPV-related cancer risk. Mod Pathol. 2015;28:994–1000. 48. Cubie HA, Canham M, Moore C, et  al. Evaluation of commercial HPV assays in the context of post-treatment followup: Scottish Test of Cure Study (STOCS-H). J Clin Pathol. 2014;67:458–63. 49. Lekskul N, Charakorn C, Lertkhachonsuk AA, et  al. The level of squamous cell carcinoma antigen and lymph node metastasis in  locally advanced cervical cancer. Asian Pac J Cancer Prev. 2015;16:4719–22. 50. Pujol JL, Grenier J, Daures JP, et al. Serum fragment of cytokeratin subunit 19 measured by CYFRA 21-1 immunoradiometric assay as a marker of lung cancer. Cancer Res. 1993;53:61–6. 51. Bonfrer JM, Gaarenstroom KN, Kenter GG, et al. Prognostic significance of serum fragments of cytokeratin 19 measured by Cyfra 21-1 in cervical cancer. Gynecol Oncol. 1994;55:371–5. 52. Suzuki Y, Nakano T, Ohno T, et al. Serum CYFRA 21-1 in cervical cancer patients treated with radiation therapy. J Cancer Res Clin Oncol. 2000;126:332–6.

Prenatal Diagnosis and Preimplantation Genetic Diagnosis

43

Chengcheng Liu, Xiaoting Lou, Jianxin Lyu, Jian Wang, and Yufei Xu

Nowadays, millions of individuals suffer from dominant or recessive genetic mutations that cause highly severe or life-­ threatening phenotypes in the world. In total, the Online Mendelian Inheritance in Man (OMIM) database currently reports more than 4600 phenotypes with a genetic cause. In fact, every individual carries alleles that in a homozygous state could cause recessive disorders. Besides, approximately 0.2% of the human population are carriers of a balanced translocation that often causes infertility or recurrent miscarriages (owing to embryonically lethal segmental aneuploidies in the conceptuses), or severe birth defects in offspring. To eschew the transmission of pathogenic genetic variants and to enable early discovery of genetic disorders, prenatal genetic testing (PGT) is introduced. Genetic testing can potentially provide an accurate diagnosis to a fetus with developmental anomalies detected by ultrasonography and enable its parents to make an informed decision about the pregnancy. For couples who are known carriers of mutant alleles, preimplantation genetic diagnosis (PGD) enables the detection of genetic disorders in embryos that have been ferC. Liu (*) Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China X. Lou Key Laboratory of Laboratory Medicine, Ministry of Education of China, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China J. Lyu (*) Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China e-mail: [email protected] J. Wang (*) · Y. Xu Department of Molecular Genetic Diagnostics, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China e-mail: [email protected]

tilized in vitro, thereby preventing the transmission of these disorders to offspring. The Timeline map below provides an overview of the evolution of both prenatal and preimplantation genetic testing (Fig. 43.1). In 1966, karyotyping of cultured cells obtained from amniotic fluid sampling, the first human prenatal genetic test, was performed providing a chromosomal view of the fetus. In the following year, the first prenatal diagnosis of a chromosomal abnormality was achieved. Later, the introduction of chromosome-banding techniques in the 1970s led to an increase in resolution and enabled the detection of segmental chromosomal imbalances. Over the next 40  years, karyotyping became gold standard for prenatal diagnosis for the genome-wide detection of genomic rearrangements, despite its intrinsic limitations (including invasive procedure to obtain a tissue sample, culturing of cells, visual screening for numerical or structural chromosome anomalies, and limited resolution). Hence, there has been an unceasing pursuit for improving DNA-based molecular genetics techniques. From 1990s, as the development of molecular biology, a series of technologies, such as fluorescence in situ hybridization (FISH), quantitative fluorescence PCR (QF-PCR), multiplex ligation-dependent probe amplification (MLPA) as well as array comparative genomic hybridization (aCGH) were introduced subsequently (Table  43.1). Figure  43.2 shows the application of cell-free fetal DNA in aneuploidy screening in prenatal diagnosis, which is extensively used in clinical practice at present. Preimplantation genetic diagnosis (PGD) was introduced at the beginning of the 1990s as an alternative to prenatal diagnosis, to avoid termination of pregnancy in couples with a high risk for offspring affected by a sexlinked genetic disease. At that time, embryos obtained in vitro were tested to ascertain their sex, and only female embryos were transferred to uteri. Since then, techniques for genetic analysis at the single-cell level, involving assessment of first and second polar bodies from oocytes or blastomeres from cleavage-­stage embryos, have evolved.

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_43

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Fig. 43.1  Timeline of prenatal and preimplantation genetic diagnostics. Key milestones in the implementation of genetic tests for invasive (gray) and noninvasive (blue) prenatal and preimplantation diagnosis. aCHG array comparative genomic hybridization; ADO allele drop out; cfDNA

cell-free fetal DNA; FISH fluorescence in situ hybridization; IVF in vitro fertilization; MLPA multiplex ligation-dependent probe amplification; NGS next-generation sequencing; PGD pre-implantation genetic diagnosis; QF-PCR quantitative fluorescence PCR; WES whole-exome sequencing

Table 43.1  Prenatal diagnostic tests Tests Biochemical markers NIPT Karyotype Molecular DNA testing FISH CMA NGS

Invasive N N Y Y

Specimen Maternal serum cffDNA, fNRBCs AF, CV AF, CV

Item AFP, uE3, β-HCG, Inhibin A MPSS, TMPS, SNP-based MPS NA RFLP, QF-PCR, MLPA

Utilities ONTDs, trisomy 18, trisomy 21 Aneuploidies, deletions, duplications Aneuploidies, chromosome abnormalities Mutations and aneuploidy

Y Y Y

AF, CV AF, CV AF, CV

NA aCGH, SNP microarray WGS, WES

Aneuploidies, deletions, or duplications Uniparental disomy, aneuploidies, DNA copy number Discovery of novel mutations

Note: + possible; − not possible; +/− possible but requires specific work-up; NA not available; AF amniotic fluid; CV chorionic villus; CVS chorionic villus sampling; AFP alpha-fetoprotein; uE3 unconjugated estriol; β-HCG β-human chorionic gonadotropin; DIA dimeric inhibin A; ONTDs open neural tube defects; NIPT non-invasive prenatal testing; cffDNA cell-free fetal DNA; FISH fluorescence in situ hybridization; fNRBCs fetal nucleated red blood cells; MPS massively parallel sequencing; MPSS massively parallel shotgun sequencing; TMPS targeted massively parallel sequencing; RFLP restriction fragment length polymorphisms; QF-PCR quantitative fluorescent PCR; MLPA multiplex ligation-dependent probe amplification; FISH fluorescence in situ hybridization; CMA chromosomal microarray analysis; aCGH array comparative genomic hybridization; SNP Microarray single nucleotide polymorphism microarray; NGS next-generation sequencing; WGS whole-genome sequencing; WES whole-­exome sequencing

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Fig. 43.2  Cell-free fetal DNA aneuploidy screening methods. Two major strategies are widely implemented in routine cell-free fetal DNA (cfDNA) aneuploidy testing; random and targeted sequencing. Chr chromosome; NGS nextgeneration sequencing

FISH has been introduced for the analysis of chromosomes and PCR for the analysis of genes in cases of monogenic diseases (Fig. 43.3).

43.1 Noninvasive Prenatal Testing Chengcheng Liu

43.1.1 Overview Genomic abnormalities are a leading cause of birth defects and pregnancy complications, including in utero growth retardation and risk of miscarriage [1]. In the past decade, it has been observed that the development of technologies have

revolutionized prenatal genetic testing; that is, genetic testing from conception until birth. Newly developed technologies, such as Genome-wide single-cell arrays and high throughput sequencing analyses, are significantly improving our ability to detect embryonic and fetal genetic abnormity, and have substantially improved embryo selection for in vitro fertilization (IVF). Moreover, both invasive and noninvasive mutation scanning of the genome are helping to identify the genetic causes of prenatal developmental disorders. These advances are changing clinical practice and pose novel challenges for genetic counseling and prenatal care [2]. Traditional invasive procedures to detect such genomic abnormalities could pose a relative risk to both mother and unborn fetus potentially. The discovery of free fetal DNA (ffDNA) in maternal circulation marked the beginning of the Noninvasive prenatal testing (NIPT) era, and allowed the

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a

PB Female pronucleus Male pronucleus 6- or 8-cell embryo

Zygote

Blastocyst

Blastomeres

PB1 and PB2

Trophectoderm cells

PGD

PGS

(monogenic)

(chromosomal)

b Multiplex qPCR

NGS

Read counts

Probe

aCGH

Fluorescence

FISH

Target Cycles

Chr1

c

Chr2

Chr3

Aneuploidy or mutation Embryo selection No aneuploidy or mutation

Select for transfer

Fig. 43.3  Preimplantation genetic diagnosis and screening. (a) The developmental stages of an in vitro fertilization (IVF)-derived embryo and the type of cells used for pre-implantation genetic diagnosis and

screening. (b) The methods routinely applied in diagnostic services. (c) Embryos are prioritized for transfer to the uterus. PB polar body

development of the leading noninvasive prenatal tests [3]. NIPT is a method that determines the genomic status of a fetus in utero by analyzing circulating fetal DNA in maternal plasma or serum. Recently, a series of clinical studies have shown that human cell-free detection of trisomies 21, 18, and 13 in singleton pregnant subjects has resulted in fewer false-­ positive and higher positive prediction rates than using the previous standard screening procedures. Furthermore, coupled with the use of more advanced and refined molecular technologies, NIPT is becoming a promising clinical tool for the screening and diagnosis of subchromosomal abnormalities and monogenic diseases (e.g., cystic fibrosis, sickle cell disease, and β-thalassemia) [1]. In this section, we will dis-

cuss here the principle, methods of detection as well as applications of NIPT.

43.1.2 Laboratory Examination 43.1.2.1 C  ell-Free Fetus DNA(cffDNA) and Its Detection in Maternal Plasma Cell-free DNA (cfDNA) is composed of small fragments of extracellular DNA that circulate freely in the maternal plasma. The cfDNA in gravida is consist of two parts: the majority is maternal in origin and the rest, only accounting for around 10% on average, is from the fetus (cffDNA) that

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

is released from the placenta at around 4 weeks’ gestation. The cffDNA fragments which represent the entire fetal genome are pregnancy specific as they are rapidly eliminated from the maternal circulation after delivery. Therefore, analysis of cfDNA offers significant potential for use in prenatal diagnosis. However, analysis is less reliable when the proportion of cffDNA in the maternal circulation is below 4%. The fetal fraction increases with advancing gestation and, while this often reaches levels sufficient for testing for some applications by around 7 weeks’ gestation, in the majority of women. In order to ensure high accuracy, many tests are not performed until later gestations [4]. NIPT is generally defined as the screening, testing, and diagnosis of fetal chromosomal or genetic conditions by analysis of cell-free fetal nucleic acids in maternal plasma or serum. In 1997, the presence of cffDNA was reported by Lo and colleagues firstly. Conventionally, the detection of fetal chromosomal or genetic conditions requires sampling of fetal or placental tissues by amniocentesis or chorionic villus sampling, which are invasive procedures with a small but definite risk of fetal loss and maternal morbidity. As a result, noninvasive means of prenatal testing are demanding in the past few decades. Before the discovery of cffDNA in maternal plasma, circulating fetal cells had been the primary focus of NIPT research, but the rarity of these cells and the difficulty in isolating them limited the development of this approach. Thus, the research focus switched to the analysis of circulating cell-free fetal nucleic acids. cffDNA is a minor cell-free DNA species admixing with cell-free maternal DNA that is dominant in circulating cfDNA. On average, 10–20% of cfDNA in maternal plasma is originally from fetus. This percentage varies among individuals and generally increases with gestational age until delivery, after which fetal DNA is rapidly cleared from the maternal plasma within hours. cffDNA, likely from apoptotic trophoblasts in placenta, can be detected as early as 5 weeks of gestation, whereas cell-free maternal DNA has a predominant hematopoietic origin. Unlike intact genomic DNA, cfDNA in maternal plasma exists in fragments, and cffDNA is generally shorter than cell-free maternal DNA. The peak size of cell-­free maternal DNA is 166 base pairs (bp), whereas for fetal DNA it is 143 bp. The distribution of fetal and maternal DNA across the entire genome is relatively even [5]. Among all molecular techniques used for characterization of cell-free nucleic acids, massively parallel sequencing (MPS) has significantly accelerated the evolution of this field since it emerged, as it enables millions to billions of nucleic acid molecules in plasm to be sequenced in a high-­ throughput and comprehensive manner at single nucleotide resolution. An important parameter of data generated by MPS is the sequencing depth, which is the number of reads that a certain base in the reference genome is sequenced.

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Different diagnostic purposes require different sequencing depths. Deeper sequencing allows one to discover less common nucleic acid sequence variants, for instance, to distinguish a paternally inherited pathogenic cytosine (C) variant in fetal DNA from the majority of sequence reads from maternal DNA that carries the normal adenine (A) allele, which could be missed at low sequencing depth because of random error. In contrast, the sequencing depth required for chromosomal aneuploidy detection is lower. Sequencing coverage, another important parameter, is sometimes used interchangeably with depth, but it has also been specifically defined as the percentage of target bases that are sequenced a given number of times (the “breadth” of sequencing). In recent years, the ability to deep sequencing cfDNA in the maternal plasma has enabled various newer developments in the field of NIPT [5]. At present, two primary approaches used for MPS-based NIPT are massively parallel shotgun sequencing (MPSS) and targeted massively parallel sequencing (TMPS). In both methods, sequence reads can be used to detect a slight excess of fetal genomic material coming from the chromosome of interest. Nevertheless, MPSS produces a large number of sequence reads from all chromosomes while TMPS generates a larger proportion of reads from the chromosomes of interest. Quantifying the fetal DNA fraction is important so as to ensure that the threshold of detection is reached for these testing methods, achieving high sensitivity and specificity, which are required for a diagnostically applicable clinical test. There are various methods for quantifying cffDNA, the first of which involves the analysis of size distribution of circulating DNA. Compared with cfDNA original from gravida in maternal plasma, these cffDNA fragments are relatively short, 99% being shorter than 300 bp. A ratio of short to long read-lengths generated by whole-genome sequencing can accurately predict the fetal DNA fraction relative to maternal cfDNA [1].

43.1.2.2 Fetal Nucleated Red Blood Cells cffDNA-based NIPT is widely used in the clinical practice for screening for fetuses with trisomy 21, 18, and 13 in high-­ risk gravidas presents no risk of miscarriage and provides an economical, convenient, and effective method compared to other invasive technologies. However, the limitations of cffDNA-based NIPT remain: (1) it cannot eliminate chromosomal anomalies like mosaicism, duplication, and deletion; (2) limited data are currently available on the use of NIPT in twins and multiple pregnancies; (3) cell-free DNA cannot be used to distinguish specific abnormalities such as Robertsonian translocation and high-level mosaicism; (4) samples from gravidas with low-level mosaicism or solid tumor as well as a high body mass index (BMI) or early gestational age will result in variations of circulating cffDNA impacting prenatal testing results [6].

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Fig. 43.4  Schematic showing NIPT using fetal nucleated red blood cells (fNRBCs)

Due to inherent drawbacks of cffDNA in NIPTs, attention is attracted to circulating fetus-derived cells in the maternal bloodstream. Till now, four types of fetal nucleated cells have been reported: trophoblasts, fetal nucleated red blood cells (fNRBCs), hematopoietic progenitor cells, and lymphocytes [7–9]. Among these, fNRBCs are the preferred choice for NIPTs owing to their unique characteristics. First, fNRBCs have intact nuclei containing the total fetal genome for prenatal analysis. And second, fNRBCs have distinct cell markers, such as epsilon hemoglobin transferrin receptor (CD71), thrombospondin receptor (CD36), GPA, and antibody 4B8/4B9, enabling isolation of these rare cells from large volumes of maternal blood [10– 15]. Recently, a highly sensitive and rapid isolation method has been developed for NIPT using fNRBC-specific antibody (anti-CD147) and a capture-releasing material that is composed of biotin-doped polypyrrole nanoparticles [6, 16]. Figure  43.4 describes the principle of NIPT using fNRBCs.

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genomics-based noninvasive prenatal testing (gNIPT) to analyze the fetal genome [17]. Table 43.2 shows the characteristics of commonly reported chromosomal aneuploidies in literature. Two updated meta-analysis showed that MPSS and TMPS perform similarly in terms of clinical sensitivity and specificity for the detection of fetal T21, T18, and T13. However, as to the replacement of invasive tests, the performance of gNIPT observed is not sufficient to replace current invasive diagnostic tests [17, 18]. In addition, the fNRBCs presented exciting performances in the diagnosis of chromosomal aneuploidy. Although NIPT presents high sensitivity and accuracy in aneuploidy diagnosis, the existence of infrequent confined placental mosaicism (CPM) may cause a discrepancy between NIPT and Amniotic Chromosomal Test, probably false-positive results [19]. Hayata K et al. experienced a case of advanced maternal age in which a fetus was found to be positive for trisomy 18 at re-examination following indeterminate NIPT. Later, amniotic fluid chromosomal test revealed a normal karyotype, and CPM was observed in a SNP microarray analysis of the placenta. Finally, the child was born with no defects or complications [20].

43.1.3.2 Subchromosomal Abnormalities As the methods for detecting aneuploidy through NIPT becomes more refined and more reliable, the possibility to detect abnormalities beyond the chromosome copy number becomes true. Subchromosomal abnormalities (mainly including deletions or duplications) in the genome have far-­ reaching influences on the developing fetus. Several studies have shown the potential of extending NIPT to detect fetal deletion/duplication syndromes from maternal plasma. However, the detection of subchromosomal abnormalities requires deep sequencing, which is costly and time-­ consuming, and thus, is limited to only a handful of patients 43.1.3 Clinical Application [21, 22]. Recently, a work by Yin and colleagues showed that NIPT could reliably detect subchromosomal deletions/dupli43.1.3.1 Chromosomal Aneuploidy cations in women carrying high-risk fetuses by using less Aneuploidies are chromosomal abnormalities characterized sequencing depth at a lower cost. The study, which used a by a different (additional or missing) number of chromo- semiconductor sequencing platform (SSP), demonstrated somes than that normally present in humans (23 pairs). These that increased concentration of cfDNA and increased chromosomal anomalies are among the most common types sequencing depth could improve the detection of these of genetic disorders and they cause significant morbidity or abnormalities [23]. Besides, cell-based NIPT method also death in both childhood and adulthood. Due to the serious has the capacity to reliably diagnose fetal subchromosomal health consequences of various aneuploidies and given their abnormalities [24]. incurable nature, prenatal screening is an option essential for Although the accuracy of this method was less than that pregnant women. Traditionally, pregnant women, who were of chromosomal microarray analysis, especially for delefound to be at high risk of fetal aneuploidy after the prenatal tions/duplications smaller than 5  Mb, it may offer the screening, were suggested to undergo an invasive diagnostic ­advantage of unbiased testing, and is substantially more test (e.g., amniocentesis) which could cause a procedure-­ accurate than current blood-based biomarker screening related risk of miscarriage. The discovery of circulating tests (e.g., AFP, HCG, and estriol) for aneuploidy. At a cfDNA in maternal blood has enabled the development of sequencing depth of 3.5 M reads, this report detected 75%

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Table 43.2  Characteristics of reported chromosomal aneuploidies Other Aneuploidies name(s) Trisomy 21 Down syndrome

Prevalence 1 in 800 newborns

Clinical features Intellectual disability (mild to moderate), heart defect, gastroesophageal reflux, celiac disease, hypothyroidism, a characteristic facial appearance, hypotonia, cognitive delays, delayed development (speech, language, motor), mood disorders, autism spectrum disorder Intellectual disability (severe), intrauterine growth retardation, low birth weight, heart defects and abnormalities of other organs, abnormal head, small jaw and mouth, clenched fists with overlapping fingers

Trisomy 18

Edwards syndrome

1 in 5000 newborns

Trisomy 13

Patau syndrome

1 in 16,000 newborns

Intellectual disability (severe), heart defects, brain or spinal cord abnormalities, microphthalmia, extra fingers or toes, cleft lip with or without cleft palate, hypotonia

45,X

Turner syndrome

1 in 2500 newborn girls

47,XXX

Trisomy X

1 in 1000 newborn girls

47,XXY

Klinefelter syndrome

1 in 650 newborn boys

47,XYY

Jacob’s syndrome

1 in 1000 newborn boys

48,XXXX

Tetrasomy X

NA

48,XXXY

XXXY syndrome

1 in 17,000 to 50,000 newborn boys

48,XXYY

XXYY syndrome

1 in 18,000 to 40,000 males

Female, infertile, short stature, ovarian hypofunction, webbed neck, low hairline, lymphedema of hands and feet, skeletal abnormalities, kidney problems, heart defect Female, normal sexual development,able to conceive children, learning disabilities, delayed development (speech, language, motor), hypotonia, behavioral and emotional difficulties, seizures, kidney abnormalities Male, infertile, small testes, delayed or incomplete puberty, gynecomastia, decreased muscle mass and bone density, cryptorchidism, hypospadias, micropenis, tall stature, delayed development (speech, language, motor), mood disorders, autism spectrum disorder, metabolic syndrome Male, normal sexual development, able to father children, learning disabilities, delayed development (speech, language, motor), hypotonia, mood disorders, increased belly fat, macrocephaly, macrodontia, pes planus, clinodactyly, hypertelorism, scoliosis Female, mental retardation, small head, wide-set eyes, expressionless face, epicanthic folds, absent menstruation, weak eye muscles, webbed neck, mild to severe mental retardation, growth restriction, hypotonia, ovarian failure Male, infertility, intellectual disability (mild), tall stature, learning difficulties, delayed development (speech, language, motor), mood disorders, hypotonia, hyperextensibility, elbow abnormalities, fifth finger clinodactyly, pes planus, ocular hypertelorism, upslanting palpebral fissures, epicanthal folds, short penis, cryptorchidism, small testes, gynecomastia Male, infertility, tremor, dental problems (delayed teeth appearance, thin tooth enamel, crowded and/or misaligned teeth, multiple cavities), peripheral vascular disease, deep vein thrombosis, allergies, asthma, type 2 diabetes, seizures, and congenital heart defects. Other clinical features are likely similar to that in 48, XXXY

Prognosis Mean and median life expectancies are estimated to be 51 and 58 years old

Many individuals with trisomy 18 die before birth or within their first month. Only 5 to 10 percent of children with this condition live past their first year Many infants with trisomy 13 die within their first days or weeks of life. Only 5 to 10 percent of children with this condition live past their first year Mortality in 45,X women is threefold higher than in the general population with an average life span of 69 years Mortality significantly increased with a median survival age of 70.9 years compared to 81.7 years for euploid females Life expectancy is slightly shorter (approximately 2 years) than euploid men

Mortality increased with a reduction of life span of 10.3 years compared to euploid men

NA

NA

The standardized incidence ratio and standardized mortality ratio for non-Hodgkin lymphoma in patients with a 48,XXYY constitution was 36.7 (95% CI = 4.4 to 132.5) and 32.6 (95% CI = 6.7 to 95.4).

NA not available

more abnormal karyotypes compared to aneuploidy alone. In the long term, sensitivity could be further improved with greater sequencing depth. Thus, as no specific prenatal screening currently exists for subchromosomal abnormalities routinely, it could represent just a powerful extension of NIPT.

43.1.3.3 Single Gene Mutations NIPT is not confined to detect common chromosome disorders as well as clinically significant copy number variations. Monogenic disorders, generally caused by deletion/insertions and even a single base mutation, require base-level resolution in determination. Nevertheless, the level of circu-

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lating cffDNA is very low in maternal plasma at 11–17 weeks of gestation. For example, the average size of the circulating cffDNA could be as short as 166 bp, and its concentration could be as low as 25 genome copies/mL. Thus, conventional diagnostic approaches faced great challenges [25, 26]. Via approaches such as relative mutation dosage (RMD) for β-thalassemia, hemophilia, and sickle cell disease, NIPT has already been proved to be have the possibility of performing in monogenic diseases [27–30]. Other approaches include SNPs for whole-genome sequencing, direct linear amplification and quantification for Wilson’s disease, relative haplotype dosage for congenital adrenal hyperplasia (CAH), and β-thalassemia. Furthermore, with the development of digital PCR and NGS technology, novel detections, such as paternally inherited mutant alleles, mutations arising de novo, genotyping, and quantitation of circulating fetal DNA in maternal plasma become reality. When the information on parental and proband haplotypes was provided, doctors can construct fetal haplotypes and then generally diagnose the genetic disease.

43.1.3.4 Fetal Sex Determination The first application of cffDNA in maternal plasma was aimed to determine the fetal gender. It is extremely important for a mother who is a carrier of an X-linked disorder (such as Duchenne muscular dystrophy or hemophilia), because pregnancies with male fetuses are primarily at high risk. Besides, it is also beneficial for pregnant women who are at risk of conditions associated with ambiguous development of external genitalia (e.g., congenital adrenal hyperplasia), because early maternal treatment with dexamethasone can protect a female fetus from serious virilization [31, 32]. As for detection of male fetus-specific DNA in maternal plasma, the most commonly used technology is qPCR, which can amplify the single copy SRY (sex-determining region Y) gene, the single copy sequence DYS14, and the multicopy DAZ gene [33–35]. But when the analytical tests were performed ahead of 7 weeks of gestation, it was found that the results were unreliable because of the low cffDNA level [36]. However, identification of a female fetus using NIPT is based on the null results of Y chromosome-specific sequences and therefore may lead to the potential of false-negative results if the amount of male fetal DNA is too low to be detected [37]. Besides, organ transplantation status could also affect the determination. For example, Neofytou M and colleagues presented an exceptional case where NIPT contradicts the ultrasound-based sex determination. The pregnant woman was a recipient of a liver transplant from a male donor. Thus, graft-derived cell-free male DNA released into the maternal circulation interfered with the NIPT-based sex determination. Hence, NIPT is not suitable for gravidas underwent an organ transplant [38]. In addition, maternal mosaicism should also be considered in practice [39].

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Therefore, other approaches are required. One strategy was based on epigenetic markers. Some genes have been identified to display a differential methylation pattern in maternal blood cells and in fetal placenta. For example, the maspin gene (SERPINB5) promoter is unmethylated in the placenta but hypermethylated in maternal blood cells, while the RASSF1A gene (a tumor suppressor gene) is unmethylated in maternal cells and hypermethylated in the placenta, allowing us to distinguish maternal from fetal DNA [40–42]. Another strategy of identifying female fetuses from maternal plasma was to use paternally inherited short tandem repeats (STRs) located on the X chromosome [43]. As NIPT can be performed without posing a risk to the pregnancy, it could lead to an increase in such requests. However, ethical concerns about the use of NIPT for nonmedical traits and objectification of the child have been raised and thus should not be neglected [44, 45].

43.1.3.5 Hemolytic Disease of the Newborn RhD antigen is an important blood type antigen expressed on the surface of red blood cells of human beings. Among the population, the majority are RhD positive in blood type, while very few people present RhD negative. When a woman whose blood type is RhD negative is pregnant, it is likely that the fetus might have a RhD-positive blood type. When the fetal RhD-positive cells enter maternal circulation, an immune response, which is termed as “sensitisation,” launches leading to the production of anti-D antibodies against the RhD antigen. The response can happen at any time during the pregnancy, but it is most common in the third trimester and during childbirth [46]. The process of sensitization itself has few adverse effects on the mother and does not usually affect the first fetus. However, in the next pregnancy with an RhD-positive fetus in women already sensitized, the anti-D antibodies in maternal plasma may cross the placenta into the fetal blood resulting in hemolytic disease of the fetus and newborn. This can cause severe fetal anemia that leads to a series of fetal complications, e.g., heart failure, fluid retention and swelling (hydrops), hyperbilirubinemia, kernicterus, and even perinatal death [47]. In NIPT of fetal RhD status, a real-time qPCR method is used to detect cffDNA, which are small fragments of extracellular DNA derived from the placenta in the maternal plasma. Furthermore, via application of automated platforms, high-throughput NIPT is capable of performing large numbers of tests at the same time, and is therefore suitable for large-scale population screening of pregnant women. As a result of the unclear fetal RhD status, once RhD-negative women conceive a fetus, anti-D immunoglobulin should be used to prevent the pregnant mothers from sensitization via blocking the production of anti-D antibodies. If the fetus is RhD-negative, the risky therapy seems to be unnecessary. With the help of NIPT, fetal RhD status can be determined

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

and may enable anti-D immunoglobulin to be retreated from RhD-negative women who are carrying an RhD-negative fetus. In addition, these women may not need extra treatment of anti-D immunoglobulin following potentially sensitizing events, and there may no longer be a requirement for serologic cord testing at birth. High-throughput NIPT has already been used in this way in some European countries [48, 49]. A lately published systematic review has shown that high-­ throughput NIPT testing presents high diagnostic performance for the detection of fetal RhD status in RhD-negative women, with very low false negative and false positive rates in women tested at or after 11 weeks’ gestation. After all, high-throughput NIPT which could be used as a routine screening test for fetal RhD status in RhD-negative women can largely remove unnecessary exposure to prophylactic anti-D treatment [50].

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quantification has been documented as a promising biomarker for the prediction of pre-eclampsia [55]. Rolnik’s research shows that the lower the cffDNA, the higher were the risks for preeclampsia, but its capacity to act an as independent first-trimester marker in an algorithm for screening for pre-eclampsia requires further research [56]. On the contrary, other literatures also reported pre-eclampsia may be associated with elevated cffDNA in maternal serum [57–59]. Thus, well-designed further research remains needed.

43.1.4 Summary

The discovery of circulating cffDNA was inspired by reports of circulating tumor DNA in cancer patients [3]. This opened a tremendous era of research into the use of cfDNA for developing noninvasive methods for extracting fetus-specific 43.1.3.6 Other Applications information during early pregnancy. Chromosomal aneuIndeed, NIPT can rapidly screen fetal abnormality in genes, ploidy is a leading cause of fetal miscarries and birth defects, meanwhile, it could also be extensionally used for maternal so attentions are primarily focused on how to use fetal health status monitoring, such as tumor and pre-eclampsia. cfDNA as a potential noninvasive source of genetic informaSimilar to placental DNA, tumor DNA can also be detected tion. And this could facilitate the detection of aneuploidies. With the progress of biotechnology, especially the now in plasma, and therefore cell-free tumor DNA can be analyzed for characterization and monitor cancers. Plasma DNA available deep sequencing and single molecule PCR methanalysis allows for pre-symptomatic detection of tumors in ods, scientists are exploring more specific fetal genomic pregnant women undergoing routine NIPT. Amant F and col- abnormalities, including monogenic diseases and large chroleagues reported that during NIPT in over 4000 prospective mosomal insertion/deletion mutations. Although promising, pregnancies by parallel sequencing of maternal plasma cell-­ NIPT still draws skepticism because some recent reports free DNA, 3 aberrant genome representation (GR) profiles claim that compared to ultrasonography techniques, up to were observed that could not be interpreted by the maternal 8% of NIPT studies have underdiagnosis. Nevertheless, the or fetal genome. Those 3 patients were performed whole-­ variety of potentially life-altering genetic diseases has cerbody diffusion-weighted magnetic resonance imaging tainly not been exhausted, and consequently, the possibility (MRI), which revealed an ovarian carcinoma, a follicular of delivering an accurate prenatal diagnosis of such condilymphoma, and a Hodgkin lymphoma, each confirmed by tions remains a promising research avenue [1]. subsequent pathologic and genetic investigations. In addition, the copy number variations in the subsequent tumor biopsies were accordant with the previous NIPT plasma GR 43.1.5 Typical Medical Case profiles [51]. Also, Cohen and colleagues reported a proof-­ of-­concept method describing the early diagnosis of ovarian Clinical Background  The patient was a healthy pregnant cancer using sub-chromosomal changes in plasma DNA in woman, whose age, height, and weight were 34  years, the nonpregnant women, identified with a routine NIPT plat- 164 cm, and 71 kg, respectively. The history of gestation was form, but the debate still exists [52, 53]. And the mutual gravida 2, para 1. influence between circulating tumor DNA and circulating fetal DNA needs further investigated [54]. Imaging Examination  The ultrasound examination at a Hypertensive disorder during pregnancy, especially pre-­ gestational age of 26 weeks indicates that besides low-lying eclampsia, is one of the major risk factors of increased mor- placenta, the fetus has a left ventricular bright spot and a bidity and mortality of pregnancy and perinatal period all small amount of pericardial effusion. over the world. Early prediction of pre-eclampsia, the need for modern obstetrics, can timely prevent the progress of the Test Performed  NIPT results indicated that chromosome disease and reduce related fetal and maternal morbidity and 21 was abnormal. Then, prenatal diagnosis was performed, mortality. In addition to the screening of fetal aneuploidies, including karyotyping and chromosome microarray Rhesus-D status, fetal sex, single gene disorders, the cffDNA analysis.

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NIPT  Z-score of chromosome 21 was −6.876 suggesting occurence of deletions, and the deletion was approximately 18 Mb (Fig. 43.5a). Chromosomal Microarray-Based Analysis  Chromosomal microarray-based analysis results showed about 19.2  Mb deletions in 21q11.2-q22.11 (15,016,486-34,251,578)x1 (Fig. 43.5b, c). Chromosome Karyotype Analysis  Karyotype analysis of amniotic fluid showed chromosome structural abnormalities 46, XN, del(21)(q11.2q22.1). The analysis of chromosome karyotype of the parents showed no obvious abnormalities (Fig. 43.5d). Final Diagnosis  The deletion region (21q11.2-q22.11) contains some disease-causing genes, including LIPI, LPDL, PRED5, PRSS7, ENTK, APP, AAA, CVAP, AD1, SOD1, ALS1, MRAP, FALP, C21orf61, GCCD2, FGD2, C21orf59, CILD26, SYNJ1, PARK20, EIEE53. The deletion of two or more genes in this region is critical. To a great extent, it will have birth defects if the fetus is born. Finally, the parents opted for termination of pregnancy. This case was from The First Affiliated Hospital of Air Force Medical University.

43.2 Mitochondrial Deafness Xiaoting Lou and Jianxin Lyu

43.2.1 Introduction 43.2.1.1 Mitochondria and Mitochondrial Genome Mitochondrion is a two phospholipid bilayers-bounded organelle, exists in almost all eukaryotic cells except the erythrocytes. Mitochondrial complex V (ATP synthetase) provides almost 90% of the adenosine triphosphate (ATP) required for the body. Hence, mitochondria are usually called as the “powerhouse” of the cells [60]. Mitochondrion is an essential cellular organelle, which participates in a lot of pivotal metabolic pathways not only the well-known oxidative phosphorylation (OXPHOS) but also fatty acid oxidation, amino acid metabolism, lipid metabolism, urea cycle, Krebs cycle, gluconeogenesis, and ketogenesis [61]. Mitochondria are the only organelles having their own genome in animal. Human mitochondrial genome (mtDNA) is a  ~  16.6  kb circular, double-stranded DNA (Fig.  43.6). Mitochondrial genome includes 37 genes, 2 rRNA genes, 22 tRNA genes, and 13 mRNA genes encode for mitochondrial

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respiratory chain structural proteins. Unlike the nuclear genome (nDNA), mtDNA lacks an intron–exon structure. Furthermore, mtDNA follows a maternal inheritance while the nDNA is inherited in a Mendelian manner. Lastly, each cell contains different copies of mtDNA, according to different cell types the number can vary from a few hundred to tens of thousands [62, 63]. The mitochondrial genomes have their own functions of replication, transcription, and translation, controlled by the Displacement Loop (D-Loop). D-Loop is a  ~  1.1  kb single non-coding region. There are about 1500 mitochondrial proteins in total, which are encoded by both mitochondrial genome and nuclear genome (13 of them are encoded by mtDNA). Among the 1500 proteins, only about 150 proteins are directly involved in OXPHOS and ATP production. Assembly of the complexes, maintenance and expression of mitochondrial DNA, protein synthesis, and mitochondrial dynamics counts for the rest.

43.2.1.2 Mitochondrial Diseases Mitochondrial diseases are a group of clinical heterogeneous genetic disorders, can be caused by deleterious variants both in mitochondrial genome and nuclear genome. Mitochondrial disease is one of the most common subtype of hereditary disease, the prevalence of mitochondrial diseases in children is estimated to be 5 ~ 15 in 100,000 [64, 65]; while which is ~12.5 in 100,000 among adults [66]. According to a cohort study in England, nuclear genome mutant rate is estimated to be 2.9 per 100,000 and mitochondrial genome mutant rate is around 9.6 per 100,000 [66]. Mitochondrial diseases can be observed at any age, such as neonates, kids, teenagers, and adults; as mitochondria are ubiquitous in the human body, mitochondrial diseases can be associated with any tissues or organ with high energy demands (e.g., heart, brain, skeletal muscle, cochlea). As describe, there are hundreds of and thousands of mitochondrial DNA moleculars, normally, all moleculars are the same in the tissues which is called homoplasmy. However, when a tissue contains both mutant mtDNA and wildtype mtDNA, they are given the name of heteroplasmy. The distribution of mutant and wild-type moleculars determines the clinical manifestation of the patient, usually there exist a critical “threshold” (minimum percentage of mutant mtDNA). Over the threshold, the mitochondria cannot function properly, causing mitochondrial diseases. The severity of mitochondrial disease may vary throughout the lifetime, which partly induced by “Mitotic segregation” of cells. During the division, the mutant mtDNA moleculars in daughter cells can vary a lot, which finally will make accordingly changes in the clinical phenotype. With the development of next-generation sequence (NGS), the original method of sanger sequence is gradually get substituted with long-range polymerase chain reaction (LR-PCR) and then NGS technology. The LR-PCR plus NGS helps a lot in the diagnosis of mitochondrial genome-­

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis Fig. 43.5  Analysis result of fetal chromosome 21. (a) Result of fetal chromosome 21 analyzed by NIPT. The red dot area refers to the deletion part chr21; (b) Result of fetal chromosome 21 analyzed by chromosome microarray showing approximate 19.2 Mb deletions in 21q11.2-q22.11 of chromosome 21; (c) Microarray profile of chromosome 21 showing the region of deletions and the corresponding genes in OMIM; (d) The fetal karyotype was 46,XN,del(21) (q11.2q22.1)

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780

Fig. 43.6  Mammalian mtDNA

related diseases, with the accurate detection of the heteroplasmy of the sample. However, the effective treatment methods still lacked here for mitochondrial diseases. Although there are some investigations on mice model have received good news [67–69]. For example, Michele and his colleagues applied the hypoxia treatment in a mouse model with Leigh syndrome received evidence of reversion of disease to a certain degree [70]. In the future, further investigations and correlated therapeutic clinical trials are required to follow up.

43.2.1.3 Deafness and Mitochondrial Deafness Deafness is one of the major human health concerns. According to the World Health Organization, hearing impairment affects about 5% population worldwide [71]. It is well recognized that a normal hearing ability is critical for speech and language skills development, however, the impairment of hearing will impair the voice, speech, and language, which will furthermore do harm to the communication ability. Conventionally, higher numbers of decibels (dB) indicating worse hearing: normal hearing was defined when hearing thresholds ≤25 dB in both ears, disabling hearing in adult defined when hearing thresholds >40 dB, and disabling hearing in child defined when hearing thresholds >35 dB [72]. Ear is a critical organ, for detecting the sound and maintain-

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ing the balance, which can be divided into three anatomical portions external (outer), middle, and inner region (Fig. 43.7). There are several methods to describe and categorize the hearing loss. Based on the anatomic defects, hearing loss can be classified into conductive hearing loss (CHL), sensorineural hearing loss (SNHL), mixed hearing loss (both CHL and SNHL exist); based on the etiology, hearing loss can be mainly classified into genetically inherited hearing loss and environment-induced hearing loss. As reported, among the congenital hearing loss, genetically inherited hearing loss count 50%, while among the prelingual hearing loss, genetic count for 80%. Therefore, we need to focus more on genetically inherited hearing loss [73]. The inheritance pattern of hereditary hearing loss including autosomal recessive, autosomal dominant, X-linked, and mitochondrial inheritance. In this chapter, we will focus on the mitochondrial inherited hearing loss and deafness. The same as other mitochondrial DNA induced disease, mitochondrial deafness is maternally inherited, mainly affect the tissues with high energy demand (e.g., cochlea), the onset age is variable which will range from 5 to 50 years old, and the hearing impairment degree also variable from mild to profound but mostly progressive. Mitochondrial hearing loss is belonging to the sensorineural hearing loss, not conductive (Fig. 43.8). According to if the hearing loss is occurred with other symptoms affecting other parts of the body, mitochondrial impairment can be classified into syndromic and nonsyndromic [73].

43.2.2 Clinical Appearance Hearing impairment is a frequent sign of mitochondrial disease with extremely variable onset and severity. The clinical phenotypic expression of mitochondrial hearing loss (mtDNA related) can be highly variable with several causes, the mutation load of mtDNA (heteroplasmy); the nuclear background, as He reported in 2013, the predisposition to aminoglycoside antibiotics of mt.1477C>G mutation can be modulated by the nuclear gen modifier MTO2 [74]; the mitochondrial DNA haplogroup plays a role in the mitochondrial disease clinical manifestation, Hudson and his colleagues studied more than 3600 patients found the clinical expression of Leber Hereditary Optic Neuropathy can be affected by the mitochondrial genome haplogroup group [75]; the environmental factors. Cochlea, which is high energy required, is highly sensitive to mitochondrial dysfunction. Accordingly, mitochondrial disease is presumably occurred with sensorineural hearing loss. Mitochondrial sensorineural hearing loss could present in both syndromic hearing loss and non-syndromic hearing loss forms.

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

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Fig. 43.7 Anatomical structure of the human ears

Medical and birth history Audiometric assessment of hearing loss Three-generation pedigree and family medical history Physical examination Suspect acquired hearing loss?

No

Yes Provide CMV testing, imaging, or other testing based on suspected etiology (e.g., rubella, meningitis)

Acquired etiology confirmed?

Yes

Provide pre-test genetic counseling and genetic testing as clinically indicated: • If syndromic hearing loss is suspected, consider targeted gene testing based on suspected diagnosis; • If nonsyndromic hearing loss is suspected, consider single-gene tests such as GJB2 and GJB6 gene panel tests, or NGS testing based on history and findings Provide imaging or other testing as appropriate for suspected diagnosis

No or inconclusive

Genetic etiology confirmed?

Yes

Reconsider potential acquired and genetic etiologies

No or inconclusive Provide additional testing and imaging based on findings

Provide treatment as clinically indicated Provide follow-up counseling, including genetic counseling, as needed, based on genetic and other test results and findings Provide rolorrals to specialists, as needed, based on genetic and other test results and findings Provide follow-up care at periodic intervals based on genetic and other test results and findings, and patient needs

Fig. 43.8  Schematic view of the evaluation and diagnosis of hearing loss from ACMG guideline

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43.2.2.1 M  itochondrial Syndromic Hearing Loss As mentioned, mitochondrial sensorineural hearing loss could occur both in syndromic and non-syndromic forms. The auditory deprivation is categorized as mitochondrial syndromic hearing loss if mitochondrial sensorineural hearing impairment present with other symptoms (e.g., neuromuscular signs, retinopathy, and kidney diseases). Here, we will mention some typical mitochondrial syndromic hearing impairment. Kearn-Sayre Syndrome Kearn-Sayre syndrome (KSS; OMIM #530000) is a condition caused by mitochondrial dysfunction. KSS appears when mitochondrial oxidative phosphorylation could not provide enough energy because the certain deletion of certain genes in mitochondrial genome is inherited in a mitochondrial pattern (maternal inheritance). Depending on the epidemiological study, KSS appears around 1–3 in 100,000 [76]. The patient was first reported by Kearn in 1965 [77], characterized by ophthalmoparesis and pigmentary retinopathy. KSS is an early onset disease, symptoms presumably appear before 20 years old. KSS is a kind of rare mitochondrial dysfunction induced progressive neuromuscular disease with multi-system and multi-organ affected (e.g., central nervous system, musculoskeletal system, retina, kidney, liver, heart, and inner ear). As reported, hearing loss in patients with Kearn-Sayre syndrome almost present in a high-frequency hearing deteriorating form [78]. Due to the energy deficits among KSS patients, the cochlea cannot work properly, especially the external hair cells in basal coil who are responsible for the high-frequency sound [78].

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mutation, m.8344A>G mutation in MTTK gene is most common. Patients with MERRF usually first experience the myoclonus (muscle twitches) then followed the weakness, ataxia, and epilepsy, which is a canonic feature of MERRF. Ragged red fibers can be seen in most muscle biopsies of the patients. This syndrome was first described by Fukuhara in 1980 [81]. Most MERRF share the common mitochondrial disease symptoms including learning disability, short stature, optic atrophy, and hearing loss. Hearing loss in MERRF manifested with learning disability, short stature, optic atrophy, and hearing loss. And hearing loss in MERRF is the type of bilateral sensorineural hearing impairment which can appeared suddenly or gradually [82, 83].

43.2.2.2 Mitochondrial Non-Syndromic Hearing Loss The same as mitochondrial syndromic hearing loss mentioned before, mitochondrial non-syndromic hearing loss generally is sensorineural hearing loss with the impairment to cochlea (a critical inner ear structure). Most mitochondrial non-syndromic hearing loss are caused by mutations in MT-RNR1 and MT-TS1 (Table  43.3). MT-RNR1 encodes mitochondrial 12S ribosomal RNA, and MT-TS1 encodes the serine transfer RNA.  Both 12S ribosomal RNA and serine transfer RNA play a key role in mitochondrial protein synthesis. MT-RNR1 Related Mitochondrial Hearing Loss Sensorineural Hearing Impairment Induced by Aminoglycosides

As well recognized, aminoglycoside antibiotics are widely used for Gram-negative bacilli infections. However, aminoglycosides like gentamicin, kanamycin, tobramycin, and paromomycin may put patients at the risk of renal toxicity Mitochondrial Encephalopathy with Lactic Acidosis, and ototoxicity. Individuals with pathogenic variants in and Stroke-like episodes Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-­ MT-RNR1 can be associated with the predisposition to like episodes (MELAS; OMIM#540000) is a syndrome due aminoglycosides-­induced ototoxicity. Concisely, aminoglyto the abnormality of mitochondrial oxidative phosphoryla- cosides destroy the bacteria by binding to the small subunit tion function, usually caused by m.3243A>G mutation [79]. (30S) of the bacterial 70S ribosome, which will destroy the MELAS is a multi-systemic syndrome with variable signs protein synthesis (translation). The difference between the and symptoms includes encephalopathy, seizure, dementia, structures of 70S ribosomes (prokaryotes, each consisting of lactic acidosis, muscle weakness, exercise intolerance, 30S and 50S subunits) and 80S ribosomes (eukaryotes, each stroke-like episodes, learning disability, auditory depriva- consisting of 40S and 60S subunits) helps aminoglycoside tion, and recurrent vomiting [79]. Most clinical appearance antibiotics combat the bacteria without doing harm to the will first present between 2 and 40 years old. Hearing impair- human mammalian cells. However, the mutation in MT-RNR1 ment is not rare in patients with MELAS, according to a (e.g., mt.A1555G) will make the human mitochondrial 80S cohort study of 45 patients, the frequency of hearing loss is ribosomes more similar to the 70S ribosomes, which will almost 77% [80]. Hearing deficits in MELAS are mostly per- facilitate the aminoglycosides to bind to human ribosome site [109]. Once the aminoglycosides are bound, they will formed in a mild and progressive form. hold a long half-life in the hair cells (HCs) in the inner ear (even several months) finally lead to an ototoxicity. Myoclonic Epilepsy with Ragged Red Fibers Hearing loss induced by aminoglycosides generally Myoclonic epilepsy associated with ragged red fibers (MERRF; OMIM#545000) is a multi-system involved mito- appears bilateral, severe to profound, most irreversible but chondrial disorder, can be induced by mitochondrial DNA not progressive, and with a variable onset time (a few days to

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

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Table 43.3  Mitochondrial mutations associated with hearing loss Variations (nucleotide Genes change) rRNA genes MT-RNR1 m.827A>G m.961delT/insC m.961T>G m.1095T>C

Product

Clinical significance/status Additional features

Aminoglycoside ototoxicity

References

12S rRNA 12S rRNA 12S rRNA 12S rRNA

Drug response Pathogenic Pathogenic Drug response

+ + + +

[84] [85] [86] [87]

12S rRNA 12S rRNA

Drug response Drug response

Non-syndromic SNHL Non-syndromic SNHL Non-syndromic SNHL Non-syndromic SNHLor with Parkinson, and neuropathy Non-syndromic SNHL Non-syndromic SNHL

+ +

[88] [89]

tRNASer(UCN) tRNASer(UCN) tRNASer(UCN) tRNASer(UCN) tRNASer(UCN) tRNASer(UCN) tRNASer(UCN)

Pathogenic Pathogenic Pathogenic Pathogenic Pathogenic Pathogenic Pathogenic

Non-syndromic SNHL Non-syndromic SNHLor with PPK Non-syndromic SNHLor with PPK Ataxia, dysarthria, myoclonus

+ / / /

Non-syndromic SNHL Non-syndromic SNHL

/ /

m. 7512T>C

tRNASer(UCN)

Pathogenic

/

MT-TI MT-TQ

m.3243A>G m.3256T>C m.4269A>G m.4336A>G

tRNALeu(UUR) tRNALeu(UUR) tRNAIle tRNAGln

/ / / /

[103] [104] [105] [106]

tRNAGln tRNAHis

Alzheimer, Parkinson, migraine

MT-TH

m.4336T>C m.12201T>C

Pathogenic Pathogenic Pathogenic Uncertain significance Pathogenic Pathogenic

Progressive myoclonic epilepsy, ataxia, and hearing impairment MIDD, MELAS, PEO MERRF Cardiomyopathy Migraine

[90] [91] [92–95] [96] [97] [98, 99] [100, 101] [102]

/ /

[107] [108]

m.1494C>T m.1555A>G tRNA genes MT-TS1 m. 7444G>A m. 7445A>C m. 7445A>G m. 7471_7472insC m. 7505T>C m. 7510T>C m. 7511T>C

MT-TL1

SNHL sensorineural hearing loss; PPK palmoplantar keratoderma; MIDD maternally inherited diabetes and deafness; MELAS myoclonic epilepsy, lactic acidosis, and stroke-like episodes; PEO progressive external ophthalmoplegia; MERRF mitochondrial encephalomyopathy with ragged red fibers; rRNA ribosomal RNA; tRNA transfer RNA

weeks after the usage of aminoglycosides) [92]. However, the vestibular signs are not common under this situation according to a big cohort in China [110]. Sensorineural hearing impairment without aminoglycosides exposure

MT-RNR1 pathogenic variants would render individuals predisposition to aminoglycoside antibiotics; however, mutations in MT-RNR1could also lead to hearing impairment even deafness independent with an aminoglycosides exposure. As reported, m.1555A>G is a hotspot in mitochondrial non-syndromic hearing loss [111]. M.1555A>G induced sensorineural hearing loss has variable clinical manifestation, onset time, ranging from congenital to late-onset; severity, ranging from mild, moderate to profound. The Vestibular signs are also rare here. According to Zhu [112], the severity of the sensorineural hearing loss induced by m.1555A>G mutation is consistent with the mutant load, a higher mutant load is correlated to a worse hearing ability.

ated with many health conditions, include MERRF, Palmoplantar keratoderma with deafness [93], non-­ syndromic hearing loss, and other symptoms and signs. Non-­ syndromic hearing loss induced by MT-TS1 has a wide array of clinical appearances [92]. The severity of hearing impairment varies from mild, moderate, to profound. Most patients share the onset time of childhood, and with a progression of the deterioration.

43.2.3 Diagnosis Diagnosis is the very first step for later treatment, management, and genetic counseling. Teamwork is welcomed for the identification and evaluation of mitochondrial hearing impairment, otolaryngologists, audiologists, clinical geneticists, and other specialists are needed.

MT-TS1 Related Mitochondrial Hearing Loss

43.2.3.1 M  itochondrial Sensorineural Hearing Loss

MT-TS1 is located on mitochondrial genome 7446-7514, encoded for transfer RNA serine 1 (tRNASer(UCN)), tRNASer(UCN) is critical for mitochondrial oxidative phosphorylation protein synthesis. The pathogenic variants in MT-TS1 are associ-

Initial Diagnosis The diagnosis of mitochondrial sensorineural hearing loss is suggested in a proband with the followings:

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Table 43.4  Hearing loss graded by level of severity A. Family history Clinical geneticists are required to collect the three-­ Degree of hearing loss Hearing loss range (dB) generation family history information of the proband. Normal −10 to 15 More attention should be paid to the relatives with hear- Minimal 16 to 25 26 to 40 ing loss, physicians are supposed to obtain the informa- Mild 41 to 55 tion by performing the direct test or reviewing the Moderate 56 to 70 medical records. As the maternal inheritance of mito- Moderately severe Severe 71 to 90 chondrial genome-related diseases, we should be more Profound 91+ careful about the maternal relatives. B. Clinical examination of mitochondrial genome mutation, so the panel is supFurther clinical examinations are required here if relposed to have the ability to identify the heteroplasmic evant symptoms and signs exist, or evidence indicates level of the pathogenic variant. other organ involvement. For example, renal tests, cardiac tests, and ophthalmologic evaluation are always C. Mitochondrial genome sequencing If both target testing and multigene panel do not give required here. any satisfactory result, and clinical evaluation indicates C. Audiometric testing maternal inheritance pattern, complete mitochondrial Auditory Brainstem Response (ABR) Test and genome sequencing should be considered. Mitochondrial Brainstem Auditory Evoked Response (BAER) Test. whole-­ g enome long-range PCR plus next-generation These two tests are designed to check if the brain will sequencing now is widely applied, with this method, pathorespond to sound properly. Otoacoustic Emissions (OAE) genic variants on mitochondrial genome can be easily idenis a test that helps check how the inner ear responds to tified including the heteroplasmic level of the sample. sound. Behavioral Audiometry Evaluation is a test that could check all regions of ear, examine how an individual 43.2.3.2 Differential Diagnosis responds to a sound overall.

The results of mitochondrial deafness supposed to behave as follows, severity: moderate to profound hearing loss (classification based on severity using decibel (dB) system, Table 43.4); mild-to-moderate high-frequency hearing loss; the family history indicates a maternal inheritance pattern of hearing loss or other relative symptoms (hearing loss sometimes is sub-clinical cannot be noticed). Establishing the Diagnosis In order to establish the diagnosis of mitochondrial hearing loss either syndromic or non-syndromic, clinicians are required to finish the initial evaluation first. If the initial results indicate a high possibility of mitochondrial deafness, a molecular genetic test is required to establish the diagnosis. A. Targeted testing If an individual develops a hearing disability after the exposure to aminoglycoside antibiotics, the target testing could be performed first, including MT-RNR1 m.1555A>G, MT-RNR1 m.1494C>T, MT-TS1 m.7445A>C, MT-TS1 m.7445A>T, and MT-TS1 m.7445A>G. B. Multigene panel There are several mitochondrial deafness-related multigene panels, however, clinical geneticists are trained to choose or design an appropriate panel according to the evaluation results. The heteroplasmy is a critical feature

Aminoglycosides-Induced Ototoxicity Aminoglycosides is a class of commonly used antibiotics with the side effects of nephrotoxic, ototoxicity, and vestibular toxicity. As reported, individuals with the MT-RNR1 mutation will predispose them to aminoglycoside-induced ototoxicity, however, hearing impairment will also happen when there are no MT-RNR1 pathogenic variants as aminoglycosides could generate free radicals within the inner ear.

43.2.4 Management 43.2.4.1 Treatment of Manifestations Appropriate and timely evaluation and treatment are required for the auditory deprivation patients, a team of specialists including an audiologist, an otolaryngologist, a clinical geneticist, and others are needed here. As for the later rehabilitation, hearing aids, cochlear implantation, speech therapy, and language training are required. For the patients suffered from severe to profound hearing loss, an earlier cochlear implantation (>12 months) is benefit for the attainment of language skills and social functions. Electric acoustic stimulation (EAS) is good for patients with residual hearing left in the lower frequencies. Lastly, the enrollment of appropriate educational programs may be important for the children’s knowledge acquisition.

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43.2.4.2 Prevention of Primary Manifestations Firstly, before applying the treatment of aminoglycoside antibiotics, clinicians are required to inquire or review medical records and family history of relevant hearing loss. If a patient has relatives with a history of aminoglycosides-­ induced hearing deficits, especially when he/she is a maternal relative, aminoglycosides treatment should be substituted. When sensorineural hearing impairment happens when an individual is accepting the aminoglycosides treatment, the treatment ought to stop as soon as possible.

Amniocentesis and chorionic villus sampling (CVS) are the two main methods in PND.  PND can be processed, if the disease-causing mutation is identified in the family. If the mutation load in mother is at very high percentage, there is even no need to perform genetic testing based on the maternally inherited pattern. However, because of the mitotic segregation, we should be careful that the mutant load of the mtDNA in amniocentesis and CVS is not reliable to correspond to that of other fetal or adult tissue and the disease severity [113].

43.2.4.3 Prevention of Secondary Complications Auditory deprivation may cause severe sequelae with proper early auditory intervention. Sequelae like poor language development (including speech production and communication skills), reading development may do harm to individuals’ social adaptation. Thus, timely auditory intervention (including auditory amplification, otologic surgery, and cochlear implantation) would be good for the hearing loss, especially prelingual hearing loss.

43.2.5.2 P  reimplantation Genetic Diagnosis in Mitochondrial Deafness As mentioned, prenatal diagnosis needs to be concerned because of the mitotic segregation, there may be a poor correlation between the mutation load of mtDNA in fetal samples and disease severity. Preimplantation genetic diagnosis (PGD) is the genetic profiling of embryos before implantation, a way to help the doctor to identify genetic defects within embryos. In vitro fertilization (IVF) is the common process to create the embryos used in PGD.  The obtained embryos are analyzed and only the ones with the mutation load below the certain threshold has the chance to be transferred. Some certain genetic disorders can be prevented to pass to the child with PGD. PGD was first reported in 1990 and was first performed by Handyside [114, 115]. To our knowledge, PGD may provide the mtDNA mutation carriers a chance to conceive a healthy baby. However, we are supposed to know that the application of PGD may only reduce not eliminate reproductive risk.

43.2.4.4 Surveillance Firstly, audiometric testing needs to be done annually (in order to evaluate the stability and progression of hearing impairment). A complete document is required to help the physician track the progression. Moreover, as mentioned before, a teamwork may help the identification, the related physical examinations are also required to be done by a pediatrician or a physician. 43.2.4.5 Agents and Circumstances to Avoid If an individual carries the pathogenic variant of MT-RNR1 m.1555A>G, MT-RNR1 m.1494C>T, MT-TS1 m.7445A>C, MT-TS1 m.7445A>T, and MT-TS1 m.7445A>G, he/she should not be exposed to aminoglycoside antibiotics and unnecessary environmental noise. If one of maternal relatives, have the mutations mentioned here, exposure should also be avoided. 43.2.4.6 Evaluation of Relatives at Risk Individuals at risk for hereditary deafness and hearing loss should receive early detection and could benefit from avoiding aminoglycosides and early management.

43.2.5 Prenatal Diagnosis and Preimplantation Genetic Diagnosis in Mitochondrial Deafness 43.2.5.1 P  renatal Diagnosis in Mitochondrial Deafness Prenatal diagnosis (PND) is a procedure used prior to birth to help diagnose if there is any defect in your developing baby.

43.2.6 Conclusion This chapter focuses on mitochondrial deafness, including a simple introduction, clinical appearance, diagnosis, and management. In the introduction part, we give a brief background of mitochondria, mitochondrial genome, deafness, and mitochondrial deafness. Mitochondrial deafness, belongs to sensorineural hearing loss due to the inner ear structure, cochlea deficit. Hearing deficits is one of the most common features in mitochondrial diseases both syndromic and non-syndromic. Benefit by the advances of the next-­ generation sequencing (NGS), the diagnosis of genetic diseases becomes more precise. Genetic diagnosis is critical for the following, prognosis, genetic counseling, and management. Auditory deprivation may cause severe sequelae with proper early auditory intervention. Sequelae like poor language development (including speech production and communication skills), reading development may do harm to individuals’ social adaptation. Thus, timely auditory intervention (including auditory amplification, otologic surgery, and cochlear implantation) would be good for hearing loss, especially prelingual hearing loss. For the patients suffered

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from severe to profound hearing loss, an earlier cochlear implantation (>12 months) is benefit for the attainment of language skills and social functions.

43.2.7 Internet Resources The following organizations are good resources for information on mitochondrial deafness: 1. Online Mendelian Inheritance in Man (OMIM) https://www.omim.org/ 2. MITOMAP http://www.mitomap.org/ 3. Hereditary Hearing Loss Homepage https://hereditaryhearingloss.org/ 4. Mitochondrial disorders https://neuromuscular.wustl.edu/mitosyn.html 5. ACMG Guideline https://www.acmg.net/docs/ACMG_Guideline_for_ Clinical_Eval_and_Etiologic_Dx%20of_Hearing_Loss_ GIM_Apr20

43.3 Hereditary Vascular Retinopathy Jian Wang and Yufei Xu Diseases involving the retinal vascular system constitute the most frequent causes of visual impairment and blindness. There are many causes of retinal vascular disorders, including the effects of ocular and systemic diseases on retinal vessels, which can be divided into hereditary and non-hereditary factors. Regional variations in retinal vascular function are important for the diagnosis and management of a number of retinal diseases. This chapter summarizes the content of hereditary vascular retinopathy, including the clinical features, examination, management, and genetic counseling of several major disorders.

43.3.1 Overview 43.3.1.1 The Anatomy of the Retina The retina is a thin, light-sensitive layer of tissue, which is the basis of forming various visual functions. It begins at the ora serrata and ends at the optic papilla. Ten layers of cells in the retina can be observed microscopically. In general, the retina is composed of four main layers: (1) The retinal pigmented epithelium (RPE) is next to the choroid, (2) The light-sensitive cells (the layer of rods and cones) are above the epithelium, (3) A layer of nerve cells (neurons) are called the bipolar cells, (4) The ganglion cells are the innermost

Fig. 43.9  The anatomy of the retina. The optic disc, the macula, and artery can be observed from the fundus examination

layer of neurons and connected with bipolar cells. The transmitted messages are carried out of the eye along their axons, which constitute the optic nerve fibers. The RPE and retinal neurosensory layer, which is formed by the differentiation of the outer and inner layers of the optic cup, respectively [116]. There is a potential gap between the two layers. Normally, the retinal neurosensory layer is attached to RPE. The adhesion between the two layers is weak, which is the anatomical basis for their easy detachment. From the center of the optic nerve radiate the major blood vessels of the retina (Fig.  43.9). There are two sources of blood supply to the retina: the central retinal artery and the choroidal blood vessels. The choroid receives 65–85% blood flow and is essential for the outer retina (particularly the photoreceptors) and the remaining 20–30% flow through the central retinal artery from the optic nerve head to nourish the inner retinal layers. The central retinal artery has four main branches in the human retina. The arterial intraretinal branches then supply three layers of capillary networks (i.e., the radial peripapillary capillaries, and the inner and outer layer of capillaries). Retinal blood vessels consist of non-­fenestrated endothelial cells surrounded by contractile pericytes, which are vital for hemodynamic autoregulation in response to changing metabolic requirements of the retina [117]. The tight junctions and pericytes between the endothelial cells of the retinal capillary wall constitute the inner retinal barrier. The tight junctions between the RPE cells constitute the outer retinal barrier [116]. Due to the existence of inter-

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

nal and external retinal barriers, the retinal neurosensory layer, normally, keeps dry and transparent. If any of these barriers are destroyed, blood plasma and other components in the blood vessels will leak into the retinal neurosensory layer, causing retinal edema or detachment. RPE, Bruch’s membrane and choroidal capillary, called the pigment epithelium-­ Bruch membrane-chorio-capillaris complex, constitute a unified functional network. It plays an important role in maintaining photoreceptor microenvironment. Many disorders of the ocular fundus are associated with the impaired complex.

43.3.1.2 The Physiology of the Retinal Vessels Both the inner and the outer retinal vascular supply display regional differences in structural and functional ways, which are reflected in the disease patterns occurring in these regions [116]. The inner retinal vascular supply has been more extensively studied than the outer, due to its availability for examination through the optics of the eye in vivo. The purpose of the inner retinal vascular supply is to meet the metabolic demands of the inner down to the outer plexiform layer, and thus its distribution will reflect the organization of these layers. The retinal vascular system has no autonomic innervation, indicating that the retinal blood flow is regulated by pressure autoregulation, metabolic autoregulation, or vasomotion [118]. The retinal vascular system has a limited capacity to increase the blood flow when the retinal metabolism is enhanced, and the high oxygen extraction imposes a vulnerability of the retina to conditions where its metabolic demand exceeds the supply from the blood. This may be the reason why capillary ischemia is one of the main signs of retinal vascular disease. The choroidal blood flow not only meets the metabolic demand of the outer retina, but also acts to divert the heat generated when light passes the photoreceptors and is absorbed in the pigmented layers of the retina and the choroid [117]. This results in a low extraction of metabolites and has the phenomena that the metabolic environment of the choroidal veins and the outer retina is almost similar to that of the arterial blood. There are many reasons for retinopathy caused by retinal vascular changes [117], including the effects of ocular and systemic conditions on retinal vessels, which can be summarized as follows. (1) Mechanical obstruction of retinal vessels: retinal vascular obstruction due to internal embolism or thrombosis, or external compressions, such as retinal artery occlusion or retinal vein occlusion. (2) Retinal vascular inflammatory disease or immune complex invasion of vascular wall, such as Eales disease, giant cell arteritis, and acute retinal necrosis. (3) Systemic vascular disease: hypertension or arteriosclerosis can cause various retinopathy, such as malignant hypertension and pregnancy-induced hypertension syndrome. (4) Systemic hematopathy, such as

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anemia, leukemia, and hemoglobin abnormality can cause different retinopathies. (5) Metabolic diseases, such as diabetic retinopathy. (6) Retinal vascular abnormalities and developmental abnormalities: such as Coats disease, retinopathy of premature infants, and retinal cavernous hemangioma.

43.3.1.3 The Examination of the Retina The regional lesions of retinal vascular diseases reflect the pathophysiology of these conditions, and in cases where a causal relation can be established between pathophysiology and the distribution of lesions, this information may help to predict the course of and in devising new therapeutic interventions for the disease. A prerequisite for studying regional lesions in vascular retinopathy is to have access to methods that allow the resolution of these regional variations in retinal vascular structure and function. The fundus is the only part of the body where blood vessels can be observed directly [118]. Therefore, fundus examination is helpful to the diagnosis and to understand the severity of the lesions (Fig. 43.10). Clinical diagnoses are made by noting how morphological lesions in the retina vary in shape, size, location, and dynamics, and subsequently concluding the presence of a specific disease entity. The following approach can be used to record the morphological changes, to identify the site of a retinal vascular occlusion, and to assess whether retinal diseases are primarily due to changes in the larger retinal vessels or the microcirculation [117]. (1) Ophthalmoscope is a traditional and routine method for observing fundus morphology. Indirect ophthalmoscope has a large field of vision and strong stereoscopic sense. It is widely used in the clinic to observe distant periphery combined with scleral compression. (2) The slit lamp combined with various preplacedmirror or three-­mirror lens examinations can also delicately observe various parts of the fundus. (3) Ultrasound can be used to detect the posterior segment of the eye when the refractive medium is opaque, and to diagnose and differentiate the posterior segment mass. (4) Fundus fluorescein angiography can be used to understand the condition of the retina and choroidal circulation system (Fig.  43.10). Indocyanine green angiography (ICGA) can provide a clearer and more intuitive understanding of choroidal circulation dynamics. (5) Optical coherence tomography (OCT) is a non-contact and noninvasive imaging technique with high resolution, which is of great value in the diagnosis, differential diagnosis, disease tracking, and therapeutic evaluation of posterior segment diseases (­ especially macular diseases). (6) Electroretinogram (ERG) can diagnose retinopathy in different layers according to the abnormality of various waves. Electrooculogram (EOG) mainly reflects the functional status of RPE [118].

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OS, FA, 0:29.20 55º

OS, FA, 1:42.39 55º

Fig. 43.10  Fundus fluorescein angiography of the left eye

43.3.2 von Hippel-Lindau Syndrome 43.3.2.1 Overview von Hippel-Lindau (vHL) syndrome (OMIM #193300) is an autosomal dominant inherited tumor predisposition syndrome, which is characterized by a variety of benign and malignant neoplasms, involved predominantly the retina, cerebellum, spinal cordf kidney, adrenal gland, and pan-

creas. Of these, retinal hemangioblastomas sometimes are present as the primary manifestation, further lead to vision loss, visual impairment, and even blindness. The incidence of vHL syndrome is estimated at approximately one in 36,000 births [119]. Although the majority of tumors occur in adulthood, children patients account for a significant proportion and are vulnerable to the delayed diagnosis and its sequelae [118].

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43.3.2.2 Clinical Appearance Tumors occurring in different regions result in specific phenotypes. Even among family members, the manifestations and severity are quite different. The most characteristic tumor type in vHL is hemangioblastoma. Hemangioblastomas of the cerebellum, retina and spinal cord, pancreatic and renal cysts, clear cell renal cell carcinoma, neuroendocrine tumors, and pheochromocytoma are the most frequent. Although it is a benign tumor made of newly formed blood vessels, it may cause complications such as ataxia and loss of vision when the tumor grows in the central nervous system (CNS) and retina [120]. Retinal hemangioblastomas are identified in around 70% of affected individuals. The tumors are most often located in the vessels of the temporal periphery, optic disc, and posterior pole. Patients could be asymptomatic or show a visual field defect/vision loss caused by retinal exudation, hemorrhage, or detachment [121]. The probability of vision loss may increase with age, although the number of tumors is steady. CNS hemangioblastomas remain the main cause of death. The most common sites are infratentorial space (mainly the cerebellar hemispheres) and the pituitary stalk. For instance, patients present with headache, vomiting, or ataxia, indicating the lesions occurred in infratentorial regions [118]. Spinal hemangioblastomas, most commonly occur in the cervical or thoracic region, usually present with pain, sensory and motor loss resulting from cord compression. Cysts are also common manifestations and mostly occurred in the kidneys, pancreas, and genital tract. Multiple and bilateral renal cysts are frequent. Most pancreatic lesions are simple cysts and have no malignant potential and rarely cause endocrine or exocrine insufficiency. Biliary obstruction may be caused by the cysts that happened in the head of the pancreas. Tumors in other regions can be observed in patients with vHL [122]. Around 69% of patients with vHL will develop renal cell carcinoma (RCC), which is also the dominant factor of death. Pheochromocytoma, located in one or both adrenal glands, can be asymptomatic or cause headaches, hypertension. Neuroendocrine tumors, which have been in about 5%–17% of vHL syndrome patients, are often nonfunctional. However, malignant behavior has been found. Endolymphatic sac tumors lead to vertigo, tinnitus, sudden hearing loss in 10%–16% of patients. Bilateral epididymal and broad ligament cystadenomas could cause infertility.

fundoscopy and routine ophthalmoscopy. The abnormal retinal function could be caused by quiescent retinal angiomas [123]. Associated audiological evaluation could evaluate for hearing loss. Serum and urinary catecholamine metabolites test can assist in the diagnosis of pheochromocytomas. Imaging examinations such as ultrasound, CT, or MRI are useful in identifying vHL lesions of the brain, spinal cord, abdomen, and middle ear. Identification of a heterozygous germline pathogenic variant in vHL gene through genetic testing approaches include single-gene testing, multigene panel, whole-exome/ genome sequencing [122].

43.3.2.3 Examination Physical examination is usually limited since the diagnosis is generally made based on accessory examination findings. Retinal hemangioblastomas and others such as retinal detachment, macular edema, or cataracts can be detected on

Isolated Hemangioblastoma or RCC The application of molecular genetic testing could effectively rule out vHL syndrome with a high degree of certainty. However, it is worth noting that somatic mosaicism of a vHL gene variant still could be thought about.

43.3.2.4 Diagnosis The diagnosis of vHL syndrome is established in a patient with the clinical manifestations and/or by identification of a germline pathogenic variant in vHL gene through molecular genetic testing. A clinical diagnosis of vHL can be established in one of two scenarios: (1) in a proband with a family history of vHL syndrome and with one or more of the manifestations of spinal, cerebellar or retinal hemangioblastoma, pheochromocytoma, RCC, or multiple renal and pancreatic cysts; (2) in a sporadic case with two or more characteristic lesions of hemangioblastomas, RCC, pheochromocytomas, or other tumors mentioned in Clinical Characteristics Section [120]. When a proband who has suspected characteristics regardless of a vHL family history should be performed genetic testing. vHL protein is primarily responsible for the degradation of hypoxia-inducible factor, which plays an important role in oxygen regulation in the cells. Mutations in the vHL tumor suppressor gene prevent the production or cause abnormal production of the vHL protein, resulting in the uninhibited upregulation of hypoxia-inducible factor and associated downstream growth factors and consequently the formation of cysts and vascular tumors [118]. 43.3.2.5 Genotype–Phenotype Correlations Several researchers suggested the molecular etiology of pheochromocytomas is distinguished among vHL lesions. Thus, four vHL subtypes (type 1, type 2A, type 2B, type 2C) are classified according to the risk of pheochromocytoma and other tumors. Table. 43.5 summarizes the genotype–phenotype studies to date [124]. 43.3.2.6 Differential Diagnosis

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Table 43.5  Genotype–phenotype correlations of vHL syndrome vHL subtype Phenotypes Type 1 Low risk for pheochromocytoma

Type 2A Type 2B

Type 2C

High risk for pheochromocytoma, retinal and CNS hemangioblastoma High risk for pheochromocytoma, retinal and CNS hemangioblastoma pancreatic cysts, neuroendocrine tumors, RCC High risk for pheochromocytoma

Genotype Truncating or missense variants that disrupt the folding of the protein Missense variant Missense variant

Missense variant

Pheochromocytoma Several other genes like RET, SDHA, SDHB, or TMEM127 are associated with pheochromocytoma. Patients with pheochromocytoma should be distinguished from multiple endocrine neoplasia type 2, hereditary paraganglioma-pheochromocytoma syndrome, and neurofibromatosis [122].

ment papillary cyst adenomas are symptomatic or threatening fertility, they require surgical treatment. Regular surveillance and early detection could prevent secondary complications, like neurologic symptoms, the demand for kidney transplantation besides hearing or sight loss [126]. To those vHL probands and at-risk relatives, regular surveillance includes regular assessment for neurologic symptoms, visual and auditory functions, blood pressure detection, plasma urine for fractionated metanephrines, thin-­ slice MRI in comparison with the internal auditory canal, abdominal ultrasound/MRI scan, and MRI of the brain and spine. Tobacco, chemicals and industrial toxins, and contact sports should have refrained as they have been regarded to damage vHL-involved organs [126]. Several innovative treatments target the upregulated signal pathways like VEGF receptor inhibitors (ranibizumab and bevacizumab) have been applied in patients to treat retinal and CNS hemangioblastomas [127]. Sunitinib, a tyrosine kinase inhibitor, has also been demonstrated to effectively treat RCC.  Preclinical investigation of premature termination codon 124 (PTC124) effects is ongoing.

RCC Patients with RCC and/or family history may be considered 43.3.2.8 Genetic Counseling as hereditary leiomyomatosis and renal cell cancer and Birt-­ VHL syndrome is inherited by autosomal dominance. About 79% of probands have an affected parent and others resulted Hogg-­Dubé (BHD) syndrome. from a de novo vHL gene variant [128]. There is also a report about mosaicism. The offspring of vHL syndrome patients 43.3.2.7 Management The major cause of mortality was attributable to CNS heman- are at a 50% risk. Prenatal testing like a preimplantation gioblastoma and RCC.  In recent years, owing to the early genetic diagnosis for a pregnancy in danger is necessary for diagnosis, recognition of tumors, and multidisciplinary man- the presence of pathogenic variant. If the pathogenic variant is identified with certainty, molecular genetic testing can be agement, the risks have been significantly decreased. Most CNS hemangioblastomas can be surgically removed used to screen the genetic status within high-risk families to and some asymptomatic lesions can follow with yearly imag- determine if the follow-up monitoring is needed. ing studies. Surgical treatment was approved to be relatively safe and effective. Retinal hemangioblastomas except for optic nerve are suggested to be applied with prospective 43.3.3 Retinal Vasculopathy with Cerebral Leukodystrophy treatment including diathermy, xenon, laser, and cryocoagulation, in order to avoid progressively blindness [125]. Compared with the ophthalmoscopy and conventional angi- 43.3.3.1 Overview ography, ultra-widefield fluorescein angiography is more Retinal vasculopathy with cerebral leukodystrophy (RVCL) (OMIM #192315) was primarily referred to in various names useful in the evaluation of retinal hemangioblastoma. Early surgery is the better option for large lesions of RCC, (known as hereditary vascular retinopathy, hereditary endowhile closely monitoring and cryoablation is suitable for theliopathy, and cerebroretinal vasculopathy, retinopathy, small lesions. Individuals who undergo the necessary bilat- nephropathy, and stroke), and was considered to represent eral nephrectomy should receive renal transplantation. different neurovascular syndromes. Nowadays, owing to the Pancreatic cysts and neuroendocrine tumors are usually not identification of pathogenic heterozygous C-terminal framehormonally active and have no malignant behavior. Close shift mutations in TREX1 gene, it unified these three synmonitoring is needed and surgical removal is necessary for a dromes into one single autosomal dominant disorder [129]. high risk of metastasis. Then vestibular function of individu- Due to systemic vascular involvement, RVCL is regarded as als with small endolymphatic sac tumors can be protected by a neurovascular syndrome. It affects the microvessels of the early intervention [122]. When epididymal or broad liga- eyes, brain, kidneys, and liver.

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43.3.3.2 Clinical Appearance RVCL means a small vessel vasculopathy, which especially involved the retina and brain, resulting in CNS degeneration including progressive vision loss, motor damage, cognitive decline, and stroke. Vascular retinopathy and brain lesions usually related to progressive visual impairment and progressive neurological function impairment, respectively, were the most diagnostic disease-specific discovery [130]. The retinal vasculopathy is featured by retinal capillary occlusion beginning in the macula, retinal microangiopathy, retinal hemorrhages, areas of capillary non-perfusion, and even occlusion of large retinal vessels leading to a wide range of avascular areas, which could induce a neurovascular response [130]. In the early stages, retinopathy was presented with telangiectasias, micro-aneurysms, and cotton wool spots (edema, swelling, and degeneration of the nerve fibers). In the later stages, it is manifested by perifoveal capillary obliteration and neovascularization. The findings from histopathological examination of the retina were in accordance with scattered microinfarcts, including the thickened hyalinized walls of the retinal arteries and focal areas of disruption of the ganglion cells and inner nuclear layer of the retina besides vascular changes [131]. Also, some lesions may progress to retinal hemorrhage and neovascularization. The retinopathy cause symptoms of a gradual decrease of visual acuity, transient visual disturbances, and visual field defects in adulthood. Later, especially around 60 years old, most patients suffer from severe visual impairment and even blindness. The other predominant features are cerebral white matter and contrast-enhancing mass lesions, which cause focal and global neuropsychiatric symptoms, progressive neurological decline, and even premature death. The cause of death primarily results from the complications of neurological decline. Symptoms of brain injury included focal neurological deficits, migraine, seizures, psychiatric disturbances, and cognitive impairment [132]. The findings of neuroimaging revealed punctate, hyperintense, rim-enhancing mass white matter lesions with calcifications or nodular enhancement [133]. Substantial numbers of affected individuals have Raynaud’s phenomenon, micronodular cirrhosis, and glomerular dysfunction, indicating the involvement of systemic vessels. These phenotypes are more common in RVCL patients than the major population, showing that are also belong to the clinical spectrum [118]. Systemic features involved liver disease, nephropathy, anemia, hypertension, mild Raynaud’s phenomenon and gastrointestinal bleeding. 43.3.3.3 Examination Neuroimaging studies including brain MRI or CT show contrast-­enhancing lesions in the white matter of the cerebrum and cerebellum suggested abnormal vascular permea-

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bility [133]. Studies on histopathology showed ischemic necrosis with minimal inflammation, luminal narrowing, and multi-laminated basement membranes. The lesions have prominent small infarcts in the white matter of the brain, which can be combined to form pseudotumors. Fluorescein angiography can be used to detect retinal abnormalities even in asymptomatic family members. When patients presented with symptoms mainly during later stages of the disease, positive manifestations included block of branches of large retinal arteries, avascular areas in the retinal periphery, and sometimes proliferative retinopathy with extensive avascular areas, even near the optic disc [132]. Other laboratory examinations like liver function (serum levels of gamma-glutamyltranspeptidase, alkaline phosphatase, transaminases, etc.) and renal function (urine protein, serum creatinine, etc.) is helpful for early detection of systemic lesions [134]. Studies of histopathology in biopsy and autopsy have reported that nodular regenerative hyperplasia was the predominant feature when the lesions affected the liver. Other findings included micro- and macro-vesicular steatosis, periportal inflammation, and portal and bridging fibrosis were also reported. Pathologic examination of the kidney revealed that renal arteriolosclerosis, arteriolonephrosclerosis, and focal or diffuse glomerulosclerosis. Identification of a heterozygous germline pathogenic variant in TREX1 gene through genetic testing approaches include single-gene testing, multigene panel, whole-exome/ genome sequencing [129].

43.3.3.4 Diagnosis The relatively non-specific clinical and imaging findings lead to the challenging of diagnosis. Stam AH, and colleagues show that the combination of vascular retinopathy, neurological decline, positive family history related to the described white matter lesions on brain MRI should prompt clinical monitoring of the syndrome and genetic detection [135]. To aid the clinical diagnose of RVCL, they proposed operational diagnostic criteria (Table  43.6). Demonstration of C-terminal frameshift variants in TREX1 gene would confirm the diagnosis. TREX1 (three prime repair exonuclease 1) encodes a 3′-5’ DNA exonuclease involved in clearing cytosolic nucleic acids, which plays an important role in multistep processes of DNA replication, repair, and recombination that required the excision of nucleotides from DNA 3-prime termini [136]. Variants may impair DNA degradation and homeostasis causing the disruption of cell death mechanisms. 43.3.3.5 Genotype–Phenotype Correlations Mutations in TREX1 gene are associated with four different, although overlaying, clinical phenotypes. The molecular pathology of RVCL seems different from that of Aicardi-­ Goutieres syndrome (AGS), familial chilblain lupus (FCL),

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Table 43.6  Proposed diagnostic criteria of RVCL Strength of evidence Major diagnostic criteria

Supportive features Possibly associated features

Description Vascular retinopathy Features of focal and/or global brain dysfunction associated on MRI with typical lesions Positive family history Identification of a heterozygous C-terminal frameshift variant in TREX1 gene CT/MRI indicated white matter lesions Microvascular liver disease Microvascular kidney disease Anemia consistent with blood loss and/or chronic disease Microscopic gastrointestinal bleeding Hypertension Migraine Raynaud’s phenomenon

and systemic lupus erythematosus (SLE) [131]. Although white matter brain disease and intracranial calcification can be presented in both AGS and RVCL, these diseases are clinically different. The heterozygous p.D200N and p.D18N variants are respectively associated with AGS1 and CHBL with the evidence of exonuclease enzyme analysis. Studies showed that either p.D200N or p.D18N mutant proteins entirely lacked for degrading dsDNA and degraded ssDNA that were about 2 times lower than wild-type protein. Besides, the mutant proteins suppressed the dsDNA degradation activity of wild-type TREX1, supporting an interpretation for the dominant phenotype [130]. Contrarily, the homozygous p.R114H mutation was reported to cause AGS while heterozygous p.R114H mutation was reported to cause SLE, leading to the dysfunctional dsDNA and ssDNA degradation activities. The p.R114H homodimer lacked inhibitory activity against wild-type protein, indicating the recessive inheritance mode of the mutation in AGS. Frameshift mutations in the C-terminus of the gene are considered to be related to SLE, and a C terminus frameshift mutation are discovered in patients with typical AGS. Loss of function recessive mutations in TREX1 gene are related to AGS and SLE [129]. Mutant proteins of p.Asp18 and p.Asp200 residues, were reported to result in skin and neurological phenotypes compatible with AGS or FCL. Mutations in TREX1 gene, which are associated with AGS, FCL, and SLE, appear to share a common pathological mechanism of the induction of a type I interferon response [137]. Contrary to the mutations presented in AGS, FCL and SLE, RVCL-associated TREX1 gene variants have been reported exclusively to develop as frameshift mutations in the C-terminus. Mutations in TREX1 gene, which are associated with RVCL, may result in altered cellular distribution with pathological mechanism of gain-of-function or toxic effect [134].

43.3.3.6 Differential Diagnosis RVCL is a rare disorder, which is difficult to diagnose due to the systemic involvement of complex manifestations and radiological findings. Young adults with systemic microangiopathy involving the brain, retina, kidney, and other organs should be suspected. The cerebral lesions observed in patients with RVCL may be mistaken for a tumor or multiple sclerosis. Thus, RVCL sometimes could be misdiagnosed, which leads to unnecessary diagnostic procedures such as biopsies [125]. Regarding certain brain MRI researches, these white matter lesions may cause neurologic phenotypes. This situation should remind physicians of RVCL in the differential diagnosis of demyelinating lesions associated with multiple sclerosis or certain gliomas. The results of genetic tests can be helpful in differential diagnosis. 43.3.3.7 Management Due to the rarity of disease reports, there are no unified guidelines for management. The main management is focused on the treatment of manifestations. So far, no effective treatments for the vascular retinopathy associated with the TREX1 gene variants have been reported. Raynaud’s phenomenon is usually asymptomatic without treatment. Several studies have presented the affected individuals who received different medical treatments. At present, it is not clear which way can treat these kinds of tumor-like lesions optimally. DiFrancesco and colleagues reported one case treated with immunosuppression did not receive substantial improvement [129]. Although they also applied cyclophosphamide in the course, further information was absent. The combination of rituximab and administration of intravenous cyclophosphamide monthly has also successfully applied in a patient with RVCL and the clinical course was reported to be apparently stable during the 24-month follow-up period. One report suggested that natalizumab may be beneficial for RVCL patients while no documented data was recorded. The management of leukodystrophies caused by other etiologies is based on different approaches, including specific therapies, symptomatic therapies like hematopoietic stem cell transplantation, and supportive therapies like occupational physical and speech training. Specific therapies have been increasingly developed for the metabolic leukodystrophies and the life expectancy and quality of patients have been shown to be significantly improved. Also, a study proposed that plasma exchange, intravenous immunoglobulin, or chronic immunosuppression has no benefit, and a healthy lifestyle and prevention of related risk factors for cerebrovascular disease may delay the onset of age and severity of the disease. The evaluation of these therapeutic options of these RVCL patients is needed in a longitudinal follow-up.

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43.3.3.8 Genetic Counseling RVCL is inherited in an autosomal dominant manner with plausible 100% of penetrance [122]. There has been no case report of RVCL cases caused by compound heterozygous or homozygous variants in TREX1 gene. Each offspring from RVCL patients is at a 50% risk of inheriting the pathogenic variant of TREX1 gene and being affected. Prenatal diagnosis and preimplantation genetic diagnosis of high-risk pregnancies are possible when pathogenic variants of TREX1 gene are found in affected family members.

43.3.4 Familial Exudative Vitreoretinopathy 43.3.4.1 Overview Familial exudative vitreoretinopathy (FEVR) is a hereditary retinal disorder, which is characterized by the defective development of the retinal vasculature. To date, seven genes have been reported to be responsible for this disease, including FZD4, NDP, EVR3, LRP5, TSPAN12, CTNNB1, and ZNF408 genes [117]. Its expressivity varies greatly even within families, from asymptomatic to severe. Severely affected patients usually manifested as blind in infancy, while mildly affected patients may present with few visual problems, which can only be seen through fluorescein angiography that there may be non-vascular areas around the retina. This peripheral avascularity is the main abnormality in FEVR patients and is the result of incomplete retinal angiogenesis. In the past, it was generally considered a rare disease, because the clinical status was determined by visual impairment or other clinical symptoms. Now, it has been found that penetrance is 100% in view of the fact that all affected individuals have the same characteristics from fluorescein angiography [122]. Thus, the frequency of FEVR was likely to be underestimated. 43.3.4.2 Clinical Appearance FEVR is characterized by the absence of peri-retinal blood vessels. The main pathology is considered as a premature arrest of retinal angiogenesis or abnormality of retinal vascular differentiation, leading to incomplete peripheral retinal vessels. Shukla and colleagues reported that affected individuals who have normal vision were at the most mildest spectrum with only an asymptomatic area of peripheral retinal avascularity. Retinal exudates, retinal traction, and even developed with rhegmatogenous retinal detachment were found in some FEVR patients. Severe phenotypes, including reduced vision, strabismus, and leukocoria, usually occur in the first decade of life. In the most serious situations, blindness occurred in affected infants shortly after birth [122]. Thus, this area of retinal avascularity is believed to exist at birth and to have further sequelae that may stabilize in early adulthood and/or later life.

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Studies have found that the failure of peripheral retinal vascularization is unifying common feature presented in all patients. However, it usually does not cause any clinical symptoms by itself. The resulting visual problems and other phenotypes caused by retinal ischemia such as the development of hyperpermeable blood vessels, neovascularization, vitreoretinal traction, and falciform retinal folds. These complications lead to visual impairment and, in some cases, cause partial or total retinal detachment [118]. Rare retinal features included peripheral brush margin anastomosis, peripheral fibroangioma, retinal hiatus, exudation, retinoschisis, and giant retinal tears have also been described. Hence, the severity of the disorder is associated with the complications. Expressivity, accordingly, may be asymmetric and vary greatly from mild (absence of symptoms) to severe (early onset of blindness). Severely affected individuals manifest clinical symptoms in the first decade of life, while mild patients may receive the diagnosis of FEVR through the professional ophthalmological examination without any discomfort [138]. Other features included low bone mineral density and susceptibility to fracture. These osteological features are more frequent in the affected individuals with pathogenic variants in LRP5 gene, and bone mass is reported to have decreased in these patients [138]. While preliminary studies of FEVR patients indicated that these findings are not observed in patients with FZD4 gene pathogenic variants or other forms of FEVR.

43.3.4.3 Examination The manifestations of FEVR patients are somehow similar to those of other vitreoretinal diseases in children and adults. Thus, careful examination is the key to distinguish FEVR from other diseases. Full ophthalmic examinations are essential in suspected individuals, including the measurement of visual acuity, examination of slit lamp, and fundus fluorescein angiography. The findings of retinal avascularity may be missed but they are easier to be detected with the help of the indirect ophthalmoscope and scleral indentation. Based on ophthalmoscopic findings, the severity of FEVR could be categorized into the following five stages [122], which has been summarized in Table 43.7. The pathogenic changes of retinal vessels can be observed more vividly with fundus fluorescein angiography. And, a study suggested that the application of fundus fluorescein angiography can detect 100% of FEVR patients. The classic features from fundus fluorescein angiogram would show the lesions with no blood vessel area around retina, dilated and truncated capillaries with leakage of fluorescein dye, and the preserved straightened retinal vessels near the avascular zone. In severe cases, it may be observed the extensive vit-

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Table 43.7  Clinical classification of FEVR Stage 1 2 3 4 5

Ophthalmoscopic findings Avascular areas in the retinal periphery without extraretinal vascularization Avascular areas in the retinal periphery with extra-retinal vascularization, and with/without exudate Subtotal retinal detachment (primarily exudative, primarily tractional), not involving fovea Subtotal retinal detachment (primarily exudative, primarily tractional), involving fovea Total retinal detachment

reoretinal traction, retinal fold, neovascularization, and other retinal features mentioned before. Germline pathogenic variants in one of the seven disease causing genes (FZD4, NDP, EVR3, LRP5, TSPAN12, CTNNB1, and ZNF408) can be identified by genetic testing methods including single-gene testing, multi-gene panel, whole-exome/genome sequencing. In a cohort of Chinese FEVR pedigrees, the study analyzed six FEVR-associated genes and identified variants including 16.1% in LRP5 gene, 9.7% in NDP gene, 6.5% in FZD4 gene, and 3.2% in TSPAN12 gene [135].

43.3.4.4 Diagnosis The diagnosis of FEVR is established in patients with the typical clinical manifestations and/or by identification of germline pathogenic variants in FEVR-related genes through molecular genetic testing, which is compatible with the proband’s family history. The main clinical evidence is the examinational findings (i.e., bilateral peripheral retinal avascularity) from fundus fluorescein angiography, or the indirect ophthalmoscope. When a proband with or without a family history of FEVR who have suspected manifestations should be performed genetic testing. Pathogenic variants in one of seven genes (FZD4, NDP, EVR3, LRP5, TSPAN12, CTNNB1, and ZNF408) are known to be associated with FEVR [122]. Due to the various inheritance mode of these genes, FEVR can be inherited by autosomal dominant (the most common), autosomal recessive, or X-linked. Thus, gene panel or whole-­ exome sequencing would be the better option to order. Among the seven genes, most of them encode proteins as part of the norrin/beta-catenin signaling pathway, in which the ligand (norrin) binds to receptor complexes composed of frizzled-4, co-receptor low-density lipoprotein receptor-­ related protein-5 and auxiliary protein-12. Variants may impact the expression and/or function of the associated encoded protein, leading to reduction of the binding affinity of frizzled-4. In the absence of norrin binding, the activation of this signaling pathway is arrested or the transduction of β-catenin signaling is impaired, which resulting in phosphorylation of cytoplasmic β-catenin and directional degradation through ubiquitin-proteasome pathway [139]. However,

pathogenic variants in these genes are responsible for only part of the FEVR patients, there remain unknown genes associated with FEVR to be identified. Thus, failure to identify pathogenic variants in these seven genes does not rule out the diagnosis.

43.3.4.5 Genotype–Phenotype Correlations Several studies indicated that affected individuals with FEVR who carried pathogenic variants within LRP5 gene might show signs of osteoporosis on a dual X-ray absorptiometer more frequently than those who carried variants in other genes [122]. One study suggested that there existed synergistic action between the FZD4 and the LRP5 pathogenic variant in the FEVR phenotypes, due to the findings that patients with pathogenic variants in both genes in cis had more severe phenotypes than patients with the same FZD4 pathogenic variant only. The affected individuals who carried the LRP5 gene variants showed broader phenotypic spectra that varied from stage 2 to stage 5. While patients carrying NDP gene variants were correlated with severe phenotypes, which may be stage 4 or above. It is controversial that the pathogenic missense variants in the C-terminus region of NDP gene may be related to the milder phenotypes of FEVR [138]. 43.3.4.6 Differential Diagnosis Retinopathy of Prematurity Retinopathy of Prematurity (ROP) is also characterized by the failure of the peripheral retinal system development. The manifestations of retinal neovascularization and cicatricial sequelae, which are similar to those of affected individuals with FEVR, mainly occur in premature infants due to premature delivery before retinal vascularization is completed. As a sporadic disorder, a history of premature birth and a lack of family history are helpful in the differential diagnosis. Retinal neovascularization and scar sequelae are similar to those of affected individuals with FEVR. Coats Disease Coats disease is characterized by severe retinal telangiectasia, retinal exudates, and retinal detachment that resembles FEVR phenotypes. Most affected the unilateral eye and were predominantly common in male children. Fundus fluorescein angiography could detect abnormal vessels presented throughout the fundus. Persistent Hyperplastic Primary Vitreous Persistent Hyperplastic Primary Vitreous (PHPV), known as “persistent fetal vasculature, is a developmental eye malformation, which is characterized by the retrolental fibrovascular membranes. It is attributed to the failure of primary vitreous degeneration in utero [122]. This abnormality is

43  Prenatal Diagnosis and Preimplantation Genetic Diagnosis

usually unilateral and observed in full-term infants, companied by microphthalmia, cataract, glaucoma, and congenital retinal nonattachment. Microphthalmia are more common compared with FEVR. Norrie Disease Norrie Disease (ND) is a known X-linked recessive condition characterized by early blindness in childhood due to degenerative and proliferative changes of the neuroretina. Retinal findings of this disorder can mimic the manifestations of FEVR. Patients with Norrie disease may also present with progressive mental features, sensorineural deafness, growth failure, seizures, etc. Funduscopic examination is helpful in distinguishing between the two disorders. Toxocariasis As an acquired disorder, patients with toxocariasis usually have an infectious history of the roundworm Toxocara canis. In certain forms, it may present with retinal traction and retinal fold due to the development of a peripheral granulomatous mass.

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regression and the accelerated fibrosis, indicating it can be used as an alternative treatment for FEVR [141]. The long-­ term evaluation of new therapies remains to be confirmed with more data. Individuals who are at high risk such as have a positive family history should be carried out a routine ophthalmological examinations to assess the development of retinal exudates, neovascularization, and traction. Regular surveillance should be performed as follows [122]: (i) ophthalmologic examination, such as indirect ophthalmoscopy; (ii) fundus fluorescein angiography, especially in those asymptomatic affected individuals, which may improve the detection rate of peripheral avascularity. The frequency of the follow-up depends on the assessment of the professional ophthalmologists.

43.3.4.8 Genetic Counseling Because of the various inheritance mode of FEVR (i.e., autosomal dominant, autosomal recessive, and X-linked manners) and the difficulty of distinguishing by clinical ophthalmologic examination, the genetic counseling is more complicated. If it is inherited in an autosomal dominant mode, the off43.3.4.7 Management In spite of the presence of retinal avascularity, patients may spring of affected individuals have a 50% risk of inheriting not manifest any symptoms, and in general, did not receive the pathogenic variant. The situation of asymptomatic inditreatment. However, it has the risk of causing retinal periph- viduals with FEVR needs to be cautious. In an autosomal ery lesions including ischemia and neovascularization. These recessive manner, the parents of a proband are at a 25% risk lesions could be cured by preventive cryotherapy or argon of having another affected child, an even chance to have a laser photocoagulation, which have the potential to induce carrier child [122]. When it is inherited in an X-linked manregression of the new vessels. The application of the above-­ ner, the pathogenic variant would be passed from the male mentioned techniques is also beneficial to prevent retinal patients to all their potential carrier daughters. Carrier detachment, which may result from retinal exudate or hiatus. females have an even chance to transmit the pathogenic variHowever, the prognosis of exudative retinal detachments is ant to boys who would be affected, or daughters who would reported to be poor [140]. Rhegmatogenous retinal detach- be carriers. If the pathogenic variants have been identified in ments, which are derived from retinal traction, can be the pedigrees, it is possible to carry out carrier testing, prenarepaired by conventional surgery with satisfactory prognosis. tal pregnancy detection, and preimplantation genetic diagnoCurrent methods do not prevent peripheral retinal avascular sis for their high-risk relatives. arcs, which are believed to be abnormal in retinal development. Although the long-term follow-up information is not 43.3.5 Typical Medical Case available yet, patients with FEVR (especially those who have pathogenic variants in LRP5 gene) presented with bone Clinical Background  The proband is a 15-month-old mineral density reduction may be expected to benefit from Chinese boy, born at 40-week gestation with a birth weight medicine like bisphosphonates and calcium agent used to of 3600  g via cesarean section. The proband was the first treat osteoporosis in order to prevent secondary complica- child of the couple. Neonatal clinical examination at the local hospital did not show any abnormalities. At 3 months tions [122]. Vascular endothelial growth factor (VEGF) inhibitors of age, his parents noticed that his eyes did not react to light. have been widely applied in acquired retinal diseases with At 9 months of age, he had developmental delay and mild neovascularization or exudation components. Some reviews thumb adduction and hypertonia of the extremities. suggested this kind of treatment may be beneficial for those patients with FEVR. Also, several studies reported the appli- Physical Examination (at the Age of 15 Months)  His head cation of intravitreal injection of bevacizumab acquired circumference was 44.0 cm (< −2SD). He had low-set ears, change of neovascularized tissue, which benefits from rapid a high palate arch, and hypertonia, He still had no reaction to

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Fig. 43.11  Sequencing traces of the pedigree. Left side showed the results of Sanger sequencing. Right side showed the family tree

the light and appeared to have a complete lack of vision. He could raise his head but still could not sit or walk without support. No language development was found. Ophthalmologic Examination  Results of an ultrasound of the eyes showed a retinal detachment and lens and vitreous opacities in both eyes. Intraocular pressures were as follows: oculus dexter: 15.3   mm Hg; oculus sinister: 14.7   mm Hg. Falciform retinal folds and fundus hemorrhages were also reported. Fundus fluorescein angiography in both eyes was normal. Ultrasound examination of the eyes at 4 and 7 months revealed persistent retinal detachments in both eyes. Magnetic resonance imaging of the proband’s brain, electroencephalogram, abdominal ultrasound, and hearing examination revealed no abnormalities. His genetic/metabolic workup, including karyotype, liver function tests, alkaline phosphatase, glucose, lactate, electrolytes, and urinalysis were all normal. Genetic Results  The proband was born from physically healthy and non-consanguineous parents, without a positive family history. Combined with the clinical features, familial exudative vitreoretinopathy was initially suspected. Whole-­ exome sequencing was performed. The variants were filtered and classified according to the guidelines recommended by the American College of Medical Genetics and Genomics. Clinical features of developmental delay, ophthalmic diseases, and microcephaly subsequently served as indexes to analyze the candidate variants. The result revealed a novel heterozygous nonsense variation (c.1627C>T, p.Gln558*) in exon 11 of the CTNNB1 gene was identified in the proband, which was confirmed by Sanger sequencing. The Sanger sequencing results confirmed the presence of the CTNNB1 gene variant in the proband and manifested that the variant was de novo (Fig. 43.11).

Follow-Up  The proband was followed up to 3.5 years old. He still could not walk alone or speak. Although the option of surgery was proposed by the clinician, his parents refused the suggestion due to private reasons. The proband was blindness and even showed leukocoria. Although the variant was supposed to be de novo and the risk to the sibs of the proband appears to be low, we should still be cautious because of the possibility of germline mosaicism. When his mother was pregnant again, we recommended prenatal testing of amniotic fluid DNA, and the result showed that the fetus does not carry the mutation. As far as we know, the second child is 3-month old and no abnormalities have been found. The case was from Shanghai Children’s Medical Center.

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Transplant Matching

44

Binwu Ying and Lijuan Wu

(now the West China Hospital of Sichuan University). After the 1980s, the efficacy of organ transplantation was greatly improved due to advances in surgical techniques, improvements in preservation methods, development of high-speed transportation, establishment of transplant centers, and especially the use of new immunosuppressive agents with few side effects and strong potency. Currently used transplant organs include kidney, heart, liver, pancreas and islets, parathyroid glands, cardiopulmonary, bone marrow, and cornea; in the initial clinical or experimental stage, there are heart, lung, small intestine, adrenal gland, thymus, testis as well as 44.1 Overview liver cells, fetal liver cells, spleen cells Infusion, etc. The transplant immune response is an immune response Transplantation is a technology to transfer cells, tissues, or against the graft antigen, and involves both humoral and celorgans in the same individual or to another individual by sur- lular immunity, and has a strong correlation with natural gery or other methods which would replace dysfunctional immunity. In hyperacute rejection, the main cause of graft loss cells, tissues, or organs. An individual who donates a trans- is humoral immunity, while in acute rejection, cellular immuplant, is called a donor and the person who receives a trans- nity is predominant. Due to the characteristics of organ transplant, is called a recipient. According to the relationship plantation itself, dangerous signal molecules such as HMGB1, between the donor and the recipient, it can be divided into which are caused by surgical wounds, can activate APC and autologous transplantation, that is, the donor and recipient of other cells that participate in both natural and acquired immuthe organ are the same person; homologous transplantation, nity, and accelerate the process of immune rejection. In addithat is, the donor and the recipient are not the same person, tion to the recipient’s immune cells, the donor’s immune cells but the donor (i.e. the identical twin) has the same genetic are also involved in the transplant immune response, and the material; allogeneic transplantation, that is, human-to-human donor’s APC can activate the same reactive T cells in the reciptransplantation; xenotransplantation, that is, transplantation ient through a direct recognition mechanism, causing a strong between different species of animals. According to the dif- rejection. According to different subjects of immune rejection, ferent grafts, they are divided into cells, tissues, and organs. allograft rejection includes host versus graft reaction (HVGR) Transplantation does not include the use of synthetic poly- and graft versus host reaction (GVHR). Depending on the meric materials in the body. Transplant medicine is a cutting-­ degree of tissue compatibility between the graft and the host, edge discipline. The earliest established Institute in China of as well as the immune status of the recipient, transplant rejecOrgan Transplantation of Wuhan Medical College was offi- tion is mainly manifested in three different types. cially established in 1980, then, the establishment of the transplant center was Zhejiang Medical University, Zhong Shan Medical University, and Huaxi Medical University 44.1.1 Hyperacute Rejection Organ transplantation is one of the most effective methods to solve end-stage organ failure. Transplant matching is an important factor affecting the success and long-term survival of organ transplantation. Since the waiting list for organ transplants is growing, improving the survival rate of organs in  vivo and prolonging its survival time could markedly relieve the demand for organs. Good transplant matching is vital for the later survival of the transplanted organs with important clinical significance.

B. Ying (*) · L. Wu Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China

The hyperacute rejection reaction typically occurs 24 hours after transplantation. It is currently believed that this r­ ejection is mainly caused by antibodies to the ABO blood group anti-

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_44

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body or to the class I major histocompatibility antigen. This antibody binds to the vascular endothelial cells of the transplanted kidney, directly destroys the target cells by activating complement, or a variety of complement cleavage fragments generated during complement activation, resulting in platelet aggregation, neutrophil infiltration, and activation of the coagulation system. It eventually leads to severe ischemia and graft necrosis. Once hyperacute rejection occurs, it will lead to graft failure. Therefore, appropriate donors are selected by ABO and HLA matching before transplantation to prevent the occurrence of hyperacute rejection.

44.1.2 Acute Rejection Acute rejection is one of the most common types of rejection [1], usually occurring within days to months after transplantation, and progresses rapidly. The cellular immune response is the main cause of acute rejection of transplants, and CD4+ T cells and CD8+ T cells are the main effector cells. Even with the application of human leukocyte antigen (HLA) matching and immunosuppressive drugs before transplantation, 30% to 50% of transplant recipients will develop acute rejection. Most acute rejection can be alleviated by the use of effective immunosuppressive agents.

44.1.3 Chronic Rejection Chronic rejection usually occurs several months to several years after organ transplantation [2]. The main pathological feature is the proliferation of vascular endothelial cells in transplanted organs, narrowing the arterial lumen, and gradual fibrosis. Chronic immune inflammatory response is the main cause of the above-mentioned histopathological changes. There are currently no ideal treatments for chronic rejection [1–3].

44.2 HLA and Transplantation Matching 44.2.1 Overview The human major histocompatibility complex (MHC) (Fig. 44.1), also known as HLA, is located on the short arm of chromosome 6, and is the main gene system that regulates the human-specific immune response and determines the individual susceptibility to disease [4]. The corresponding DNA is about 3500 kb long. According to the function and product structure, they are divided into three groups: classical HLA gene, immune function-related genes, and immune-­independent genes. It is closely related to blood transfusion and acute rejection of transplantation, and is

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also familiar in the traditional sense, namely the first group of classical HLA genes. Classical HLA genes often show a high degree of polymorphism, that is, most of the loci have a large number of alleles, and are directly involved in the regulation of the human immune response, and are divided into HLA-I genes and HLA-II genes [4, 5]. HLA-I genes include A, B, C, and other loci, located at the far end of the HLA gene complex, and the product is called HLA-I molecule or class I antigen, which the heavy chain encoded by each gene locus and the light β2-M encoded by the non-HLA gene of chromosome 15 are combined. The HLA-II gene is located at the near-tip point of the complex and is at least divided into three sub-­ regions of DR, DQ, and DP. The product is called HLA-II molecule or class II antigen that is a heterodimer composed of an α chain (35 kD) and a β chain (28 kD), both of which are encoded by a class II gene. HLA-II genes are more complex in their number and composition than class I genes.

44.2.2 Genetic Characteristics of HLA 44.2.2.1 Phenotype, Monotype, and Genotype The HLA antigen is encoded by the alleles on the chromosome, and an individual’s HLA antigen specificity can usually be detected by a known typing reagent or a committed cell, and the antigen-specific type detected by this method is called a phenotype. However, the antigenic phenotype does not reflect the pattern of allele combinations on the chromosome of the individual. The combination of HLA alleles on a single chromosome is called monotype or haplotype. If this combination is extended from class I and class II genes to class III genes, it is often called extended type (extended haplotype). The HLA genotype of an individual consisting of two monotypes, i.e., the pattern of HLA allele combinations on the two chromosomes within the individual. Monotypes and genotypes can only be determined by phenotypic analysis of individual members of the family. The phenotype of each individual can be determined in a variety of combinations, i.e., different genotypes. Understanding the individual’s monotype and genotype is important in allogeneic organ transplantation and forensic paternity testing. 44.2.2.2 HLA Genetic Model Monotype Inheritance According to the HLA phenotypic analysis of each member of the family, the HLA inheritance pattern is transmitted from the parent to the offspring in a single unit, which is characterized by linkage inheritance. The offspring can randomly obtain an HLA monotype from each of the parental counterparts to form a new genotype of the progeny. Among

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2. DNA Denaturation, neutralization(10 min).

3. Adding magnetic beads.

4. Hybridization(60°C, 15 min).

8. Data collection.

1. Amplification (90 min).

5. Wash buffer → Oscillation → Centrifugation → Removing supernatant. 7. Wash buffer.

6. Adding SAPE (60°C, 5 min).

Fig. 44.1  Human major histocompatibility complex

the siblings in the same family, the probability of two monotypes being identical is 25%, the probability of a single type being the same is 50%, and the probability of two monotypes being completely different is 25%. Therefore, when selecting the appropriate donors and recipients for clinical allogeneic organ transplantation, it is much easier to find the donor and recipient HLA antigens in the family than the unrelated donors. However, it is worth noting that when the parental single type is passed to the offspring, an exchange may occur between the two types. This has been encountered in many cases in our previous practice, which should also be noted in HLA typing [4]. Codominant Inheritance The codominant inheritance refers to the antigen encoded by each pair of alleles expressed on the cell membrane, no recessive genes, and no allelic rejection. If the two HLA monotypes of a single body are different, there are two alleles at each HLA locus, and all are reflected in the phenotype.

Linkage Disequilibrium Linkage disequilibrium refers to the phenomenon of non-­ random distribution of single-type genes, so some genes (A1-B8 in whites, A2-B46 in southern China) always appear together, and their single-type frequency (actual value) is significantly higher than the theoretical value (for the product of various allele frequencies, such as the gene frequency of the A1 gene frequency × B8), while others are less frequently present. This non-free combination phenomenon is called linkage disequilibrium (Fig. 44.2).

44.3 Tissue Matching Technology and Related Experiments 44.3.1 HLA Typing Organ transplantation is an important means of clinical treatment of end-stage organ failure and saving patients’ lives. With the improvement of organ transplantation technology,

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Fig. 44.2 Experimental analysis path of organ transplantation matching

the progress of basic research on transplantation immunity, and the rational application of immunosuppressive drugs, the success rate of organ transplantation and graft survival rate is significantly improved. A large number of studies have shown that human leukocyte antigen (HLA) matching is one of the important factors affecting rejection and graft survival. In the early 1960s, Snell and Dausset and colleagues discovered the immunogenetic histocompatibility antigen system and found an antiserum source that recognizes HLA antigens. Later, Rood and Leeuwen applied computer methods to analyze the complex components of HLA antiserum and proposed the concept of alleles. On this basis, Terasaki invented micro-complement-dependent cytotoxicity (CDC) experiment and applied it to HLA serological typing, thereby achieving standardization of HLA typing and promoting the rapid development of HLA serological research. CDC method becomes an important method and means of immunogenetics and histocompatibility research. In recent years, with the rapid development of molecular biology technology [6], it has provided new and effec-

tive means for HLA genotyping research, and the HLA typing technology has been rapidly developed. On the one hand, with the development and application of immunomagnetic beads and monoclonal antibody typing reagents for isolating and purifying T lymphocytes [7] and B lymphocytes [8], the traditional serological typing technology has been improved and developed. On the other hand, molecular biology techniques such as sequence-specific primer polymerase chain reaction (PCR-SSP), sequencespecific oligonucleotide probe hybridization (PCR-SSO), and gene chip technologies have been widely used in HLA typing research. Because DNA typing technology directly classifies HLA gene polymorphisms from the genetic level, the method is accurate and sensitive, and it can detect some genotypes that cannot be detected by serological methods. Therefore, the serological typing techniques have been replaced by genotyping techniques in many laboratories. However, serological typing technology is the basis and main means of HLA research, and many laboratories still use it as the main method of HLA-I class antigen typing.

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44.3.1.1 HLA Serological Typing Technology HLA-I antigens are expressed on both T lymphocytes and B lymphocytes, while HLA-II antigens are mainly expressed on B lymphocytes and activated T lymphocytes. Therefore, HLA class I antigen typing can be used directly. For lymphocytes, purified T lymphocytes can also be used, while HLA-II antigens can only be purified using purified B lymphocytes [9]. HLA serological typing mainly uses the principle of CDC technology. That is, the anti-HLA antibody can bind to the surface of the living lymphocyte membrane with the corresponding HLA antigen; in the presence of complement, the lymphocyte membrane is holed. If the lymphocyte membrane does not carry the corresponding HLA antigen, this effect is not obtained. Then there is no such effect. The dead lymphocytes that destroy the cell membrane can be observed and judged by various methods. The most commonly used method is the staining method, which is stained with eosin or cone blue in the early stage. Under the phase contrast microscope, the dead cells become larger due to the dye entering the human body. Living cells are not colored, and the refractive index is strong. Now ethidium bromide (EB) and acridine orange (AO) double staining is mainly used. Under the fluorescence phase contrast microscopy, dead cells become red fluorescence due to EB entering the nucleus and DNA binding. AO can quickly bind to lipids and pass the cell membrane lipid bilayer to enter the living cells and is mainly enriched in lysosomes in the cytoplasm, so that the viable cells can be labeled and green fluorescence is excited after excitation. The micro-lymphocyte toxicity test technique is an internationally accepted HLA typing standard technology designated by the National Institutes of Health (NIH). The traditional HLA typing serum is mainly from human serum and placental serum, and often has defects such as low specificity, strong cross-reaction, low titer, difficulty in obtaining some rare antibodies, complicated antibody screening, and difficult transportation and storage. In order to solve many problems in serological typing technology, in the late 1980s, Terasaki and colleagues began to develop HLA typing monoclonal antibodies to replace standard antiserum, and officially launched HLA monoclonal antibody typing plate reagents in 1992. Because monoclonal antibodies have the advantages of high specificity, high titer, and low cross-reactivity, the accuracy of typing is significantly improved. Therefore, the current clinical HLA serological typing is mainly based on monoclonal antibody technology. 44.3.1.2 HLA Genotyping Technology Since the discovery of the in  vitro gene amplification technology-­polymerase chain reaction (PCR) by Cetus in 1985, molecular biology technology has developed rapidly. At the same time, more and more molecular biology techniques have been applied to HLA typing research, making HLA research into a new stage of DNA genotyping, and the

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differences in many HLA alleles are confirmed at the nucleotide level. DNA-based HLA genotyping has higher resolution and accuracy than traditional serological typing methods. It is known that the genetic difference of HLA individuals is determined by the DNA molecules encoding the gene products. Therefore, in theory, using molecular biology techniques to perform donor–recipient matching at the DNA level can reduce rejection and improve graft survival. The rate will make more sense.

44.3.1.3 T  he Basis of HLA Genotyping– Polymerase Chain Reaction The Polymerase Chain Reaction (PCR) technique is mainly based on the semi-reserved replication mechanism of DNA in cell division in  vivo, and the nature of in  vitro DNA double-­stranded and single-strand transition at different temperatures, controlling the temperature of the in vitro synthesis system, and base-pairing the DNA template with the corresponding primers. In principle, specific binding occurs, and under the action of DNA polymerase, the primer strand can extend along the DNA template, thereby reproducing exactly the same product as the template DNA.  The entire amplification process is divided into three phases: ① DNA template denaturation. DNA double-stranded at 95 °C high temperature to form single-stranded DNA; ② annealing. During the temperature decrease, the primers added to the reaction system are complementary to the corresponding single strand of the template DNA; ③ extend. At the appropriate temperature and DNA polymerase, the primer extends from the 5′ end to the 3′ end along the template, and the newly synthesized primer strand can serve as a template for the next round of cycling and a new amplified fragment. The template DNA is denatured, annealed, and extended in multiple cycles of three stages to form a specific, uniform DNA fragment. These amplified DNA fragments of interest can be used for nucleotide sequence analysis, single-strand conformation polymorphism analysis, and restriction fragment length polymorphism analysis. 44.3.1.4 R  estriction Fragment Length Polymorphism Typing There are multiple restriction endonuclease sites in different parts of the HLA nucleotide base sequence, and the target gene is amplified by PCR, and then the amplification product is digested and cleaved with a set of restriction endonucleases. The cleavage site in the sequence produces DNA fragments of various lengths, which can be directly analyzed by gel electrophoresis, or transferred to a nitrocellulose filter to hybridize with the corresponding DNA probe, and subjected to autoradiography to analyze. Due to the different base sequences of different alleles, the restriction endonuclease sites have different distributions. Therefore, after digestion, the fragments can be produced in different numbers and

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lengths, and different DNA bands appear during electrophoresis. According to this, the specificity of the HLA gene can be identified. Restriction Fragment Length Polymorphism (RFLP) is the earliest method for HLA genotyping. It is cumbersome and time-consuming, and it is prone to incomplete restriction enzyme digestion of the target gene or the length of the fragment is similar. The difficulty of the analysis limits the wide application of this technology in the field of HLA typing.

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olution of the product is high; Specificity: specific primers designed for known allele sequences, which determine the specific amplification from the first cycle of PCR, unique amplification conditions (such as the two annealing temperatures, endogenous control primers, the use of primer pairs between the groups to determine the results, etc.) further improved the specificity; wide range of applications: according to the different uses and requirements of HLA genotyping, both low-resolution PCR-SSP typing primers can be designed, and medium-resolution and high-resolution PCR-­ 44.3.1.5 Single Strand Conformation SSP typing primers can be designed to meet different requirePolymorphism Typing ments. Different requirements for HLA genotyping in The PCR-SSCP typing technique was pioneered by Orita laboratory and transplant clinical; simple and rapid of the and colleagues. It is a method of analyzing a single-stranded results analysis: the amplification products of PCR-SSP DNA polymorphism based on single-stranded DNA obtained technology only need to be subjected to conventional agaby denaturation of a PCR amplification product by non-­ rose gel electrophoresis, and the results can be determined denaturing gel electrophoresis. The principle is that single-­ according to the specific amplification bands that appear. stranded DNA forms a certain spatial conformation when subjected to neutral polyacrylamide gel electrophoresis 44.3.1.7 PCR-Sequence Specific Oligonucleotide Probe Hybridization without denaturing agents. Single-stranded DNA of the same length can cause conformational differences due to differences in base sequence and even single bases, which in turn The Basic Principle of PCR-SSO causes different migration speeds and mobility. Through Using PCR technology, the target gene was amplified by PCR amplification, including the occurrence of a single base HLA allele or inter-group specific primers, and then hybridsubstitution site and DNA fragments on both sides, Single ized with the synthetic oligonucleotide probe. The probe was Strand Conformation Polymorphism (SSCP) analysis after hybridized with the PCR product under certain conditions, denaturation can distinguish the single base changes in the strictly following the principle of base complementation, target DNA, and effectively detect point mutations and DNA which has a high degree of specificity. The probe may be polymorphisms and detect new alleles. labeled with radionuclide (such as 32P) and detected by autoradiography, or may be labeled with nonradioactive 44.3.1.6 Sequence-Specific Primers Typing label (such as digoxin, biotin, and fluorescein) and the corThe PCR-SSP typing technique was first created by the responding markers can be detected. Swedish scientist Olerup and colleagues in the early 1990s. The molecular hybridization pattern can be divided into It was mainly used for HLA-II genotyping in the early stage forward hybridization and reverse hybridization. Forward and now has been widely used in HLA-I and HIA-II hybridization is the transfer of PCR amplification products to genotyping. solid phase carriers, such as cellulose nitrate membrane or According to the known nucleotide sequence polymor- nylon membrane, and the identification of amplification phism of HLA coding gene, a series of corresponding fragments is used by southern hybridization using H-specific sequence-specific primers (SSP) were designed, and the sequence-specific oligonucleotide probes (SSO). Reverse 3′-end base of the primer was strictly complementary to the hybridization, on the contrary, is to put the specific oligonupolymorphic sequence of the target gene. Therefore, each cleotide probes of each allele sequence of HLA on the same type of allele has a specific primer corresponding to it. A membrane, and then hybridize the product of site-specific or type-specific DNA fragment of each allele was amplified by intergroup-specific PCR amplification with biotin. After PCR, and a corresponding specific amplification product washing the membrane and detection, the hybridization sigband appeared on agarose gel electrophoresis. Homozygous nal is shown, so as to determine the allele type. Due to the produces a specific amplified band, and heterozygotes have high polymorphism of HLA allele, the number of SSO two specific amplified bands. Even a single base difference probes required is huge, and there is sequence sharing can be accurately distinguished and, therefore, can be used between alleles, so it will be very difficult to use forward for high-resolution HLA genotyping. SSP features: High hybridization technology for typing detection of a large samresolution: Each pair of primers is designed according to the ple size. Therefore, the reverse hybridization technology has principle of complementary bases of alleles, and only a spe- become the main clinical application for HLA typing cific nucleotide sequence fragment is amplified, and the res- detection.

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PCR-SSO Reverse Hybridization Typing Using reverse PCR-SSO gene hybridization technology, the unlabeled sequence-specific oligonucleotide (SSO) probes were spotted on the same membrane, and then the corresponding sites PCR products were mixed with bio-sand Hybridization. After membrane washing appropriately, coloration with avidin-labeled alkaline phosphatase and its substrate, coloration, and then judge the HLA allelic type of the sample to be tested according to the hybridization signal. This method is characterized by high efficiency, specificity, accuracy, and rapidity, and is suitable for genotyping with large sample size. Currently, a reverse PCR-SSO genotyping kit is available. Taking HLA-DRB genotyping reagent as an example, it mainly includes the steps of target gene amplification, gene hybridization, substrate color development, and result analysis. Firstly, the HLA-DRB1 gene with high polymorphism was amplified by primers, and the amplified fragment was 272 bp in length. After the amplification, the amplified product is denatured into a single strand, and added to a nylon membrane containing a sequence-specific oligonucleotide probe. The biotin-labeled amplification product is bound to the probe on the membrane to pass through the linkage. The avidin-horseradish peroxidase and its substrates H2O2 and

tetramethylbenzidine were developed, the probe hybridization position was recorded, the genotyping results were obtained by the corresponding analysis software. Flow Cytometry-SSO Typing Method Flow cytometry-SSO is an HLA typing technique that combines PCR reverse sequence-specific oligonucleotide probe technique with flow cytometry. The principle of Flow cytometry-­SSO is to use PCR technology to amplify the target gene with specific primers at HLA alleles or between groups, and then hybridize with oligonucleotide probes of each HLA gene attached to fluorescent labeled microparticle magnetic beads. Each microparticle magnetic bead is not only attached with specific HLA probes, but also color differences. Finally, the corresponding HLA alleles can be detected by laser in the liquid chip system of dedicated flow cytometry LABScanTM100, LuminexTM200, and so on (Fig. 44.3).

44.3.1.8 Gene Chip Typing Basic Principle Gene chip is a technology, which can not only detect normal DNA loci, but also detect certain genetic diseases and find Patient preparing for organ transplanation

ABO, Rh blood group

HLA genotypimg

HLA antibody

MIC antibody

Waiting for organ transplant registration form Establishing patient library

Organ donor

Conforming to the principle of blood transfusion; HLA antigen matching; No DSA

Blood group, HLA genotyping

Reselecting donor

Cross-matching of donor and recipient

Transplantation

Rejecting

Regular monitoring after Transplantation

HLA antibody

MIC antibody

Pathogen

Drug concentration monitoring

Fig. 44.3  Experiment operation process of flow-type fluorescent microspheres reverse SSO typing

Biochemistry, blood routine

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new mutation loci from the gene level. Domestic scholars such as Li et  al. have developed gene chip technology for HLA genotyping. Gene chips are miniaturized by traditional reverse dot blot hybridization with solids as supports such as glass, plastic, or silicon. Specific oligonucleotide probes are put and fixed on the phase support, and thousands to hundreds of thousands of probes can be fixed per square centimeter, so that a large number of base sequences can be detected in a short time. The probe is designed according to the unique nucleotide sequence of different subtypes of HLA, and that can be made into an HLA gene chip. After the PCR sample is labeled with fluorescein by PCR amplification, it is hybridized with the immobilized probe on the chip. Laser scanning can automatically determine the HLA allele type of sample DNA by automatically analyzing the fluorescence signal value generated by hybridization. Advantages of Gene Chip High Sensitivity  Gene chip technology has high sensitivity through secondary amplification effect (i.e., PCR amplification of the target gene DNA and fluorescence amplification). High Efficiency  Thousands of different oligonucleotide probes can be immobilized on one chip, so that all HLA alleles may be detected at the same time. High Specificity  Due to the automation and programming of experimental methods, the manual operation error is reduced and the accuracy and specificity of typing are improved. Low Testing Cost  Large sample size and automated testing can reduce the cost.

44.3.1.9 H  LA Typing Based on Sequence-Based Typing Sequence-Based Typing (SBT) is HLA typing method based on direct determination of DNA base sequence. The above typing methods can only detect the phenotype of HLA, but cannot determine the nucleotide sequence of the phenotype. The essence of biological polymorphism is the nucleotide sequence encoding gene products rather than the phenotype. Individuals with the same phenotype do not necessarily have the same DNA sequence. Due to the high polymorphism of HLA, it is difficult to determine all alleles by the above methods. Direct analysis of HLA base sequence is the most accurate and reliable method. In recent years, SBT has evolved from manual sequencing to automated sequencing and is equipped with a range of database analysis software. SBT can not only recognize and classify sequences, but also find new sequences.

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44.3.2 HLA Antibody Detection Kidney transplant recipients may develop antibodies in the body due to transplant failure, pregnancy, blood transfusion, infection, and other reasons. In addition to anti-A and B bloodtype antibodies, HLA antibodies are the main antibodies that affect the survival of the graft [10, 11]. It has been suggested that sensitization to HLA antigens is the only cause of clinically relevant positive cross-matching results. According to the theory of transplanted humor proposed by Professor Pual I. Terasaki, HLA antibodies have the following effects: ① lead to hyperacute rejection of grafts; ② lead to graft C4d deposition combined with early graft failure; ③ is an indicator to predict the sensitization state leading to acute rejection; ④ lead to chronic graft rejection; and ⑤ new HLA antibodies after transplantation predict subsequent graft loss. Before transplantation, the recipient should regularly detect the presence of HLA antibodies, antibody levels, and antibody specificity in the serum to determine whether there is an HLA antibody corresponding to the donor HLA antigen, which can provide a basis for the selection of appropriate donors. The laboratory usually uses three levels of detection: HLA mixed antigen plate (LATM) as the qualitative screening to determine the presence of HLA antibodies in the recipient serum. When LATM was positive, HLA antigen plate (LAT) was used to determine the level of HLA antibody (the degree of sensitization). Finally, HLA monoclonal antigen plate (IHD) detection was performed to determine the specificity of positive antibodies. The measurement method is as follows.

44.3.2.1 Complement Dependent Cytotoxicity CDC method is a method for the determination of antibody specificity and reaction degree in unknown serum by using standard complement-dependent cytotoxicity method after lymphocytes from 30 to 90 different individuals were cryopreserved on Terasaki plate. The standard CDC method was used to detect the antibody specificity and response degree in the unknown serum. The main disadvantages of the method are: 1. Non-HLA autoantibodies were detected. The improvement measures including incubation at 37  °C to avoid cold antibody reactions and add serum dithiothreitol (DTT) to the serum to inactivate IgM antibody. However, the strong autoantibodies of SLE patients are IgG antibodies, and DTT cannot solve the problem. 2. The CDC requires the combination of complement and lysis of target cells to detect antigen–antibody reactions. However, some antibodies against common epitopes cannot effectively activate complement, namely cytotoxicity test negative-absorption test positive phenomenon. The improvement measure is to add anti-human globulin (AHG), which increases the sensitivity of the test by enhancing the binding efficiency of complement C1q.

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44.3.2.2 ELISA The purified HLA antigen was coated on the microplate and the antibody reaction was determined by the sandwich method. The main advantage of ELISA is that the board reader is easy to obtain and relatively inexpensive. The specificity of the antibody can be detected by many reactions in a 96-well (60-well) assay, and ELISA does not require fixation of the complement. Main advantages of ELISA: 1 . More sensitive than CDC and the specificity is high. 2. Can simultaneously detect HLA-I and HAL-II antibodies. 3. Not affected by anti-lymphocyte therapy. 4. Easy to use.

44.3.2.3 Flow Fluorescent Microsphere Method The purified HLA-I and HLA class II antigens are coated on the microparticles, and the anti-human globulin labeled with the fluorescent marker shows the HLA antibody. The laser beam is used to excite the fluorescent label to detect the reaction intensity. The specificity of HLA antibodies can be determined using microparticles coated with different HLA antigens, and HLA-I and HLA-II antibodies can also be distinguished in one trial. The method can detect low titer HLA antibodies. The latest single-specific immune microbeads technology and liquid chip technology (Luminex) are employed to read the results, which can identify antibody specificity accurately.

44.3.3 MICA Antibody Analysis MICA antigen is an antigen expressed by major histocompatibility complex class I chain-related genes (MIC gene), which has 30% homology with the molecular heavy chain of HLA-I antigen and is expressed on the surface of endothelial cells and fibroblasts [12]. Like HLA antibodies, MICA antibodies can also be induced during pregnancy, blood transfusion, and transplantation. The presence of MICA antibodies in recipients can lead to acute or chronic rejection of organ transplantation. At present, MICA antibody is detected by ELISA, flow cytometry, liquid chip technology (Luminex) in the laboratory. Luminex is currently the most widely used method due to its superior sensitivity and reproducibility for purification and recombinant alloantigen.

44.3.4 Non-HLA Antibodies Non-HLA antibodies associated with kidney transplantation mainly include antibodies to vascular endothelial cells and Lewis blood group antigens [13, 14]. Antibodies that affect the graft may be directed against non-HLA antigens. Antibodies against epithelial, mononuclear, and endothelial

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cells have been found in patients who have rejected the graft. Similarly, antibodies in endothelial cell lines are not necessarily polymorphic, but antibodies are produced after endothelial damage. Superovulation caused by endothelial cell antibodies has been reported. Concealed superovulation (ultra-urgent rejection occurs after the incision is closed) is likely to be associated with non-HLA antibodies, which are less damaging than classical HLA antibodies and have a slower rate of microcirculatory thrombosis. Clinically, patients with negative HLA antibody were found to have primary non-function of transplanted kidney (PNF) after surgery, which was confirmed by pathological examination of the graft as hyperacute rejection. Ozawa et al. reported a kidney transplant between 15 HLA identical twins. All the recipients had rejected the transplanted kidney. The results of antibody detection were anti-Lewis antibody positive in 2 cases, anti-MICA antibody positive in 8 cases, anti-­ endothelial antibody positive in 3 cases, and the other 6 cases did not find non-HLA antibodies. Some antibodies may be beneficial to the graft, such as autoantibodies. Preoperative anti-Fab antibodies have proven to be beneficial, and recently, anti-Fab autoantibodies against IgA have been shown to improve graft survival. They may be able to antagonize the activity of common cytotoxic antibodies. Antiidiotypic antibodies to HLA may also have antagonistic effects.

44.3.5 Cross-Matching Experiment of Donor and Recipient Rejection is an important cause of organ transplantation failure. Hyperacute rejection and accelerated rejection caused by the existence of preexisting antibodies (including HLA antibodies) against donor-specific antigens in recipients are the key factors that must be avoided during transplantation [14]. Therefore, the cross-matching between donors and recipients before transplantation is very important for the success of transplantation. At present, the more commonly used cross-matching method is the complement-dependent micro-lymphocyte toxicity test (CDC), which involves the co-incubation of the recipient’s serum with the donor’s lymphocytes and fresh complement for a period of time before staining. Finally, according to the percentage of dying cells staining, the compatibility between the recipient and the donor can be evaluated.

44.3.6 Monitoring of Drug Concentration After Transplantation It is of great clinical significance to regularly detect the blood concentration of immunosuppressants (cyclosporine A, tacrolimus, etc.) after transplantation, so as to achieve the purpose of safe, effective, and rational drug use.

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44.3.7 Detection of Pathogens Associated with Infection After Transplantation Due to the use of immunosuppressants after transplantation, the recipient’s immunity is inhibited, which leads to an increased risk of infection. According to the specific situation of the recipient, the detection of relevant pathogenic microorganisms can be carried out, including various viruses (cytomegalovirus, Epstein-Barr virus, BK-JC virus, etc.), fungi, tuberculosis, Pneumocystis, and other bacteria.

44.3.8 Routine Blood Cell Analysis and Biochemical Index Detection After transplantation, regular blood cell analysis and biochemical indexes examination are helpful for doctors to understand the functional recovery of the transplanted organs of the recipients and whether the drugs used after transplantation are toxic and have side effects.

44.4 C  linical Significance of HLA Matching in Organ Transplantation The debate about the clinical significance of HLA matching in organ transplantation has gradually become unified. 1. HLA matching is necessary for organ transplantation, and the degree of HLA compatibility is still one of the main factors affecting the long-term survival of grafts. At the same time, the influence of other factors cannot be ignored. 2. Kidney transplantation class I antigens mainly affect long-term survival, especially HLA-B antigen. Class Q antigens have an impact on long-term survival and short-­ term survival. In general, HLA-DR antigen is the most important in cadaveric kidney transplantation. 3. The level of fineness of HLA typing in bone marrow transplantation is higher. 4. For other substantial organ transplants, including heart transplantation, liver transplantation, and pancreas transplantation, the clinical values of HLA matching have been gradually considered and valued. But the first consideration is the compatibility of ABO blood group [15].

44.4.1 HLA and Kidney Transplantation HLA-I antigens are expressed in all tissues of the kidney, while HLA-II antigens are expressed only in some tissues such as glomeruli, renal tubules, and endothelium. The effect

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of HLA on kidney transplantation is the group with the deepest study and the largest number of accumulated cases, and it is also representative of the HLA compatible effect in the study of substantial organ transplantation [16].

44.4.2 HLA and Liver Transplantation HLA-I antigens have high density in hepatic biliary epithelial cells, venous epithelial cells, and mesenchymal–epithelial cells, and have low density on hepatocytes. HLA class II antigens are not expressed in normal hepatocytes, but in hepatic portal vein epithelial cells, mesenchymal cells, and sinusoidal cells. During acute rejection, the expression of HLA-I antigen on hepatocytes and expression of HLA-II antigens in biliary epithelial cells and hepatic portal vein epithelial cells increased significantly. Liver transplantation is mostly an emergency operation. The recipients wait for the liver transplantation for a short period of time, the liver has limited storage time, and the cold ischemia time is significantly shorter than the kidney transplantation. There is also a significant difference between liver immunology and kidney transplantation. If liver transplantation is not easy to hyperacute rejection, it is generally believed that hyperacute rejection does not occur; liver transplantation is not sensitive to immune rejection, and chronic rejection is mostly random.

44.4.3 HLA and Heart Transplantation Heart transplantation is mostly an emergency transplant with limited waiting time for recipient [16]. Nonimmune factors are the main risk factors for early death. Based on the clinical research results of foreign scholars in recent years, the impact of HLA compatibility on heart transplantation mainly includes the following aspects. HLA compatibility can reduce the incidence of cardiac transplant rejection. The effects of HLA compatibility on cardiac allograft rejection include a reduction in early acute rejection and a decrease in the incidence of overall rejection. HLA compatibility can improve the survival rate of heart transplantation. From large sample retrospective studies, most scholars believe that HLA compatibility can improve short-term survival and improve long-term survival. However, some results only show an improvement trend without statistical difference. Overall analysis of HLA compatibility on the survival of transplanted heart, HLA-DR antigen has the most obvious effect, HLA-B antigen also has a certain relationship, and the effect of HLA-A antigen seems to be small.

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44.4.4 HLA and Lung Transplantation The effect of HLA compatibility on lung transplantation is basically the same as heart transplantation. Due to the small number of transplants, there is a lack of large sample retrospective clinical study. Scholars believe that DR compatibility can reduce acute rejection of lung transplantation, reduce antirejection therapy, and reduce the incidence of pathogenic bacteria infection, as well as shorten hospital stay and reduce costs. But in fact, HLA compatibility in lung transplantation is difficult to achieve. The main reason is the organ preservation time and the limitations of the recipient sample pool, plus the influence of other nonimmune factors on preoperative selection, the probability of achieving HLA-A.B.DR compatibility is very low. A.B.DR single or combination analysis, the more mismatches, the greater the relative risk, any one of the matches is an independent positive effect, which significantly affects the survival rate of lung transplantation [17].

References 1. Justiz Vaillant AA, Waheed A, Fan J, et  al. Acute transplantation rejection. Treasure Island, FL: StatPearls; 2019. 2. Justiz Vaillant AA, Waheed A, Mohseni M. Chronic transplantation rejection. Treasure Island, FL: StatPearls; 2019. 3. Tong A, Sautenet B, Chapman JR, et  al. Research priority setting in organ transplantation: a systematic review. Transpl Int. 2017;30:327–43.

811 4. Edgerly CH, Weimer ET. The past, present, and future of HLA typing in transplantation. Methods Mol Biol. 2018;1802:1–10. 5. Montgomery RA, Tatapudi VS, Leffell MS, et al. HLA in transplantation. Nat Rev Nephrol. 2018;14:558–70. 6. Madden K, Chabot-Richards D. HLA testing in the molecular diagnostic laboratory. Virchows Arch. 2019;474:139–47. 7. Macdonald WA, Chen Z, Gras S, et al. T cell allorecognition via molecular mimicry. Immunity. 2009;31:897–908. 8. Vallin P, Desy O, Beland S, et al. Clinical relevance of circulating antibodies and B lymphocyte markers in allograft rejection. Clin Biochem. 2016;49:385–93. 9. Dragun D, Muller DN, Brasen JH, et  al. Angiotensin II type 1-receptor activating antibodies in renal-allograft rejection. N Engl J Med. 2005;352:558–69. 10. Picascia A, Grimaldi V, Napoli C. From HLA typing to anti-HLA antibody detection and beyond: the road ahead. Transplant Rev (Orlando). 2016;30:187–94. 11. Loupy A, Lefaucheur C, Vernerey D, et  al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med. 2013;369:1215–26. 12. Luo L, Li Z, Wu W, et al. Role of MICA antibodies in solid organ transplantation. Clin Transpl. 2014;28:152–60. 13. Zhang Q, Reed EF.  The importance of non-HLA antibodies in transplantation. Nat Rev Nephrol. 2016;12:484–95. 14. Jackson AM, Lucas DP, Melancon JK, et  al. Clinical relevance and IgG subclass determination of non-HLA antibodies identified using endothelial cell precursors isolated from donor blood. Transplantation. 2011;92:54–60. 15. Campbell P.  Clinical relevance of human leukocyte antigen antibodies in liver, heart, lung and intestine transplantation. Curr Opin Organ Transplant. 2013;18:463–9. 16. Robson KJ, Ooi JD, Holdsworth SR, et  al. HLA and kidney disease: from associations to mechanisms. Nat Rev Nephrol. 2018;14:636–55. 17. Ju L, Suberbielle C, Li X, et  al. HLA and lung transplantation. Front Med. 2019;13:298–313.

45

Paternity Testing Binwu Ying and Juan Zhou

Paternity testing refers to the analysis of human genetic markers using the tools of medicine, molecular biology, and genetics as well as identification of the genetic relationship between the alleged biological parents and their child according to the principles of genetics. Parentage determination is needed in many situations [1]. For instance: 1. A mother accusing a man of being her child’s biological father 2. A husband suspecting that the child is not his own 3. An accusation of babies switched at birth in the hospital 4. Confirmation of lost children and their relatives 5. Inheritance dispute 6. Relationship confirmation in cases dealing with the immigration of children born out of transnational marriages 7. Determination of sexual assault resulting in pregnancy 8. Biological identification of illegal children 9. Evidence in cases of child abduction Determining parentage has been a problem since ancient times. In ancient China, forensic medical technicians attempted to determine paternity by assessing the compatibility of the blood samples collected from the involved individuals. If the two person’s blood can be merged, then they are family; otherwise, they are not. Trial judges also sometimes applied the practice of forensic psychology to help resolve paternity disputes. A traditional Chinese historical drama described a story of how Judge Bao Zheng in China’s Song Dynasty once solved a case when two women both claimed that an infant boy was their son. Bao Zheng separated the two women, drew a circle on the ground in the middle, put the boy inside, and asked the women to pull the baby out, claiming that whoever got the baby first would be the mother. In the beginning, the two women were determined to pull the boy to their side. When the baby started B. Ying (*) · J. Zhou Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China

crying, one woman, afraid of hurting the boy, released her grip, while the other woman did not care and successfully got the boy. Bao Zhen then determined that the woman who let go was the true mother of the baby because she could not bear to hurt the boy and let him keep crying in pain. In 1900, Karl Landsteiner discovered the ABO blood group system. The genetic law of serological blood group typing provided an essential scientific basis for paternity testing, but it alone was far from adequate. Later, with the analysis of various other proteins and enzymes, the accuracy of paternity testing gradually improved. After the advent of deoxyribonucleic acid (DNA) fingerprinting technology, DNA profiles started to be used in paternity testing. This revolutionized the identification ability and dramatically improved the diagnostic accuracy of parentage determination. Nowadays, forensic medicine methods of paternity testing include the identification of blood types, comparison of physical features, examination of skin texture, examination of genetic diseases, distinction of earwax, examination of taste blindness, and the inference of pregnancy duration, production period, and reproductive capacity. With the discovery of new genetic markers, additional methods of paternity testing have been developed. At present, we can perform accurate DNA profiling by testing blood (fresh or stains), body fluids (fresh or stains), shed epithelial cells, hair, nails, etc. We can also test the unborn fetus using amniotic fluids or chorionic villi. Using this technology for paternity test, certain probability can reach above 99.99%, and the accuracy of excluding biological father is higher.

45.1 Overview 45.1.1 Alleged Father (AF) /Alleged Mother (AM) In paternity testing, a man/woman who is required to clarify the genetic relationship with a child is called a controversial father/mother, assumed father/mother, or alleged father (AF)/alleged mother (AM).

© People’s Medical Publishing House Co. Ltd. 2021 S. Pan, J. Tang (eds.), Clinical Molecular Diagnostics, https://doi.org/10.1007/978-981-16-1037-0_45

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45.1.2 Genetic Marker

B. Ying and J. Zhou

etes via meiosis followed by cellular differentiation, with each sperm and ovum normally containing only 23 unpaired Genetic marker refers to genetic characteristics including chromosomes. With fertilization, the mature gametes then both physical characteristics and blood genetic characteris- fuse to form a zygote, thus combining the two sets of chrotics, inherited from parents and controlled by genes on pairs mosomes, one inherited from each parent. A gene, made up of DNA in humans, is the basic physical and functional unit of chromosomes. Physical hereditary features, such as hair, skin, eye color, of heredity. A variant form of a gene at a genetic locus on a ear hair, face shape, brachydactyly, and polydactyly, can chromosome is called an allele. Some genes can have a varioccasionally help resolve parentage disputes. For example, if ety of different alleles, which are said to be polymorphic. a boy has a black hairy mole in front of his right ear and the Humans inherit two alleles for each gene, one from each parAF has a same black mole in the same location, the AF is ent. Each pair of alleles represents the genotype of a specific very likely the child’s biological father, because the chance gene. Nucleotides, each containing a nitrogenous base, are of a random coincidence of black moles in front of the right the basic building blocks of DNA.  The haploid human nuclear genome comprises about 3 billion DNA base pairs. ear is tiny. Blood hereditary features include the many genetic mark- The random interchange and combination of genetic mateers and polymorphisms of the four main components of rial before germ cell formation makes it never possible for blood, namely, the red blood cells (RBC), white blood cells two people in the world to have the same genome, and this (WBC), platelets, and plasma. To date, more than 260 eryth- forms human genetic polymorphism. The theoretical basis for judging the parent-child relationrocyte homologous antigens, 50 erythrocyte enzymes, nearly 100 plasma proteins, and 124 leukocyte homologous anti- ship is the Mendel’s Law of Segregation and the Law of gens have been found to be useful for paternity identifica- Independent Assortment [2]. The genes in the somatic cell tion. There are 539 genetic markers involving 56 blood group nucleus appear in pairs and determine the genetic traits of the systems, out of which 107 have been tested clinically organism. The Law of Segregation refers to the fact that when germ cells form gametes through meiosis, pairs of (Table 45.1). Since the advent of DNA fingerprinting technology and alleles randomly segregate from each other so that each gamthe establishment of polymerase chain reaction (PCR) ete contains only one allele for each gene. For example, in method, in laboratories with better conditions, multi-­ the MN blood group system, during gametes formation, parameters combining blood groups, enzyme types, and alleles M and N of the heterozygote gene MN are split into DNA polymorphisms have been used, significantly improv- different gametes; which allele enters which gamete is entirely up to chance. The Law of Independent Assortment ing the probability of paternity exclusion. states that genes of different traits are passed onto the offspring independently of each other, such that the inheritance of genes at one location does not influence the inheritance of 45.1.3 Genetic Law genes at another location in a genome, as long as there is no In humans, DNA and histone proteins are tightly packaged genetic linkage among these loci. This law is the theoretical into chromosomes in the nucleus of cells. A typical human basis for calculating the cumulative discriminant probability somatic and germ cell has 23 pairs of chromosomes (46 of genetic markers in personal identification statistics. chromosomes)—22 paired autosomes plus the 23rd pair of Individual identification or paternity testing should select sex chromosomes. The germ cell gives rise to haploid gam- genetic markers that conform to the Law of Independent Assortment. Usually, these markers are located on different chromosomes or at distant locations on the same chromoTable 45.1  Blood genetic markers for resolving paternity disputes some. They need to be confirmed to have no genetic linkage Genetic markers System and can be inherited independently through population surKnown Applied Known Applied veys. Multiplication principle can be used to calculate the Blood components number number number number cumulative discriminant probability of multiple genetic 260 26 24 10 Erythrocyte markers only when independent phenotype or gene frehomologous antigen quency is used independently. Erythrocyte 55 23 13 7 In addition, maternal inheritance and paternal inheritance isozyme are the other two major inheritance laws closely related to Plasma protein 100 37 18 13 paternity testing [2]. A typical example of maternal inheri124 21 1 1 Leukocyte tance is mitochondrial DNA, which mainly transmits genetic homologous antigen information to the next generation through the ova, making the Total 539 107 56 31 mitochondrial DNA sequences of the offspring accordant with

45  Paternity Testing

those of his/her mother. Maternal inheritance can be used to trace the history of human maternal evolution. Also, since the mitochondrial genetic markers are only derived from the mother, they are particularly valuable in the absence of paternity testing and for the identification of siblings of the offspring or siblings of the same mother. Paternal inheritance refers to the fact that most of the Y chromosome DNA does not recombine with the X chromosome. These non-­ recombinant genetic markers are transmitted directly from the father to the son, which are highly conservative and characteristic. The study of Y chromosome non-recombinant DNA polymorphism in a population can not only trace the evolutionary history of human paternity but also play an important role in personal identification and paternity testing.

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AF’s genotype. If there is no obligatory gene, we can exclude the hypothetical father-child relationship. If the AF has the OG, the result cannot rule out the AF’s biological relationship. For example, if the mother is FGA-22/23 and the child is 22/25, it can be determined from the comparison that the OG is FGA-25. If the AF1 is FGA-21/24 and AF2 is FGA-­ 23/25, we can conclude that AF1 does not possess the OG FGA-25, so his biological relationship with the child can be excluded; in contrast, AF2 has FGA-25, and it does not exclude the paternal relationship with the child.

45.2 S  everal Common Paternity Testing Techniques

The child’s genetic characteristics (markers) are the combination of genes provided by both parents and determined from the moment of fertilization. Therefore, when performing a paternity test, we can test the genetic markers of the The basic principles of paternity testing are as follows: father, the mother, and the child and judge whether they con 1. If we affirm that a child’s allele is from the biological form to the genetic law. At present, the standard methods of father and AF does not have this allele, we can exclude paternity testing in forensic medicine include ABO blood him as the biological father of the child. Obviously, the group test, DNA fingerprint analysis, and STR technology more genetic markers examined, the more likely non-­ application. biological fathers will be excluded. 2. If we affirm that some alleles of the child are from the biological father, and AF also carries these alleles, we 45.2.1 Paternity Exclusion by Red Blood cannot rule out that he is the biological father of the child. Cell Type At this point, we can calculate how big is the theoretical power if we judge that he is the biological father of the Three alleles control the ABO blood group system: a receschild. sive allele i and codominant alleles IA and IB. The alleles make six genotypes IAIA, IAi, IBIB, IBi, IAIB, and ii and four In a family, the inheritance law can be summarized as phenotypes A, B, AB, and O. follows: Blood type inheritance follows Mendelian inheritance. In a family, the children’s blood type genes must come from the 1. A child cannot carry alleles that neither of the parents has. parents. However, children’s blood types are not entirely the 2. A child must have one of the pairs of alleles in each same as the parents’, because what the children inherit from parent. parents is the gene that controls blood types instead of the 3. Except when both parents have the same allele, a child blood type (phenotype). Children’s blood types are not cannot have two identical alleles. unique, but rather a highly variable probability event. 4. When one or both of the parents is/are homozygote(s), the However, except for some cases that blood type A person allele of the homozygous gene must be inherited in the married with blood type B person, other blood type combichild. nations can all result in an impossible event with a ­probability of 0. Red blood cells can only be used for paternity excluThe basic principles of paternity testing with biallelic sion, but not for paternity affirmation. genetic markers can be extended to genetic markers of multiple alleles, such as the short tandem repeats (STR) multiplex system. 45.2.2 Paternity and Family Relationship In most of the paternity testing cases, the mother-child Identification by DNA Fingerprint relationship is definite, requiring the identification of the Techniques assumed father-child relationship. Firstly, from the comparison of the mother and child genotypes, it can be determined There are three main types of DNA molecules, single-copy that which allele of the child may come from the father sequences, moderately repetitive sequences, and highly (obligatory gene, OG). Then, we can judge and observe the repetitive sequences, among which, there is a kind of repeti-

45.1.4 Basic Principles of Paternity Testing

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tive sequence with less than 300 nucleotides in length. Because of being highly repetitive, the sequence appears near the main DNA band as satellite band after ultracentrifugation, so it is also called satellite DNA, and the repeat sequence unit is called “small satellite DNA.” Small satellites are highly variable, but in “small satellite DNA,” a short sequence called “core sequence” is the same in all individuals. If the core sequences are connected in series as a molecular probe to hybridize with DNA of different individuals, the unique hybridization map will appear. They have incredibly high selectivity and specificity like human fingerprints, so it is called “DNA fingerprints.” Due to the multisite characteristic, high variability, and simple and stable heredity of DNA fingerprinting, it has attracted people’s attention since its emergence, showing great practical value. The high variability and somatic stability of DNA fingerprints make DNA fingerprinting extremely valuable for suspect identification as well as kinship establishment in forensic medicine. High-resolution DNA fingermarks usually consist of 15 to 30 bands, like the bar codes on products. Most of the bands in the DNA fingerprinting region are independently inherited and follow the Mendelian inheritance law. Each band in the offspring’s DNA fingerprints can be found in one of the parents’ DNA fingerprint. Therefore, DNA fingerprint analysis can be used for paternity identification. If the DNA pattern of the child and the test man does not match on one or more DNA probes, the man is 100% ruled out as the biological father. If the child’s DNA pattern completely matches the test man’s, we can calculate that there is a 99.9% or higher chance that he is the biological father. Herein, DNA fingerprint analysis is more accurate than ABO blood type in paternity testing. However, this technology is complicated in the operational process, time-consuming, and may be inappropriate for various situations. It requires a large number of materials. It does not allow for determining the location of each band in the chromosomes and the independence between individual sites. Furthermore, it is difficult in genotyping standardization. All these disadvantages make DNA fingerprinting difficult in application. In recent years, STR technology has gradually replaced DNA fingerprint technology in forensic paternity testing [3]. The human genome is composed of about 3 billion base pairs. STR generally refers to repetitions of 2–7 bp core sequences. For example, D5S818:5’-GGGTGATTTTCCTTTTGGT (AGAT)7-­14TGTGGCTATGATTGGAATCA-­3′ shows length polymorphism because of the difference of the repetition number of the core sequence between individuals. STR gene loci are widely distributed in the human genome, with short sequence fragments, generally ranging from 100 to 500 bp. Simultaneous multiple gene amplification can be used in PCR analysis for STR. It is not only accurate but also highly convenient and efficient. Therefore, STR is widely applied in

B. Ying and J. Zhou

individual identification, paternity test, archaeology, and gene diagnosis as the ideal DNA genetic markers [3]. STR gene loci generally follow the Mendelian dominant genetic law in gene transmission due to their polymorphism and differences in core sequence repetition numbers among individuals. In addition, short DNA fragments lead to less possibility of mismatch and preferential amplification, thus improving the success rate and sensitivity of PCR amplification. Moreover, the required amount of samples is small (0.1 ng template DNA), the amplification results are stable and repeatable, and automatic typing can be carried out. It can be used for paternity testing, individual identification, and gene ID project. Compared with traditional paternity tests performed using red blood cell types or HLA-A, B, and DR, this method has a higher probability of affirming paternity (99.99%) and more gene loci of excluding paternity (≥3). Now, it has become the main technical tool for paternity testing and individual identification in forensic laboratories, and it has greatly improved the ability of paternity identification.

45.3 J udgement and Analysis of Paternity Testing Results 45.3.1 Parent-Child Relationship Exclusion Paternity testing is most important in cases judging the parental relationship between the AF and the child. In paternity testing, when the mother is the biological mother, if the child’s genetic marks are not provided by the biological mother, it must be provided by the biological father. If the AF cannot provide the genetic markers, he is not the child’s biological father, and the parental relationship is excluded, which calls paternity exclusion. If the AF can provide the child’s OG, on the other hand, the possibility of him being the child’s biological father cannot be ruled out. In paternity testing, the parent-child relationship can be ruled out mainly in two cases [2]: 1. The child carries an allele that is absent in both his/her biological mother and the AF. For example, if the mother is FGA-22/23, the AF is FGA-22/25, and the child is FGA-22/24, the parental relationship between the AF and the child is ruled out. In this case, neither the mother nor the AF can provide the OG FGA-24 of the child. 2. The child does not have the gene that the AF would definitely pass along to his offspring. For example, if the biological mother is FGA-22/23, the AF is FGA-25/25, and the child is FGA-22/24, the parental relationship is also ruled out because the child does not have the allele FGA-­ 25, which is expected in all offspring of the AF.

45  Paternity Testing

How many genetic markers that do not meet the heredity laws are needed to make a paternity exclusion conclusion? Most experts and scholars believe that the parent-child relationship can be ruled out when more than three genetic markers do not conform to the heredity law. One incongruent genetic marker could be caused by mutation or unequal amplification. Two incongruent genetic markers are indeed suspicious, but it has been reported that in a true triplet family, two STR loci variants were found when testing more than twenty STR loci. When there are more than three independent genetic markers that do not meet the heredity laws, the excluded conclusion can be reasonably made with confidence. Assuming the mutation rate of a genetic marker is 0.002, the probability of simultaneous mutation of three genetic markers is only 8 × 10−9; thus the probability of false exclusion is very low. In our practice, there were very few cases in which we reported paternity exclusion based on only three genetic markers. We found more than five incongruent genetic markers in most of the exclusion cases. The more incongruent genetic markers, the lower the probability of an erroneous exclusion. Therefore, for each genetic marker system, there is an excluding probability of paternity when only men with certain genetic markers can be excluded and others cannot be excluded among all possible combinations of mother and child.

45.3.1.1 Excluding Probability of Paternity (EP) The systemic efficacy of genetic markers for paternity testing is often quantitatively assessed using the excluding probability of paternity (EP). The EP refers to the probability that a man who is not the father of a child can be excluded from the genetic markers. When a man who is not a child’s father is mistakenly accused of a biological father, it can theoretically be denied based on genetic marker detection. However, when the genetic marker has a poor discriminating ability, the genetic markers of a random man and a child without blood relationship may coincide with the genetic law, so it is not certain that he has no parent-child relationship with the child. Different genetic markers have different polymorphisms; the probability that an assumed man cannot be excluded due to chance is high or low. Therefore, it is necessary to know the EP when it is applied to a man who is mistakenly accused of a child’s father by applying a genetic marker [2]. For example, if the EP of the HLA-B system is 0.8071, it means that 80.71 non-parents can theoretically be excluded out of 100 non-parents by detecting HLA-B antigen. 45.3.1.2 C  alculation of Excluding Probability of Paternity (EP) The EP is related to the genetic pattern and degree of polymorphism of the genetic markers. It mainly depends on the number of alleles and allelic frequency distribution of the

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genetic markers. At present, the commonly used DNA genetic marker systems are codominant inheritance, and the number of alleles is large. For a genetic marker system, let pi represent the i-th allele frequency in the population, pj represents the j-th allele frequency in the population, and the allele i is not equal to the allele j, then the EP of the genetic marker is:





EP   pi 1  pi   1 / 2   pi 2 pj2  4  3pi  3pj 2



i 1

i 1

j  i 1

45.3.1.3 C  umulative Excluding Probability of Paternity (CEP) The above formula for calculating the EP is used for a particular locus. Since more than one locus is used for paternity testing, it is necessary to know the total genetic markers used for a man who is not the father of a child in order to determine the efficacy at excluding the innocent AF, that is, the total cumulative excluding probability of paternity (CEP). The CEP is calculated as:



CEP  1  1  EP1 1  EP 2  1  EP3  1  EPk   1   1  EPk 

where EPk is the EP value of the k-th genetic marker. Check a variety of genetic markers, determine the EP value according to the genetic method of various genetic markers, and then calculate the total CEP value according to the formula. Table 45.2 shows an example of the excluding probability of paternity of 15 commonly used STR loci in the Chengdu Han population. As shown, the more genetic markers used, the higher the CEP, and the stronger the discriminating ability is.

Table 45.2  EP and CEP of 15 STR loci in Chengdu Han population Locus D5S818 FGA D8S1179 D21S11 D7S820 CSF1PO D3S1358 TH01 D13S317 D16S539 D2S1338 D19S433 vWA TPOX D18S51

EP 0. 482 0. 690 0. 718 0. 626 0. 498 0. 466 0. 428 0. 466 0. 626 0. 539 0. 672 0. 581 0. 608 0. 214 0. 757

CEP 0.482 0. 839,420 0. 954,716 0. 983,064 0. 991,498 0. 995,460 0. 997,403 0. 998,613 0. 999,481 0. 999,761 0. 999,922 0. 999,967 0. 999,987 0. 999,990 0. 999,998

818

45.3.1.4 E  rrors in Excluding Parent-Child Relationship and Its Solutions As the number of genetic markers detected increases, genetic mutations with low frequency are gradually disclosed. Lack of this knowledge makes it prone to false paternity exclusion. Genetic mutations include gene mutations, alternative alleles, weak antigens, silent genes, gene deletions, blood type variants, gene exchanges, CIS-AB effects, chimeras, mosaic antigens, and physiological and pathological variations [2, 4, 5]. Gene Mutation In the process of meiosis, the exchange and recombination of genes and the mutation of genes due to the effect of uncertain factors can occur. This is an important reason for the genetic markers of the parents and offspring to be incompatible with the genetic law. It may affect the accuracy of paternity testing and thus mislead the detection and trial of the case. Therefore, genetic markers with lower mutation rates should be used in the process of paternity testing.

B. Ying and J. Zhou

45.3.2 Affirmation of Parent-Child Relationship At present, more and more genetic markers are used for paternity testing, especially the widespread use of STR genetic markers. It has changed the condition that the blood type test can only be used for parental exclusion and cannot affirm parental rights. When the genetic marker test results do not violate the genetic law between the parent and the offspring, there may be a biological relationship. At this point, the parent-child relationship index and the parent-­ child relationship probability can be calculated to understand the possibility of the existence of a biological relationship between them and to determine whether there is a biological relationship.

45.3.2.1 Paternity Index The paternity index (PI) is the likelihood ratio of the two probabilities required to determine the parent-child relationship, that is, the ratio of the probability that the AF is the child’s biological father (X) to the probability that a random Alternative Allele man is the child’s biological father (Y). In other words, PI Among the erythrocyte blood types, there are some alterna- assesses how much more likely the AF who possesses the tive alleles, such as CW, CX, Rh26-c, EW, ET-E, V, VS-e, Mg-­ OG than a random man who also has the OG is to be the M, Fy3, Fy4-Fya, or Fyb. For example, suppose that both the child’s biological father. mother and the daughter are the N type, and the AF is the M type. Under normal circumstances, the AF should be 45.3.2.2 Relative Chance of Paternity excluded. However, if the AF is the MMg type and the child The above-calculated PI value is an absolute value. In order is the NMg type, no antigen is detected by anti-M or anti-N to express the relative chance of paternity (RCP) in a probaserum alone, and the AF could be mistakenly judged as the bilistic form, the PI value must be converted into a relative M type (MM) while the child as the N type (NN). In reality, value RCP. After calculating the PI value, RCP = [PI / (PI however, the MMg type father cannot be excluded because he +1)] × 100%. According to the practice of paternity testing at can provide the Mg allele to the child. home and abroad, it can be assumed that the father and the child have a biological relationship when the RCP value is Weak Antigen greater than 99.99%. If the RCP value does not reach 99.99%, The weak blood type antigen is challenging to detect and is it can be assumed that the parent and the child do not have a easily misidentified as a negative antigen. The following biological relationship. If the RCP value is less than 99.99%, antigens of the fetus or babies are not well developed, and the number of detection sites should be increased until the the antigenicity is very weak, including A1, I, P1, Leb, Xga, RCP is greater than 99.99%. and Hp. The A1 type is easily mistaken for the A2 type, the P1 type for the P2 type, and the HP type for the HpO type. 45.3.3 Forensic Criteria of Paternity Testing Gene Exchange Several gene exchanges of MNSs have been discovered, and 45.3.3.1 The Standard of Paternity Exclusion one Rh gene exchange has been found, but the probability is The genetic marker is tested by standardized experiments. very low. The male assumed as the father who could not provide necessary alleles to the child can be excluded paternity; in the CIS-AB Effect absence of a mutation, we can conclude that he is not the The A and B genes can be located on one chromosome as a biological father of the child. In order to avoid potential unit and pass on to the offspring. The AB blood type parents mutational effects, the exclusion of paternity should be based can produce O type children, and dozens of such cases have on at least three genetic markers. In no case can the paternity been reported in the literature. be excluded based on only one genetic marker.

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45.3.3.2 The Standard of Paternity Affirmation When genetic markers were tested by standardized experiments and the AF could not be excluded paternity, the paternity can be affirmed; that is, the AF can be judged to be the child’s biological father if the following two indicators are met at the same time after calculating the probability [6]: 1. The experimentally detected CEP of genetic markers is equal to or greater than 99.99%. 2. Assuming that the father’s cumulative PI is equal to or greater than 10,000; that is, under the condition that the pre-probabilities are the same, the father’s relative chance of paternity is assumed to be equal to or greater than 99.99%. The PI is the ratio of two probabilities, and it is usually converted, when used as evidence provided to the courts, into a conditional probability, the RCP, according to Bayes’ theorem.



RCP   PI /  PI  1   100%

For example, the PI of a paternity test is calculated to be 2497. Under the condition that the pre-probabilities are the same, then:

RCP  2497 /  2497  1   100%  99.96%

According to the above-mentioned paternity criteria, if the RCP value is greater than 99.99% (equivalent to PI ≥10,000), it is assumed that the parent-child relationship should be unambiguous.

45.3.4 Laboratory Standards of Paternity Testing The quality control of the paternity test is to standardize the laboratory and ensure that the test results are correct and reliable. More and more laboratories now recognize the importance of this work. The International Association of Forensic Genetics paternity test committee recommended the international standards for paternity testing laboratories in 2002. As a qualified paternity test laboratory, it should firstly comply with the ISO 17025 guidelines, so that the legal documents it produces are authoritative. The guidelines stipulate that the laboratory management personnel and authorized signatory should have specific qualifications and be recognized by the authority. Secondly, the appraisers should have the qualification to engage in test appraisal, relevant professional knowledge, strong instrument operation ability, and data analysis and interpretation skills. At the same time, sound equipment and equipment files should be established. Due to our dependence on highly automated instruments and equipment, routine services and regular verifications are crucial to achieving

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the correct results. In order to ensure that each result is correct and reliable, negative and positive controls must be set. The positive control is a verification of the reliability of the reagents, equipment, and sample results. Practices have proved that the accurate positive control results are the prerequisite for a correct judgment of the test samples. Qualified laboratories should also regularly participate in laboratory comparisons and laboratory proficiency testing activities. This not only allows labs to assess their capabilities but also provides opportunities to communicate with and learn from each other, making progress together. Appraisal of the paternity testing results is accurate and authoritative only under strict quality control, standardized operation, and technical standards [4–6].

45.4 Collection and Preservation of Paternity Test Samples Paternity testing nowadays uses samples containing DNA for identification. Samples are mainly divided into three categories, including routine samples (blood, bloodstain, hair with follicles, buccal swab), special samples (nails, pure spots, cigarette butts, amniotic fluid, chewing gum, toothbrush, bone, menstrual blood), and difficult samples (embryo, slice or paraffin-embedded tissue, mixed spots). Different types of materials have different degrees of change in their denaturation, degradation, and corruption due to their characteristics and environmental factors, bringing certain difficulties to the inspection and identification work. Therefore, before the samples are delivered to the laboratory, the samples should be kept appropriately, and the inspection time should be shortened as much as possible [4–6].

45.4.1 Collection of Paternity Test Samples 45.4.1.1 Blood/Bloodstain Mark the date of sample collection and sample identity on a clean envelope bag, such as father, mother, and child. First, use an alcohol swab to disinfect, collect 2–5  mL venous blood as blood specimens, and five drops of blood collected from fingertips or earlobes (for children can also take blood from soles) on a medical gauze or filter paper as bloodstain specimens. After being naturally dried (not allowed to blow dry or sundry), each is placed in a labeled envelope. Note: During the collection process, one should not touch the bloodstain sample. Ensure that the sample is consistent with the identity of the person marked on the envelope. If a person has received a blood transfusion or bone marrow transplant in the past year, blood/blood mark samples are not recommended; hair or buccal swab samples can be used as an alternative.

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45.4.1.2 Hair Mark the date of sample collection and sample identity on a clean envelope bag, such as father, mother, and child. Pull off at least five hairs from the head, eyelashes, or body hair, with clear hair follicles visible at the end of the hair. Immediately put the newly removed hair into the envelope that has been marked. Note: Make sure that the hair follicles can be seen at the end of the hair with the naked eye. Avoid touching the hair follicles of the sample with hands. Do not touch the hair follicles of the hair during the collection process. Specimens that have fallen on the ground or have been unplugged for a long time should be rejected. 45.4.1.3 Oral Swab Mark the date of sample collection and sample identity on a clean envelope bag, such as father, mother, and child. Avoid touching the cotton swab with hands. Rinse the mouth with water before collecting. Take a special cotton swab in the right cheek of the mouth and slowly rotate it 20 times. Take out another special cotton swab and repeat the above steps to contact the left cheek in the mouth and slowly rotate 20 times. Each subject needs to repeat the above steps to collect six swabs. After the collection is completed, the cotton sticks for each specimen are dried in the shade (not in the sun, or hot air), and then placed in the prepared envelope. 45.4.1.4 Saliva and Saliva Spots After the saliva is provided, let the saliva naturally flow out 1–2 mL, collect it in a clean test tube or a small beaker, and boil for 5 minutes in a water bath or store it frozen. Saliva spots are common in the remaining cigarette butts, pipes, chewing gum, melon shells, straws, beverage containers, bite marks, toothpicks, and toothbrushes. They are not visible to the naked eye and are not easy to find. Suspicious items can be sent to the laboratory for inspection. 45.4.1.5 Amniotic Fluid Samples Amniotic fluid samples must be collected by an experienced obstetrician in a hospital to ensure the safety of pregnant

B. Ying and J. Zhou

women and fetuses. Collect 3–5 mL of amniotic fluid samples, which are clear and transparent. They should not contain the blood components of the mother. The samples should be sent for inspection as soon as possible, especially in the summer. It is better to provide a blood sample of the mother as a control.

45.4.2 Preservation of Paternity Test Samples After the samples are collected, each sample must be individually packaged to avoid sample loss and cross-­ contamination during the process of inspection. The packaging of the sample should be firm, clean, and easy to label. Various types of paper bags, plastic centrifuge tubes, jars, etc. can be used. Liquid or wet samples should be stored frozen at −20 °C as soon as possible after collection. Freezing is a simple and effective way to preserve DNA test samples, but freezing cannot be sterilized. After thawing, microbial growth will still occur. Drying can inhibit the growth and reproduction of microorganisms; most of the samples can be made into dry stains for long-term preservation, and it is better to keep the dry stains in the −20 °C refrigerator. Ultraviolet light can degrade DNA more quickly, and the materials should be kept away from direct sunlight. The tissue soaked in the formaldehyde solution is difficult to extract DNA, and it can generally be stored in 75% ethanol solution.

References 1. Peng R, Bin P, Suyun H. The application of paternity testing in the field of forensic evidence. Legal System and Society. 2019;5:224–5. 2. HouYiping. Forensic biological evidence. 3rd ed. Beijing: People's Medical Publishing House; 2009. 3. Veselinović I.  Microsatellite DNA analysis as a tool for forensic paternity testing (DNA paternity testing). Med Pregl. 2006;59:241–3. 4. HouYiping LY. Forensic DNA genotyping. Beijing: Science Press; 2007. 5. Zheng X. Forensic DNA analysis. Beijing: Chinese People's Public Security University Press; 2002. 6. Chengtao L, Yiping H, Li L, Suhua Z, Yacheng L, Hongyu S. Specification of parentage testing. GB/T. 2018:37223–2018.

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Appendix A: Tests of Infectious Disease Infectious diseases refer to local tissues and systemic inflammatory reactions caused by bacteria, viruses, fungi, and very common pathogens (mycoplasma, chlamydia, tuberculosis, etc.) that invade the human body. Among the world’s deaths from diseases, infectious diseases account for 45%. In addition to clinical manifestations, the diagnosis of infectious diseases is more important by means of the detection of pathogens or their markers. Clinical microbiological testing faces enormous challenges. Traditional artificial culture takes a long time, while morphological examination technology requires high experience and is easy to miss. Finding fast and accurate markers is the key to infectious disease surveillance and effective treatment. With the all-round development of new technologies such as molecular biology detection technology and serology, pathogen variants and “new pathogens” are known to be rapidly and accurately discovered, which brings great convenience to clinical diagnosis. Infection diagnostic markers can be broadly classified into two categories: rapid antigen-antibody detection and rapid nucleic acid detection. With the introduction of new foreign technologies and testing platforms, automated and digital related detection technologies have been applied and widely used in the diagnosis of infectious diseases. A.1  Adenovirus Antibody Tests Sample Required Serum (100 μL) Detection Method EIA, CLIA, ECL Reference Ranges Negative Interpretation 1. Adenovirus infection specimens include throat swab specimens, nasal washes, and stool specimens. However, adenovirus infection is significant through serological diagnosis. At least 12 types of adenovirus have been associated with illnesses such as pneumonia, and acute hemorrhagic cystitis. Asymptomatic infections can make serologic responses difficult to interpret.

2. Take the serum in the acute phase and the recovery phase of the patient for detection. If the serum antibody titer in the recovery period is 4 times or more than the acute phase, it is meaningful. A.2  Aspergillus Antigen (Galactomannan, GM) Detection Tests Sample Required Serum, BALF, CSF (300 μL) Detection Method EIA Reference Ranges index G Detection method Direct sequencing technique is used to detect the genotype of the SNP locus of CACNA1C gene.

D.2.3 Angiotensin II Receptor Inhibitor Gene profile Common polymorphic loci The genes that influence drug treatment are CYP2C9 and CYP2C9: 1075A>C AGTR1. VKORC1: 1639G>A Common polymorphic loci Detection method and result interpretation CYP2C9: 1075A>C The CYP2C9 gene and VKORC1 gene polymorphism AGTR1: 1166A>C detection methods mostly use real-time quantitative PCR Detection method and result interpretation TaqMan technology. Samples covering the CYP2C9 gene The CYP2C9 gene polymorphism detection method homozygous wild type, heterozygous mutant, VKORC1-­ mostly uses real-time fluorescent quantitative PCR TaqMan 1639G>A homozygous mutant and VKORC1-1639G>A technology. A homozygous wild type, heterozygous mutant heterozygous mutant were selected, and the sample DNA sample covering the CYP2C9 gene was selected. The sample was extracted for real-time quantitative PCR amplification. DNA was extracted and subjected to real-time fluorescent quantitative PCR amplification detection.

D.2  Antihypertensive Therapy D.2.1 β1 Receptor Antagonist Drug introduction The beta receptor antagonist is a drug that is contraindicated in patients with severe left ventricular dysfunction, sinus bradycardia, severe atrioventricular block, and bronchial asthma. Patients with myocardial infarction and liver dysfunction should be used with caution. Gene profile 1. The genes that affect drug therapy and evaluate drug use doses are ADRB1 and CYP2D6. 2. CYP2D6 is an enzyme encoded by the human CYP2D6 gene which is mainly expressed in the liver and is also highly expressed in the central nervous system, including the substantia nigra.

Common polymorphic loci ADRB1: 1165G>C CYP2D6: 100C>T Detection method and result interpretation The CYP2D6*10 gene detection method mostly uses the ASA PCR method. Two primers complementary to the unique intrinsic sequence of the CYP2D6*10 gene were designed to generate a 790  bp fragment as a control. The experiment was carried out in two tubes, and the wild type and CYP2D6*10 type were, respectively, verified by two specific primers, and the results were based on agarose gel electrophoresis bands. D.2.2 Calcium Channel Blocker of Dihydropyridine Gene profile The gene that affects drug therapy is CACNA1C. Common polymorphic loci

D.2.4 Angiotensin-Converting Enzyme Inhibitor Drug introduction Angiotensin-converting enzyme inhibitor (ACEI) is a compound that inhibits angiotensin-converting enzyme activity. Angiotensin-converting enzyme catalyzes the production of angiotensin II by angiotensin I, which is a potent vasoconstrictor and activator of adrenocortical aldosterone release. Gene profile The gene that affects drug treatment is ACE. Detection method and result interpretation The ACE gene polymorphism detection method has the traditional Rigat method and the advanced three primer methods commonly used in China. The results of agarose gel electrophoresis or polyacrylamide gel electrophoresis are often used as the interpretation criteria, stained with ethidium bromide, and observed under ultraviolet light. D.2.5 Thiazine Diuretic Drug introduction 1. It is an antihypertensive drug that can be used alone to treat early hypertension or to treat moderate to severe hypertension with other antihypertensive drugs. 2. Thiazide can enhance the excretion of NaCl and water, producing a mild and long-lasting diuretic effect, but long-term use can lead to hypokalemia. Gene profile The genes that influence drug treatment are ADD1 and PPKCA. Common polymorphic loci ADD1: 1378G>T

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TaqMan  −  MGB Needle Ct value  −  Type A PPKCA: 1854+3730G>A TaqMan − MGB probe Ct value) Determine genotype. Detection method ADD1 gene polymorphism detection mostly uses PCR-­  .5  Anti-inflammatory Treatment SSCP and DNA sequencing technology. Primers were D D.5.1 Celecoxib designed based on different base variations. Drug introduction It is used to relieve the symptoms and signs of osteoarthriD.2.6 Furosemide tis, relieve the symptoms and signs of rheumatoid arthritis, Gene profile and treat acute pain in adults. The gene that affects drug treatment is ADD1. Gene profile Detection method The gene that affects the therapeutic effect of the drug and ADD1 gene polymorphism detection mostly uses PCR-­ SSCP and DNA sequencing technology. Primers were evaluates the dose of the drug is CYP2C9. Common polymorphic loci designed based on different base variations. 1075A>C Detection method D.3  Anticardiac Insufficiency Real-time fluorescent quantitative PCR TaqMan D.3.1 Digoxin technology. Drug introduction Digoxin is a medium-effect cardiac glycoside drug, which is white crystal or crystalline powder, odorless with bitter D.6  Antigout Treatment taste. At the time of treatment, the effect on the heart is a D.6.1 Allopurinol positive inotropic effect, slowing heart rate and inhibiting Drug introduction Allopurinol and its metabolites can inhibit xanthine oxicardiac conduction. Suitable for low-output congestive heart failure, atrial fibrillation, atrial flutter, paroxysmal supraven- dase, so that hypoxanthine and xanthine cannot be converted into uric acid, that is, uric acid synthesis is reduced. It reduces tricular tachycardia. blood uric acid concentration and urate in bones and joints. Gene profile Gene profile The gene for evaluating the dose of the drug is ABCB1. The genes that affect the therapeutic effect of the drug and Common polymorphic loci evaluate the dose of the drug are HLA-B*58:01 and ABCG2. 3435C>T Common polymorphic loci Detection method HLA-B*58: 01:52A>T The ABCB1 gene was detected using Sanger ABCG2: 421C>A sequencing. Detection method and result interpretation

D.4  Antiangina Pectoris Treatment D.4.1 Glonoine Drug introduction Nitroglycerin can directly relax vascular smooth muscle, especially small vascular smooth muscle, relax peripheral vasodilation, reduce peripheral resistance, reduce blood flow, reduce cardiac output, reduce cardiac load, reduce myocardial oxygen consumption, and relieve angina. Gene profile The gene that affects the therapeutic effect of drugs is ALDH2. Common polymorphic loci 1510G>A Detection method and result interpretation 1. Real-time fluorescent quantitative PCR is often used for ALDH2 gene polymorphism detection. 2. The TaqMan-MGB double probe was used to establish an allelic typing method based on the difference ΔCt of the cycle threshold (Ct value) of the fluorescence signal of the allele G and type A TaqMan-MGB probes (ΔCt = G type

1. HLA-B*58:01 gene detection can be performed by fluorescence in situ hybridization. 2. The in situ hybridization fluorescence staining analysis system automatically interprets the fluorescence signal value to obtain the fluorescence curve for HLA-B*5801 genotyping and positive control. When one of the two fluorescent signals is higher than the internal reference signal, the site is positive, so the results of “+/−” and “+/+” indicate HLA-B*.

D.7  Antipeptic Ulcer Treatment D.7.1 Lansoprazole Drug introduction 1. Lansoprazole, alias Dakpron, Langsonaze, is a white crystalline powder of chemicals. 2. The benzimidazole compound is orally absorbed and transferred to the gastric mucosa, and is converted into an active metabolite under acidic conditions, and the activator specifically inhibits the final step of gastric acid cell

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secretion by blocking the H+/K+-ATPase system of the gastric parietal cells. Gene profile The gene that affects the therapeutic effect of drugs is CYP2C19. Detection method Real-time fluorescent quantitative PCR. D.7.2 Omeprazole Drug introduction

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Detection method Real-time fluorescent quantitative PCR.

D.8.2 Citalopram Drug introduction Citalopram is a selective serotonin reuptake inhibitor (SSRI) that is a racemate. It can selectively inhibit the 5-HT transporter, block the reuptake of 5-HT by the presynaptic membrane, prolong and increase the effect of 5-HT, thereby producing an antidepressant effect. Gene profile 1. Omeprazole, mainly used for duodenal ulcer and Zhuo-­ The genes that influence the therapeutic effect of drugs Eye syndrome, can also be used for gastric ulcer and are CYP2C19 and FKBP5. reflux esophagitis; intravenous injection can be used for Detection method the treatment of acute bleeding of peptic ulcer. Real-time fluorescent quantitative PCR. 2. Combined with amoxicillin and clindamycin or with metronidazole and clarithromycin to kill Helicobacter pylori. D.8.3 Escitalopram As a proton pump inhibitor, it is a fat-soluble weakly Drug introduction basic drug. Aspirin, its English name is escitalopram, the molecular formula is C20H21FN2O, the CAS accession number is Gene profile 128196-01-0, and the ATC code is N06AB10. The gene that affects the therapeutic effect of drugs is Gene profile CYP2C19. Detection method 1. CYP2C19 is an important member of the second subfamReal-time fluorescent quantitative PCR. ily of CYP450 enzymes and is an important drug metaboD.7.3 Pantoprazole lizing enzyme in the human body. Drug introduction 2. It is expressed in the liver. The CYP2C19 locus is located on chromosome 10q24.2 and consists of 9 exons. 1. Clinically, it is a proton pump inhibitor drug that inhibits gastric acid secretion. For the treatment of active peptic Detection method ulcer reflux esophagitis and Zhuoyi’s syndrome. Real-time fluorescent quantitative PCR. 2. Pantoprazole is a third-generation proton pump inhibitor that selectively acts on gastric mucosal cells, inhibits the D.9  Antipsychotic Treatment activity of H, K-ATPase in parietal cells, and prevents H D.9.1 Risperidone in parietal cells from being transported into the stomach. Drug introduction Inhibition of gastric acid secretion. 1. For the treatment of acute and chronic schizophrenia. Gene profile 2. It has a high affinity with the 5-HT2 receptor and the The gene that affects the therapeutic effect of drugs is dopamine D2 receptor. CYP2C19. 3. The oral absorption of this product is rapid and complete, Detection method and its absorption is not affected by food. The blood drug Real-time fluorescent quantitative PCR. peak concentration is reached after 1 h of administration, and the elimination half-life is about 3 h. Most patients D.8  Antidepressant Therapy reach steady state within 1 day. D.8.1 Amitriptyline Drug introduction Gene profile Amitriptyline is used to treat various types of depression The gene that affects the efficacy is DRD2. or depression. It can relieve chronic pain and it is also used Detection method and result interpretation to treat children with enuresis and children with ADHD. Gene profile 1. The real-time fluorescent quantitative PCR method is comCYP2C19 is an important member of the second subfammonly used for the detection of DRD2 gene polymorphism. ily of CYP450 enzyme and is an important drug metaboliz- 2. The DRD2 gene polymorphism site was amplified by ing enzyme in the human body. gene amplification, and the obtained PCR product was sequenced using a pyrosequencing apparatus.

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D.10  Antiepileptic Treatment D.10.1 Carbamazepine Drug introduction The anticonvulsant mechanism of carbamazepine is unclear and may be associated with its ability to increase sodium channel inactivation, limit post-synaptic neurons, and block presynaptic Na+ channels, thereby limiting preand post-synaptic neurons. Gene profile The gene for carbamazepine to evaluate the drug dose is HLA-B*15:02 or HLA-A*31:01, located on the short arm of chromosome 6. Detection method Extracting specimen DNA and performing routine detection of HLA high-resolution typing by polymerase chain reaction-specific oligonucleotide probe hybridization (PCR-SSOP). D.10.2 Dilantin Drug introduction 1 . Phenytoin is an antiepileptic drug. 2. This product belongs to class IB anti-arrhythmia drug, which has membrane stability and inhibits fast sodium ion influx.

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Gene profile The gene for evaluating the dose of the drug is HLA-B*15:02. Detection method Specimen DNA was extracted and subjected to routine detection of HLA high-resolution typing by polymerase chain reaction-specific oligonucleotide probe hybridization (PCR-SSOP).

D.11Antileukemia Treatment D.11.1 Methotrexate Drug introduction Methotrexate is an anti-folate anti-tumor drug. This product is an orange-yellow crystalline powder. It inhibits the synthesis of tumor cells mainly by inhibiting the synthesis of dihydrofolate reductase, and inhibits the growth and reproduction of tumor cells. Tetrahydrofolate is an important coenzyme for the synthesis of purine nucleotides and pyrimidine deoxynucleotides in vivo. As a folate reductase inhibitor, this product mainly inhibits dihydrofolate reductase and prevents dihydrofolate from being reduced to physiological activity. The tetrahydrofolate, which hinders the transfer of a mono-carbon group during the biosynthesis of purine nucleotides and pyrimidine nucleotides, results in inhibition of DNA biosynthesis. Gene profile

Gene profile 1. The gene used to evaluate the dose of the drug is HLA-B or CYP2C9. 2. CYP has multiple subfamilies, of which CYP2C9 (Cytochrome P450 2C9) is an important member of the second subfamily, accounting for 20% of the total amount of liver microsomal P450 protein. 3. The CYP2C9 gene is located on the chromosomal region 10q24.2 and has a full length of about 55 kb and is composed of 9 exons. CYP2C9 shares 92% sequence homology with another P450 enzyme, CYP2C19, but these two enzymes have completely different substrate specificities. Detection method Specimen DNA was extracted and subjected to routine detection of HLA high-resolution typing by polymerase chain reaction-specific oligonucleotide probe hybridization (PCR-SSOP) D.10.3 Oxcarbazepine Drug introduction 1. Oxcarbazepine, a neurological drug, can be used for localized and systemic seizures. 2. Oxcarbazepine is mainly used for clinically allergic reactions to carbamazepine, and can be used as an alternative medicine for carbamazepine.

1. The genes evaluating its dose were SLCO1B1, MTRR, and MTHFR. 2. The SLCO1B1 gene is a key factor in the adverse reactions caused by statins. The mutant SLCO1B1 gene can cause a decrease in the ability of the liver to ingest statins, causing an increase in blood concentration and an increase in the risk of rhabdomyolysis or myopathy. Methionine synthase reductase (MTRR). The mutation of the 66th site of the MTRR gene in children increases the risk of congenital heart disease (CHD) in the offspring. The mutation of the 66th site of the mother MTRR gene significantly increases the risk of CHD in the offspring; the mother has MTRR and MTR. In combination with variants, the risk of CHD in their offspring increases. 3. The genes closely related to folate metabolism are MTHFR and MTRR. When these genes are mutated, the use of folic acid in pregnant women is inefficient, thereby increasing the risk of neonatal birth defects or spontaneous abortion. The folate metabolism gene detection is mainly to detect the three sites of human metabolism folic acid related genes MTHFR and MTRR genes (MTHFR677C>T, MTHFR1298A>C, MTRR66A>G), to evaluate the individual’s folate metabolism ability, to achieve personalized supplementation of folic acid. Objective: To scientifically guide folic acid supplementation and reduce the risk of neonatal birth defects.

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Detection method and result interpretation PCR-fluorescent probe method is often used for SLCO1B1 gene detection. Real-time PCR detection The SLCO1B1 A388G&T521C assay was performed according to the instructions of the human SLCO1B1 gene detection kit, and a positive/negative control (provided with the kit) was performed. Fluorescence quantitative PCR reactions were performed using the FQD96A real-time PCR instrument and the PCR 9600 PLUS software. PCR cycle parameters: 37 °C for 10  min (UNG enzyme treatment decontamination); 95  °C 5 min; 95 °C 15 s, 60 °C 60 s, 40 cycles. Set the FAM, VIC, and ROX 3 channels to collect fluorescence signals. Interpretation of the results: The end of the PCR amplification, the SLCO1B1 genotype was determined based on the amplification of the fluorescence curve of each sample. D.11.2 6-Mercaptopurine, Imuran Drug introduction It is an anti-tumor drug, which is effective for acute leukemia and is also effective for chronic myeloid leukemia; it is used for chorionic epithelial cancer and malignant mole. Mechanism of action of 6-mercaptopurine: 6-MP competitively inhibits hypoxanthine-guanine phosphoribosyltransferase, which prevents the phosphoribosyl ribose in the PRPP molecule from transferring to guanine and hypoxanthine, and blocks the remedial synthesis pathway of purine nucleotides. Gene profile and Common polymorphic loci 1. The genes for evaluating its dosage are TPMT*3C and NUDT15. 2. TMTP*3C is one of the three common genes that cause a decrease in TMTPase activity. Common polymorphism is 719 A to G. Detection method The NUDT15 415C>T and TPMT*3C genotypes are determined by PCR-RFLP. D.11.3 Thioguanine Drug introduction 1. The chemical name of thioguanine is 2-aminoindole6(H)thione, which is white or yellowish powder, has a umami taste, is easily soluble in water, and is insoluble in organic solvents such as ethanol and acetone. It is mainly used to treat acute leukemia. It also has a certain effect on chronic myeloid leukemia. Its action is similar to that of thiopurine, and it is active after being converted into thioguanosine monophosphate (6-TGRP) in the body. Finally, it is converted into deoxyguanine nucleotides, which interfere with DNA function and produce anticancer effects.

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2. It is a S phase specific anti-tumor drug with a retarding effect on the S/G2 boundary. It is a commonly used sputum metabolic antagonist for inhibiting the synthesis of purines. It is a cell cycle-specific drug. It is most sensitive to cells in S phase. In addition to inhibiting the synthesis of cellular DNA, it also has a slight inhibitory effect on RNA synthesis. This product is an analogue of guanine, which must be converted from a phosphoribosyltransferase to a 6-TG ribonucleotide in the human body. Gene profile The genes for evaluating its dose were TPMT*3C and NUDT15. Detection method PCR-RFLP.

D.12  Antineoplaston D.12.1 Capecitabine Drug introduction 1. Tegafur digestive cancer has a certain effect on gastric cancer, colon cancer, rectal cancer, pancreatic cancer. It is also effective for breast cancer and liver cancer. 2. Capecitabine is rapidly absorbed by the intestinal mucosa after oral administration, and then converted into an inactive intermediate 5′-deoxy-5-fluorocytidine in the liver by carboxylesterase, followed by cytidine removal from liver and tumor tissues. The action of the aminoase is converted to 5′-deoxy-5-fluorouridine and finally catalyzed by thymidine phosphorylase to fluorouracil (5-FU) in tumor tissues. Gene profile The gene that affects the efficacy of drugs is DPYD. Detection method Sequencing can be used for the detection of the polymorphism of the DPYD gene. D.12.2 Carboplatin Drug introduction 1. Carboplatin is a broad-spectrum anti-tumor drug, has no cross-resistance with other anti-tumor drugs, and has cross-resistance with DDP. The drug is easy to dissolve, does not require hydration, diuresis, diuresis, and is convenient to use. It mainly used for small cell lung cancer, ovarian cancer, testicular tumor, head and neck squamous cell carcinoma. 2. In small cell lung cancer, the remission rate of this product alone was 60%; the total remission rate of treatment with Vp-16 and IFO was 78%; the remission rate of ovarian epithelial cancer and head and neck squamous cell carcinoma was 65% and 29%, respectively. It can be used for non-small cell lung cancer, bladder cancer, cervical

Appendixes

cancer, pleural mesothelioma, melanoma and endometrial cancer. It can also be used for digestive system tumors, liver cancer, etc. and radiotherapy. Gene profile The gene that affects its efficacy is MTHFR. Detection method MTHFR gene polymorphism detection is commonly used in PCR-fluorescence probe method. D.12.3 Cisplatin Drug introduction 1. Cisplatin is an orange-yellow or yellow crystalline powder. Slightly soluble in water, soluble in dimethylformamide. It can be gradually converted into trans and hydrolyzed in aqueous solution. Clinically used for ovarian cancer, prostate cancer, testicular cancer, lung cancer, nasopharyngeal cancer, esophageal cancer, malignant lymphoma, head and neck squamous cell carcinoma, thyroid cancer and osteosarcoma can show efficacy. 2. The role of tumors is of great significance. But only cis is meaningful, and trans is invalid. It can cross-link with DNA strands and shows cytotoxic effects. After dissolving, it can pass through the charged cell membrane without carrier transport in the body. Due to the low concentration of intracellular chloride ions (4  mmol/L), chloride ions are replaced by water, and the charge is positive. It has the function of a bifunctional group similar to an alkylating agent. It can bind to the base of DNA in the nucleus to form three forms. Cross-linking causes DNA damage, disrupts DNA replication and transcription, and inhibits RNA and protein synthesis at high concentrations. Cisplatin has the advantage of wide anticancer spectrum. Gene profile The genes affecting the efficacy of the drug and evaluating its dosage are XPC, TP53, GSTP1, and ERCC1. XPC provides instructions for making a protein involved in repairing damaged DNA. Ultraviolet rays from the sun, toxic chemicals, radiation, and unstable molecules called free radicals destroy DNA. Detection method and result interpretation Gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR. The genotype is determined based on the PCR product melting curve analysis. When the melting peak appeared at (90.0 ± 0.5) °C and (90.5 ± 0.5) °C, respectively. D.12.4 Cyclophosphamide Drug introduction 1. Cyclophosphamide (CTX) is a nitrogen mustard derivative that acts upon activation of an excess of phosphoram-

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idase or phosphatase present in the liver or tumor in the human body and becomes an activated phosphoamido mustard. It has the broad anti-tumor spectrum and is the first so-called latent broad-spectrum anti-tumor drug, which is effective for leukemia and solid tumors. 2. This product is inactive in  vitro, mainly through the hydrolysis of liver P450 enzyme into aldehyde phosphoramide and then run into the tissue to form phosphoramide mustard. Cyclophosphamide can be inactivated by the conversion of dehydrogenase to carboxyphosphoramide or in the form of acrolein, resulting in urinary tract toxicity. It is a periodic non-specific drug with the same mechanism of action as nitrogen mustard. Gene profile 1. The genes affecting the efficacy of the drug and evaluating its dosage are MTHFR, GSTP1, and SOD2. 2. The gene GSTP1 encoding GSTπ is located on chromosome 11q13, contains 7 exons and 6 introns, and there are gene polymorphisms in exons 5 and 6. The A→G base occurs at the 81st position of exon 5 Substituting, can lead to the 105th amino acid of the protein peptide chain changed from ATC isoleucine (Ile) to GTC proline (Va1), which is recorded as GSTP1-I105V, GSTP1-I105V ­polymorphism, which can produce 105Ile/Ile, 105Ile/Val, and 105Val/Val three genotypes. 3. The presence of GSTs gene polymorphisms can cause different activities of the corresponding enzymes that are expressed, leading to changes in detoxification function, thereby increasing the risk of specific tumors. Studies have shown that the frequency of distribution of different genotypes of GSTP1 has significant ethnic and regional differences. SOD2 contains manganese, 4p15.3-p15.1 located on chromosome 4. Detection method and result interpretation Gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR. D.12.5 Fluorouracil Drug introduction 1. Fluorouracil acts as an antimetabolite and, after being converted into an effective fluorouracil deoxynucleotide in the cell, interferes with DNA by blocking the conversion of deoxyribouroperidic acid to thymidylate by intracellular thymidylate synthase. synthesis. Fluorouracil can also interfere with the synthesis of RNA.  After intravenous administration, fluorouracil is widely distributed in body fluids and disappears from the blood within 4 h. It is converted to nucleotides. It is preferentially taken up by actively dividing tissues and tumors, and fluorouracil eas-

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kinase and metabolized by cytidine deaminase. This prodily enters the cerebrospinal fluid. About 20% of the protouct is a pyrimidine anti-tumor drug, the mechanism of type is excreted from the urine, and most of the rest is action is the same as that of cytarabine, and its main metabolized in the liver by a mechanism that is generally metabolite is incorporated into DNA in cells, mainly in the metabolized by uracil. G1/S phase. However, the difference is that in addition to 2. Pentafluorouracil and 6-mercaptopurine were the earliest the incorporation of DNA, difluorodeoxycytidine inhibits anticancer drugs, which were all extracted from sea cucumthe ribonucleotide reductase, resulting in a decrease in ber. This product needs to be converted into intracellular deoxynucleoside triphosphate; another differ5-­fluorodeoxyuridine nucleotide by enzyme to have anti-­ ence from cytarabine is that it inhibits deoxygenation. tumor activity. 5-FU inhibits DNA synthesis by inhibiting Pyrimidine deaminase reduces the degradation of intracelthymidine nucleotide synthetase. The action of this enzyme lular metabolites and has a self-enhancing effect. may transfer one carbon unit of formyltetrahydrofolate to deoxyuridine-phosphate to synthesize thymidine monoacid. 2 . Clinically, this product and cytarabine have different anti-­ tumor profiles and are effective against a variety of solid 5-FU also has a certain inhibitory effect on the synthesis of tumors. For patients with advanced pancreatic cancer, as RNA fluorouracil. 5-FU can be injected intravenously and a second-line medication after fluorouracil failure, it can intraluminally. 5 Fluorouracil was continuously adminisimprove the quality of life of patients; secondly, it is a tered to patients with bladder cancer at a constant rate. first-line application for locally advanced (stage III) and 3. The concentration of the drug was the lowest at 10 already metastatic (stage IV) non-small cell lung cancer. o’clock, and the highest concentration was between 22 Recent data indicate that this product has palliative effects and 3 o’clock. When the constant velocity drop is not on ovarian cancer, breast cancer, bladder cancer, cervical used, the peak flow rate is set at 4 am, which allows the cancer, liver cancer, biliary tract cancer, nasopharyngeal dose to be greatly increased and the toxicity is extremely cancer, testicular tumor, lymphoma, mesothelioma, and low, and the therapeutic effect is enhanced. head and neck cancer. 4. In addition, 5-FU metabolites can also enter the RNA and DNA in the form of pseudo-metabolites, affecting cell funcGene profile tion and producing cytotoxicity. 5-FU is an atypical cell The gene associated with this drug is NT5C2. This gene cycle-specific drug that, in addition to its primary action on encodes a hydrolase that acts primarily on inosine 5′-monoS phase, also has an effect on cells in other phases. phosphate and other purine nucleotides and plays an important role in cell raft metabolism. It may play a key role in maintainGene profile ing a constant composition of the intracellular purine/pyrimi 1. The genes affecting this therapeutic effect and the dose dine nucleotides in synergy with other nucleotidase enzymes. for evaluation are DPYD and TP53. Preference is given to the hydrolysis of inosine 5′-monophos 2. TP53 is a gene located on chromosome 17pl3.1, 20  kb phate (IMP) and other purine nucleotides. The location of the long, containing 11 exons, encoding a protein of 393 amino cell: 10q24.32-q24.33, is the long (q) arm of chromosome 10 acids. P53 protein is a transcription factor involved in cell between 24.32 and 24.33. Molecular position: base pair 103, cycle regulation, DNA repair, cell differentiation, and cell 088, 017 to 103, 193, 306 on chromosome 10 (Homophone regulation. It mainly performs the DNA check “check- Update Note Version 109.20190607, GRCh38.p13). point” function. If the DNA is damaged, the P53 protein Detection method level rises rapidly and activates its downstream p2I/WAFI/ Gene detection was performed by establishing artificially CIP/gene expression, which is a group of cyclin-dependent modified biallelic specific primers in combination with protein kinases (cyclin). It arrests cells in the G phase and SYBR Green I fluorescent PCR. performs DNA repair. If the repair fails, TP53 induces death by activating the BAX gene pathway. Approximately D.12.7 Oxaliplatin 50% of human tumors are associated with allelic inactiva- Drug introduction tion or mutation of the TP53 gene. Oxaliplatin is a third-generation platinum anticancer drug, a platinum compound of diaminocyclohexane, in Detection method which a 1,2-diaminocyclohexane group is substituted for the Sequencing can be used for the detection of the polymor- amino group of cisplatin. It has the same action as other platphism of the DPYD gene. inum drugs, that is, DNA is the target site, and platinum atoms form a cross-link with DNA to antagonize its replication and transcription. Patients with colorectal cancer who D.12.6 Gemcitabine are ineffective in 5-FU therapy are still effective against Drug introduction other platinum-resistant patients. 1. Gemcitabine is a new cytosine derivative. Like cytarabine, Gene profile it enters the human body and is activated by deoxycytidine

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1. The gene that affects the efficacy of this drug is GSTP1. The gene encoding GSTπ, GSTP1, is located on chromosome 11q13, contains 7 exons and 6 introns, and there are gene polymorphisms in exons 5 and 6. 2. The A→G base occurs at the 81st position of exon 5. Alternatively, the 105th amino acid of the protein peptide chain can be changed from ATC isoleucine (Ile) to GTC proline (Va1), which is recorded as GSTP1-I105V, GSTP1-I105V polymorphism, which can produce 105Ile/ Ile, 105Ile/Val, and 105Val/Val genotypes. The presence of GSTs gene polymorphisms can cause different activities of the corresponding enzymes that are expressed, leading to changes in detoxification function, thereby increasing the risk of specific tumors. Studies have shown that the frequency of distribution of different genotypes of GSTP1 has significant ethnic and regional differences. Common polymorphic loci 313A>G Detection method GSTP gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR.

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expressed at high levels in both the gonads and the brain.” The gene is located at the 15q21.2 region of the chromosome and is about 123  kb in length, including a 30  kb coding region and a 93 kb regulatory region. A large number of related studies in recent years have shown that the genetic polymorphism of CYP19A1 is associated with AD. Mutations in some of the CYP19A1 loci increase the risk of AD. Detection method and result interpretation The CYP2D6*10 gene detection method mostly uses the ASA PCR method. Two primers complementary to the unique intrinsic sequence of the CYP2D6*10 gene were designed to generate a 790  bp fragment as a control. The experiment was carried out in two tubes, and the wild type and CYP2D6*10 type were, respectively, verified by two specific primers, and the results were based on agarose gel electrophoresis bands. D.12.9 Vincristine Drug introduction

1. Vincristine (Oncovin, VCR) is an alkaloid extracted from the vinca flower of the oleander family. It has a good anti-­ tumor effect and is currently used as a clinical anti-tumor drug. It is found in the periwinkle plant, periwinkle. When D.12.8 Tamoxifen recrystallization in methanol, it is needle crystal. The Drug introduction melting point is 211–216 °C. 1. Tamoxifen is used to treat advanced breast and ovarian 2. It also has effects on mouse Ridgeway osteosarcoma, cancer. In the clinical treatment of breast cancer, the Mecca lymphosarcoma, X-5563 myeloma and the like. effective rate is generally 30%, the estrogen receptor-­ Vincristine has a significant anticancer effect on cultured positive patients have better efficacy (49%), and the negahuman hepatoma cell line (SMMC-7721). When vincristive patients have poor efficacy (7%). Both premenopausal tine is 100 and 50 ng/mL, most of the cancer cells can be and postmenopausal patients can be used, and postmenorounded. The envelope becomes thick and falls off into a pausal and 60 years of age or older are better than presuspended state. menopausal and younger patients. 3. At the concentration of 100 and 50 ng/mL, most of the 2. From the point of view of the lesion, the skin, lymph tumor cells were rounded, the membrane became thick, nodes, and soft tissues are effective, and the effect of bone and the shedding was suspended. When vincristine was and visceral metastasis is poor. For the synthesis of anti-­ applied at a concentration of 100 ng/mL on the next day estrogen drugs. and 50 ng/mL on the 6th day, the inhibition rate of cell proliferation was very significant {inhibition rate 50%}, Gene profile and the concentration of vincristine 100 ng/mL on the 8th day after the action. The proliferation inhibition rate was 1. Effect of drug efficacy of gene CYP2D6 group gene and 99.64%. At the same time, as the concentration of VCR CYP19A1 genes. CYP2D6 (English: Cytochrome increased, the inhibition rate of protein content increased P4502D6) is an enzyme encoded by the human CYP2D6 to 75.21% (25 ng/mL). Vincristine has a strong inhibitory gene. CYP2D6 is mainly expressed in the liver and is also effect on human retinoblastoma cell line HOX-Rb44. The highly expressed in the central nervous system, including half-inhibitory concentration IC50 is 0.31 μg/mL, and the the substantia nigra. vincristine concentration is 0.01  μg/mL, which can sig 2. The CYP19A1 gene encodes a cytochrome P450 aromanificantly induce the apoptosis of K562 cells. tase (P450arom), and the aromatase is the last step-­ limiting enzyme in hormone synthesis. Under its action, Gene profile androgen androstenedione and testosterone are converted The gene associated with this pharmaceutical dose is to estrogen and estrone, respectively. Glycol, which is CEP72.

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Detection method Gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR.

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is good. Because of its good curative effect, low toxicity, low cost, and convenient oral administration, it is listed as the preferred anti-tuberculosis drug. The oral absorption rate of isoniazid was 90%; the serum drug concentration reached the peak 1–2  h after administration; Vd was D.13  Antifungal Therapy 0.61 ± 0.11 L/kg, and the protein binding rate was very D.13.1 Voriconazole low. Drug introduction 2. The product is mainly metabolized by acetylation in the body and partially hydrolyzed. Due to genetic differ 1. The mechanism of action of voriconazole is to inhibit ences, the population can be divided into fast acetylated cytochrome P450-mediated 14α-sterol demethylation in and slow acetylated. Their half-life was significantly fungi, thereby inhibiting the biosynthesis of ergosterol. ­different, and the average t1/2 of fast acetylators was 2. In vitro tests have shown that voriconazole has a broad-­ 1.1 h. Slow acetylation is 3 h. The product easily passes spectrum antifungal effect. This product has an antibactethrough the blood–brain barrier. Mainly used for the prorial effect on Candida (including fluconazole-resistant gression of various types of tuberculosis, dissolution and Candida krusei, Candida glabrata, and Candida albicans), dissemination period, absorption and improvement and has a bactericidal effect on all tested Aspergillus fungi. period, can still be used for tuberculous meningitis and 3. In addition, voriconazole also has a bactericidal effect on other extrapulmonary tuberculosis. other pathogenic fungi in vitro, including strains that are less sensitive to existing antifungal agents, such as the Gene profile genus Actinomyces and Fusarium. 1. The gene that affects the efficacy of the drug and evaluGene profile ates its dose is NAT2. The gene affecting the efficacy of the drug and evaluating 2. NAT2 is N-acetyltransferase 2 (aromatic amine N-­ its dosage is CYP2C19. acetyltransferase). This gene encodes an enzyme that actiDetection method vates and inactivates aromatic amines and terpenoids and Real-time PCR. carcinogens. The polymorphism of this gene is associated with n-acetylated polymorphisms. In the n-­ acetylated polymorphism, the human population is divided into fast, D.14  Anti-hyperthyroidism moderate, and slow acetylated phenotypes. Polymorphisms D.14.1 Methimazole in this gene are also associated with a high incidence of Drug introduction cancer and drug toxicity. The second aromatic amine Methimazole tablets are anti-thyroid drugs. They are suitn-acetyltransferase gene (NAT1) is located near this gene able for all types of hyperthyroidism. They are especially (NAT2). suitable for patients with milder disease, mild to moderate thyroid enlargement; adolescents and children, and elderly 3. The location of the cell: 8p22 is the short arm (p) of position 22 on chromosome 8, molecular positioning: base 8 patients with recurrence after thyroid surgery. It is not suitof chromosome 8, 18, 386, 585~18, 401, 219. able for patients treated with radioactive iodine 131I. Gene profile Detection method The gene affecting its dosage is mainly HLA-B*38:02:01. Methods: The polymorphism of the NAT2rs1495741 The gene is located on chromosome 6. locus in tuberculosis patients was detected by modified mulCommon polymorphic loci tiplex ASe assay. g.2465195A>G Detection method Specimen DNA was extracted and subjected to routine D.15.2 Rifampicin detection of HLA high-resolution typing by polymerase Drug introduction chain reaction-specific oligonucleotide probe hybridization (PCR-SSOP). 1. Rifampicin (also: rifamycin, meperidol, meptomycin, weifuxian, xianduolun, lifuping or limidine, INN: D.15  Anti-tuberculosis Treatment Rifampicin) is a kind of benefit A broad-spectrum antibiD.15.1 Isoniazid otic drug of the fumycin family has a strong antibacterial Drug introduction effect against Mycobacterium tuberculosis, and is also effective against Gram-positive or negative bacteria and 1. Isoniazid has inhibitory and killing effect on viruses. Mycobacterium tuberculosis, and its biofilm permeability

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2. It is a red or dark red crystalline powder and is insoluble in water. Oral drugs, usually in capsules or tablets, work synergistically with other anti-tuberculosis drugs and delay the production of resistant strains. Mainly used to treat tuberculosis, meningitis, and Staphylococcus aureus infections. External use can treat trachoma. Gene profile The gene affecting the efficacy and dosage of rifampicin and pyrazinamide is NAT2. Detection method The modified multiplex ASee assay.

D.16  Antiasthma treatment D.16.1 Salbutamol Drug introduction 1. Salbutamol, a short-acting β2 adrenergic receptor agonist, is used as an antiasthmatic agent to effectively inhibit the release of allergic substances such as histamine and prevent bronchospasm. For bronchial asthma, asthmatic bronchitis, bronchospasm, emphysema, and other symptoms. 2. Salmeterol is a novel selective long-acting beta 2 agonist that lasts for 12 h at a single dose. There is a strong inhibitory effect on the release of allergic mediators from lung mast cells, which can inhibit the early and late phase reactions induced by inhaled antigens and reduce airway hyperresponsiveness. For asthma (including nocturnal asthma and exercise asthma), asthmatic bronchitis, and reversible airway obstruction. This product is a long-­ acting selective β2 adrenergic receptor agonist with obvious bronchodilating effect. 3. Inhalation of this product 50 and 100 μg, the average time to increase the forced inspiratory volume (FEV) per minute by 15% is 17  min and 13  min, respectively, while inhaling salbutamol 200  μg, the same effect requires 14 min. When the asthmatic patients inhaled the product for 2 weeks, no rapid immunological effects on the lung effect of the product were observed. This product can strongly and long-term inhibition of histamine, leukotrienes, prostaglandins, and other inflammatory reactions, the effect lasts 12 h. Gene profile 1. The gene that affects the efficacy of salbutamol and salmeterol is ADRB2. This gene encodes a-2 adrenergic receptor and is a member of the G protein coupled receptor superfamily. 2. This receptor is directly related to one of its final effectors, class C, calcium channel Ca(V)1.2. This receptor– channel complex also contains a G protein, adenylate cyclase, a camp-dependent kinase, and an equilibrium

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phosphatase PP2A.  Assembly of signaling complexes provides a mechanism to ensure specific and rapid signaling of this G protein coupled receptor. This gene is intron free. Different polymorphic forms, point mutations, and/ or down-regulation of this gene are associated with nocturnal asthma, obesity, and type 2 diabetes. 3. The location of the cell: 5q32 is the long (q) arm of chromosome 5 at position 32. Molecular localization: The base pairs on chromosome 5 are 148,826,593 pairs to 148,828,634 pairs. Detection method Gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR.

 .17  Anesthetic Treatment of Paroxysmal Pain D D.17.1 Fentanyl Drug introduction 1. Fentanyl, suitable for all kinds of pain and analgesia after surgery and surgery, surgery, gynecology, etc.; also used to prevent or reduce the occurrence of paralysis after surgery; can also be combined with anesthetics, as an anesthesia auxiliary; Pelidogine is combined into a “deep analgesic” for a large area dressing and minor analgesia. It is an opioid receptor agonist and is a potent narcotic analgesic with pharmacological effects similar to morphine. Animal experiments have shown that the analgesic effect is about 80 times that of morphine. 2. The analgesic effect is rapid, but the duration is shorter, 1  min after the intravenous injection, and the peak is reached in 4 min, and the effect is maintained for 30 min. It takes about 7 min after intramuscular injection and is maintained for about 1–2 h. The respiratory inhibition of this product is weaker than that of morphine, and the adverse reaction is smaller than that of morphine. Gene profile 1. The genes affecting the dose and efficacy of these classes of drugs are CREB1 and OPRM1. The CREB1 gene encodes a transcription factor that is a member of the leucine zipper family of DNA-binding proteins. This protein is combined with the camp reaction element, the octamer palindrome, in the form of a homodimer. This protein is phosphorylated by multiple protein kinases and induces gene transcription when the hormone stimulates the cAMP pathway. Alternate splicing of this gene can result in multiple transcript variants encoding different subtypes. 2. It is located at the location of the cell: 2q33.3, which is the long (q) arm at position33.3 of chromosome 2. Molecular

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localization: pairs 207, 529, 892 pairs on chromosome 2 to 207, 605, 989 pairs. 3. The OPRM1 gene provides an indicator protein called μμ opioid receptor. Opioid receptors are part of the endogenous opioid system, an internal system of the body that regulates pain, reward, and addictive behavior. Opioid receptors are present in the nervous system and they are located in the outer membrane of nerve cells (neurons). When an opioid binding to a receptor, this interaction triggers a series of chemical changes between the neuron and the neuron, creating a feeling of pleasure and pain. Common polymorphic loci CREB1: g.208494234 OPRM1: 118A>G Detection method and result interpretation Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and polymerase chain reaction-­ sequence specific primer (PCR-SSP) analysis techniques were used. After separation by agarose gel electrophoresis, the gel imager system was processed for genotype interpretation. D.17.2 Tramadol Drug introduction Tramadol is a non-opioid central analgesic but has a weak affinity with opioid receptors. By inhibiting the reuptake of norepinephrine by neuronal synapses and increasing the concentration of serotonin outside the neurons, it affects the pain transmission and produces an analgesic effect. Its intensity of action is 1/10 to 1/8 of morphine. No inhibition of respiration, low dependence, and significant analgesic effect. It has antitussive effect and the strength is 50% of codeine. Does not affect the release of histamine, does not cause smooth muscle spasm. Oral, injection and absorption are good, the analgesic effect is the same. Metabolized in the liver, 80% of the original form and metabolites are excreted from the urine within 24 h. Gene profile The gene affecting the amount and efficacy of this pharmaceutical agent is OPRM1. Common polymorphic loci 118A>G Detection method and result interpretation PCR-RFLP and PCR-SSP.

D.18  Disease Prevention D.18.1 Folic Acid Drug introduction One of the folic acid vitamin B complexes, which is equivalent to pteroylglutamic acid (PGA), is extracted and purified from spinach leaves by M. K. Mitchell (1941). It has the effect of promoting the maturation of young cells in the bone marrow. The lack of folic acid in humans can cause macrocytic anemia and leukopenia, which is especially important for pregnant women.

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Gene profile The gene that affects the therapeutic effect of drugs is MTHFR. Common polymorphic loci 677C>T 1298A>C Detection method and result interpretation MTHFR gene polymorphism detection is commonly used in PCR-fluorescence probe method. Based on the amplification curve, appropriate baseline and fluorescence thresholds are specified. The Ct values of different channels were obtained to determine the genotype of the sample.

D.19  Erectile Dysfunction D.19.1 Sildenafil Drug introduction Sildenafil tablets are a drug that is used primarily to treat penile erectile dysfunction (ED). Gene profile The gene affecting the efficacy of the drug and evaluating the dose of the drug is GNB3. A heterotrimeric guanine nucleotide binding protein (G protein) is a protein that integrates signals between a receptor and an effector protein, and consists of one, one, and one subunit. These subunits are encoded by a family of related genes. This gene encodes a beta subunit belonging to the WD repeat G protein beta family. Subunits are important regulators of subunits and are regulators of certain signal transduction receptors and effectors. The single-nucleotide polymorphism (C825T) of this gene is associated with essential hypertension and obesity. This polymorphism is also associated with the development of the splice variant GNB3-s, which appears to be more active. GNB3-s is an example of alternative splicing caused by nucleotide changes outside the splice donor and acceptor sites. Alternative splicing results in multiple transcript variants. Other alternative splicing transcript variants of this gene have been described, but their full-length properties are unclear. The position of the gene is located at the location of the cell: 12p13.31, which is the short arm (p) at position 13.31 of chromosome 12. Molecular position: base pairs on chromosome 12, 6,840,922 pairs to 6,847,393 pairs. Common polymorphic loci 825C>T Detection method Gene detection was performed by establishing artificially modified biallelic specific primers in combination with SYBR Green I fluorescent PCR. D.20  Hypoglycemic Therapy D.20.1 Melbine Drug introduction Metformin tablets are preferred for type 2 diabetes, which is effective in diet control and physical exercise, especially obese type 2 diabetes. This product can be combined with insulin to reduce the amount of insulin and prevent hypogly-

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cemia. It can be combined with sulfonylurea hypoglycemic agents and has a synergistic effect. Gene profile The genes that influence the therapeutic effect of drugs are C11orf65 and TCF7L2. Common polymorphic loci C11orf65: 108412434C>A TCF7L2: 53341C>T Detection method The PCR-RFLP method is commonly used for the detection of C11otf65 gene polymorphism. The PCR product was digested with restriction endonucleases. D.20.2 Sulfonylurea Drug Introduction Sulfonylureas (SU) are the earliest, most widely used, and most widely used oral hypoglycemic agents. In recent years, glimepiride has been developed. It is called the third-­generation SU class drug because of its small dosage, certain improvement in insulin resistance and reduction of insulin dosage. Gene profile The genes that affect the therapeutic effect of drugs and evaluate the dose of drugs used are CYP2C9 and ABCC8. Common polymorphism detection site CYP2C9: 1075A>C ABCC8: 16-3C>T Detection method The CYP2C9 gene polymorphism detection method mostly uses real-time fluorescent quantitative PCR TaqMan technology. Samples covering the wild type and heterozygous mutants of CYP2C9 gene were selected, and the sample DNA was extracted for real-time quantitative PCR amplification.

D.21  Immunosuppressive Therapy D.21.1 Ciclosporin A Drug introduction Cyclosporin A is composed of a cyclic polypeptide consisting of 11 amino acids and is a potent immunosuppressive agent. Clinically, it is mainly used for anti-rejection reaction of liver, kidney, and heart transplantation. It can also be used together with adrenocortical hormone to treat immune diseases. Gene profile The gene for evaluating the drug use dose is CYP3A5. Common polymorphic loci 219–237A>G Detection method Real-time quantitative PCR. D.21.2 Tacrolimus Drug introduction Tacrolimus, also known as FK506, is a fermentation product isolated from Streptomyces tsukubaensis and its chemi-

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cal structure is a 23-membered macrolide antibiotic. It is a powerful new type of immunosuppressive agent, which inhibits the release of interleukin-2 (IL-2) and inhibits the action of T lymphocytes. Gene profile The gene for evaluating the dose of the drug is CYP3A5. Common polymorphic loci 219–237A>G Detection method Real-time quantitative PCR. D.21.3 Tumor Necrosis Factor Inhibitor Gene profile The gene that affects the therapeutic effect of drugs is TNF. Common polymorphic loci −308G>A Detection method and result interpretation 1. Detection of TNF gene polymorphism can be detected by real-time fluorescent quantitative PCR combined with high-resolution melting curve method. 2. Data were collected and genotypes were determined according to the order of melting.

D.22  Lipid Control Therapy D.22.1 Fluvastatin Drug introduction Fluvastatin is the first fully chemically synthesized cholesterol-­lowering drug. Compared with the listed natural or semi-synthetic HMG-CoA reductase inhibitors lovastatin, simvastatin, and pravastatin, fluvastatin has the advantages of relatively simple structure, selective action, and low incidence of adverse reactions. It is an excellent hypolipidemic drug. Gene profile The genes affecting drug treatment and evaluating the dose of drugs used are APOE and COQ2. Common polymorphic loci APOE: 526C>T COQ2: 779–1022C>G Detection method PCR-fluorescence probe method. D.22.2 Rosuvastatin Drug introduction Rosuvastatin is a selective HMG-CoA reductase inhibitor. The main site of action of rosuvastatin is the liver-targeting organ that lowers cholesterol. Rosuvastatin increases the number of surface receptors in liver LDL cells, promotes LDL uptake and catabolism, inhibits liver synthesis of VLDL, thereby reducing the total number of VLDL and LDL particles. Gene profile

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The genes affecting drug therapy and evaluating drug use doses are ABCG2, APOE, SLCO1B1, and COQ2. Common polymorphic loci APOE: 526C>T ABCG2: 421C>A SLCO1B1: 521T>C COQ2: 779–1022C>G Detection method PCR-fluorescence probe method.

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increased serum cholesterol clearance, lower levels of clinical use mainly to lower cholesterol, especially low-­ density lipoprotein cholesterol (LDL-C), for the treatment of atherosclerosis. Gene profile

1. Genes affecting drug therapy and evaluating drug use doses are ApoE, COQ2, and SLCO1B1. 2. The COQ2 gene mainly encodes a para-­hydroxybenzoate-­ polyprenyl transferase, which is an important enzyme in D.22.3 Pravastatin the process of synthesizing coenzyme Q10. When the Drug introduction human COQ2 gene is mutated, it will affect the biosynPravastatin is a white crystalline powder of chemicals, thesis of coenzyme Q10, which will affect the electron often in the form of a sodium salt. Pravastatin is a lipid-­ transport chain during mitochondrial oxidation. Studies lowering drug and is mainly used in patients with primary have shown that human COQ2 gene can significantly hypercholesterolemia or hypertriglyceridemia (types IIa and increase the risk of systemic atrophy after functionally IIb) with uncontrolled dietary restriction. Atorvastatin impaired variants, suggesting that this gene mutation is Lipitor (English name Lipitor, commonly known as atorvasclosely related to the pathogenesis of systemic atrophy. tatin calcium tablets) is a lipid-lowering drug developed by Pfizer. It is applicable to reduce the risk of non-fatal myocarCommon polymorphic loci dial infarction, reduce the risk of fatal and non-fatal stroke, APOE: 526C>T reduce the risk of revascularization, reduce the risk of hospiCOQ2: 779–1022C>G talization due to congestive heart failure, and reduce the risk SLCO1B1: 521T>C of angina. Detection method Gene profile APOE gene and SLCO1B1 gene detection are mostly The genes affecting drug therapy and evaluating drug detected by PCR-fluorescence probe method. Real-time dosage are APOE, KIF6, SLCO1B1, and COQ2. PCR detection was performed according to the instructions Common polymorphic loci of the human SLCO1B1 and APOE gene detection kit. APOE: 526C>T KIF6: 2104T>C SLCO1B1: 521T>C COQ2: 779–1022C>G Detection method PCR-fluorescence probe method. D.22.4 Statins Drug introduction 1. The statin, 3-hydroxy-3methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor, is currently the most effective lipid-lowering drug, not only potently reducing total cholesterol (TC) and low-density lipoprotein (LDL), and can reduce triacylglycerol (TG) to a certain extent, can also increase high-density lipoprotein (HDL), so statins can also be called more comprehensive lipid-­ lowering drugs. 2. The mechanism of action of statins is to competitively inhibit the endogenous cholesterol synthesis rate-limiting enzyme HMG-CoA reductase, block the intracellular hydroxyvalerate metabolic pathway, and reduce intracellular cholesterol synthesis, thereby feedback-stimulated low cell membrane surface. Increased number and activity of density lipoprotein (LDL) receptors resulting in

D.23  Risk Profile D.23.1 Alcohol Metabolism Assessment Gene profile The gene for evaluating the efficacy of this drug is ALDH2. Detection method and result interpretation Real-time fluorescent quantitative PCR is often used for ALDH2 gene polymorphism detection. The TaqMan-MGB double probe was used to establish an allelic typing method based on the difference ΔCt of the cycle threshold (Ct value) of the fluorescence signal of the allele G and type A TaqMan-­ MGB probes (ΔCt  =  G type TaqMan  −  MGB Needle Ct value − Type A TaqMan − MGB probe Ct value) Determine genotype. D.23.2 Children’s Asthma Risk Gene profile The gene for evaluating the risk of this drug is ORMDL3. Detection method Primers were designed for the SNP site of the ORMDL3 gene to perform multiplex PCR reactions. Detection by SNP typing technology.

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D.23.3 Risk of Venous Thrombosis Gene profile The gene for evaluating the risk of drug use is the PAI-1 gene. D.23.4 Risk of Alzheimer’s Disease Gene profile The gene for evaluating the risk of drug use is APOE. D.23.5 Risk of Habitual abortion Gene profile The gene for evaluating the risk of drug use is MTHFR. D.23.6 Risk of Postpartum Depression Gene profile The gene for evaluating the risk of drug use is MTHFR. D.23.7 Risk of Birth Defects in Newborns and Individualized Supplementation of Folic Acid Gene profile The genes evaluating the risk of drug use are MTHFR and MTRR.

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D.23.8 Risk of Cardiovascular and Cerebrovascular Diseases Gene profile The genes evaluating the risk of drug use are MTHFR and MTRR. D.23.9 Thrombosis Risk Susceptibility Gene Screening for Thrombosis Risk Gene profile The gene for evaluating the risk of drug use is the PAI-1 gene. D.23.10 Thrombosis Risk of High Homocysteine Susceptibility Gene Screening Gene profile The genes evaluating the risk of drug use are MTHFR and MTRR. D.23.11 Anti-thrombotic Protein Deficiency Susceptibility Gene Screening for Thrombosis Risk Gene profile The genes evaluating the risk of drug use are PROC and THBD.