Inflammation, Infection, and Microbiome in Cancers: Evidence, Mechanisms, and Implications

Preface
Contents
About the Editor
Chapter 1: Microbiome and the Hallmarks of Cancer
1.1 Introduction
1.1.1 Oncomicrobes
1.1.2 Hallmarks of Cancer
1.1.3 Microbiota and Cancer
1.2 Mechanisms of Microbes and the Hallmarks of Cancer
1.2.1 Cellular Proliferation
1.2.2 Deregulating Cellular Energetics
1.2.3 Avoiding Immune Destruction
1.2.4 Tumor-Promoting Inflammation
1.2.5 Genome Instability and Mutation
1.2.6 Remaining Hallmarks
1.3 Additional Microbial Factors that Influence Cancer
1.3.1 Establishing a Chronic Infection
1.3.2 Microbial Interactions
1.3.3 Location and Tumorigenesis
1.4 Conclusion
References
Chapter 2: Microbiome in Human Gastrointestinal Cancers
2.1 Introduction
2.2 Microbiome in Gastrointestinal Health
2.2.1 Functions of Bacteria in the Gastrointestinal Tract
2.2.2 Functions of Virus in the Gastrointestinal Tract
2.2.3 Functions of Fungi in the Gastrointestinal Tract
2.2.4 Functions of Archaea in the Gastrointestinal Tract
2.3 Microbiome in Gastrointestinal Cancers
2.3.1 Microbiome Alteration in Esophageal Cancer
2.3.2 Microbiome Alteration in Gastric Cancer
2.3.3 Microbiome Alteration in Pancreatic Cancer
2.3.4 Microbiome Alteration in Liver Cancer
2.3.5 Microbiome Alteration in Colorectal Cancer
2.4 Gut Microbe Interactions in Gastrointestinal Health and Cancer
2.5 Therapeutic Manipulation of the Gut Microbiome for Prevention and Treatment of Gastrointestinal Cancers
2.5.1 Fecal Microbiome Transplantation
2.5.2 Phage Therapy
2.5.3 Use of Antimicrobials
2.5.4 Probiotics and Prebiotics
References
Chapter 3: The Gut Microbiome and Colorectal Cancer
3.1 Introduction
3.2 Dysbiosis and CRC
3.3 CRC-Associated Microbiota
3.3.1 Fusobacterium nucleatum
3.3.2 Enterotoxigenic Bacteroides fragilis
3.3.3 Escherichia coli
3.3.4 Streptococcus gallolyticus (Previously Known as Streptococcus bovis Biotype I and II/2)
3.3.5 Salmonella
3.4 Mechanisms by Which the Gut Microbiome Contribute to CRC
3.4.1 Modulation of Host Immune Responses
3.4.2 Stimulation of Cellular Proliferation
3.4.3 Promotion of DNA Damage
3.4.4 Production of Metabolites
3.4.4.1 Short-Chain Fatty Acids
3.4.4.2 Secondary Bile Acids
3.5 Conclusion
References
Chapter 4: The Impacts of Salmonella Infection on Human Cancer
4.1 Introduction
4.2 Human Exposure Data
4.2.1 Non-typhoidal Salmonella
4.2.2 Typhoidal Salmonella
4.3 Association with Human Cancer
4.3.1 Colorectal Cancer and Its Precursor Lesions
4.3.2 Biliary Tract Cancer and Its Precursor Lesions
4.4 Summary and Future Direction
References
Chapter 5: Biomarkers of Esophageal Cancers and Precancerous Lesions
5.1 Introduction
5.2 Biomarkers of Esophageal Cancer and Precancerous Lesions in Clinical Application
5.2.1 Human Epidermal Growth Factor Receptor 2 or HER2
5.2.1.1 HER2 Amplification and Overexpression in Esophageal Cancer
5.2.1.2 HER2 Clinical Application in EAC
5.2.2 Programmed Cell Death 1 or PD-L1: Immunotherapy and Expression in Esophageal Cancer
5.2.2.1 PD-L1 Immunotherapy in Clinical Application
5.2.2.2 PD-L1 Expression in Esophageal Cancer
5.2.3 Vascular Endothelial Growth Factor
5.2.3.1 VEGF Clinical Application in EAC
5.2.3.2 VEGF Expression in Esophageal Carcinoma
5.2.4 Other Biomarkers in Clinical Application for Diagnosis of EAC and Precancerous Lesions
5.3 Molecular Markers in Development for Esophageal Cancer and Precancerous Lesions
5.3.1 Gene Mutations and Aberrant Expression in Esophageal Cancer and Precancerous Lesions
5.3.1.1 Esophageal Adenocarcinoma
5.3.1.2 Esophageal Squamous Cell Carcinoma
5.3.1.3 Molecular Gene Mutation: ESCC Versus EAC
5.3.2 Epigenetic Markers: Methylation, miRNA, and lncRNA
5.3.2.1 DNA Methylation
5.3.2.2 MicroRNAs (miRNAs)
5.3.2.3 Long Non-coding RNAs (lncRNAs)
5.4 Microbiome Application in Esophageal Cancer and Precancerous Lesion
5.5 Other Promising Biomarkers for Esophageal Cancer
5.5.1 Circulating Tumor Cells
5.5.2 Circulating Cell-Free DNA
5.5.3 Breath Volatile Organic Compounds
5.6 Conclusion and Future Directions
References
Chapter 6: Epithelial and Immune Cell Responses to Helicobacter pylori That Shape the Gastric Tumor Microenvironment
6.1 Introduction: Helicobacter pylori and the Attributes of Virulence
6.2 Early Epithelial and Immune Cell Responses to Helicobacter Infection
6.2.1 Induction of Protective Responses
6.2.2 Recruitment and Polarization of Macrophages
6.2.3 Recruitment and Polarization of Myeloid-Derived Suppressor Cells
6.2.4 Induction of CD44V9 and Metaplasia
6.2.5 Induction of Programmed Death-Ligand 1 (PD-L1)
6.3 Inflammation and Hypoxia
6.3.1 Regulation of Inflammation by Hypoxia-Inducible Factors (HIFS)
6.3.2 HIFs and Cancer
6.3.3 HIF Signaling Targets
6.3.4 HIF-1a and Increased Glycolysis in Tumor Cells
6.4 Impact of Early Epithelial and Immune Cell Responses on the Gastric Tumor Microenvironment
6.4.1 Defining the Gastric Tumor Microenvironment
6.4.2 Resistance to Immunotherapy
6.5 Conclusions
References
Chapter 7: Gut Microbiome and Liver Cancer
7.1 Liver Cancer Types and Risk Factors
7.1.1 Hepatocellular Carcinoma
7.1.2 Intrahepatic Cholangiocarcinoma
7.1.3 Metastatic Liver Malignancies
7.2 Carcinogenesis of Liver Cancer
7.2.1 Oncogenic Pathways in HCC
7.2.2 Oncogenic Pathways in Cholangiocarcinoma
7.3 Infectious Disease and Liver Cancer
7.3.1 Chronic Viral Hepatitis and HCC
7.3.2 Liver Fluke and Cholangiocarcinoma
7.4 Overview of Gut Microbiome and Cancer
7.5 Relevant Liver and GI Features for the Gut-Liver Axis
7.5.1 Intrahepatic Circulation
7.5.2 Liver as an Immunological Organ
7.5.3 Pattern Recognition Receptors
7.5.4 Intestinal Barrier
7.5.5 Bacterial Translocation
7.6 Gut Microbiome and Liver Cancer-Associated Conditions
7.6.1 Obesity
7.6.2 Nonalcoholic Fatty Liver Disease
7.6.3 Alcoholic Liver Disease
7.6.4 Cirrhosis
7.6.5 Autoimmune Hepatitis
7.7 Gut Microbiome Regulates Liver Cancer
7.7.1 Lipopolysaccharides
7.7.2 Bile Acids
7.7.3 Short-Chain Fatty Acids
7.7.4 Immune Cells
7.8 Gut Microbiome and Immunotherapy
7.9 Summary
References
Chapter 8: The Microbiome and Urologic Cancers
8.1 Introduction
8.2 The Urinary System
8.3 Bladder Cancer and Microbes
8.4 Renal Cell Carcinoma
8.5 Prostate Cancer
8.6 Gut Microbiome and Urinary Cancers
8.7 Conclusion
References
Chapter 9: Role of Infections and Tissue Inflammation in the Pathology of the Fallopian Tube and High-Grade Serous Ovarian Can...
9.1 Introduction
9.2 Classification of Epithelial Ovarian Cancer
9.3 HGSOC: Molecular Characteristics, Origins, and Main Risk Factors
9.4 The Fallopian Tube as a Tissue of Origin of Ovarian Cancer
9.5 Epidemiology Studies of HGSOC Prevalence and Main Risk Factors
9.5.1 Model of ``Incessant´´ Ovulation as the Main Driver of HGSOC
9.5.2 The Inheritable Risk Associated with BRCA1/2 Status
9.5.3 Recurrent Episodes of Infection and the Risk of HGSOC Development
9.5.4 The Serologic Evidence of Chlamydia Infection in Cancer Patients
9.5.5 Coinfections and HGSOC
9.5.6 Contribution of the Microbiota to the Inflamed Environment
9.5.7 Infertility and Risk of HGSOC Development
9.6 Infection of the Fallopian Tube Pathogen-Host Interaction and Long-Term Changes in Homeostasis
9.7 Patient-Derived Organoids: In Vitro Diseases Modeling and Translational Applications
9.8 Regulation of the Epithelial Renewal in the Upper Genital Tract
9.8.1 Stem Cells of the Ovary
9.8.2 Stem Cells of the Fallopian Tube- Pax8+ Progenitors
9.9 Chronic Chlamydia Infection in Human Fallopian Tube Organoids
9.10 Patient-Derived HGSOC Organoids: Evidence of Early Changes in Regulation of the Stem-Cell Niche
9.11 Wnt Signaling in Health and Disease
9.12 Tissue Inflammation as a Precursor of the Tumor Microenvironment
9.13 Tumor Heterogeneity and Local Microenvironment
9.14 Inflammation and Response to Immunotherapy
9.15 Contribution of the Microbiota to Disease Progression and Response to Immunotherapy
9.16 Future Directions in the Research of Tubal Pathology and HGSOC Development
References
Chapter 10: Commensal Microbes and Their Metabolites: Influence on Host Pathways in Health and Cancer
10.1 Introduction
10.2 Microbe-Derived Metabolites
10.2.1 Bile Acids
10.2.2 Mediators of Oxidative Stress
10.2.3 Polyamines
10.2.4 Short-Chain Fatty Acids
10.3 Future Directions
References
Chapter 11: Diet, Microbiome, Inflammation, and Cancer
11.1 Introduction
11.2 Microbiome, Inflammation, and Cancer
11.3 Diet and Microbiome Interactions
11.3.1 Diet Pattern
11.3.2 Key Components of Inflammation-Related Diet
11.3.3 Dietary Fiber
11.3.4 Fat
11.3.5 Protein
11.3.6 Micronutrients and Bioactive Components of Plant Foods
11.3.7 Diet and Oral Microbiome
11.4 Cancer Related to Diet, the Microbiome, and Inflammation
11.4.1 Colorectal Cancer
11.4.2 Liver Cancer
11.4.3 Pancreatic Cancer
11.4.4 Other Malignancies
11.5 Conclusions and Clinical Implications
References
Chapter 12: Autophagy and Cancer: Current Biology and Drug Development
12.1 Introduction
12.2 Autophagy Pathway
12.2.1 Autophagy Overview
12.2.2 Initiation of Phagophore Formation
12.2.3 Expansion and Elongation of the Autophagosome Membrane
12.2.4 Cargo Selection
12.2.5 Fusion with the Lysosome
12.3 Dual Roles of Autophagy in Cancer Initiation Versus Progression
12.3.1 Autophagy and Cancer Suppression
12.3.2 Autophagy and Cancer Progression
12.3.3 Autophagy and Cancer Stem Cells
12.4 Mitophagy: Adaptation to Drive Tumor Progression
12.4.1 Mitophagy Overview
12.4.2 Mitophagy and Cancer Metabolism
12.4.3 Mitophagy and Iron Homeostasis
12.5 Autophagy-Targeted Drug Development for Cancer Therapy
12.5.1 Clinical Trials Targeting Autophagy for Cancer Therapy
12.5.2 Targeting Autophagy in CSCs
12.5.3 Targeting Mitophagy
12.5.4 Targeting Ferroptosis
12.5.5 Vitamin D and Autophagy
12.6 Conclusions/Perspectives
References
Chapter 13: Mitochondrial Regulation of Inflammation in Cancer
13.1 Introduction
13.2 Mitochondrial ROS
13.3 Mitochondrial Dysfunction
13.4 Mitochondrial ROS and Dysfunction Promote Inflammation
13.5 Mitochondria and Cellular Signaling During Inflammation
13.6 Hypoxia and Inflammation
13.7 Mitochondria and Cytokine Production via Inflammasomes
13.8 Targeting Inflammation and the Mitochondria
13.9 Conclusion
References
Chapter 14: Modern Germ-Free Study Designs and Emerging Static Housing Technology in a Growing ``Human Microbiome´´ Research M...
14.1 Introduction
14.2 Market Value and Exponential Growth of the GF and the Human Microbiome Industry
14.3 Basic Animal Germ-Free Biology and Gnotobiology from Studies in the 1960s
14.4 Retroviruses and mdr1a May Influence Pharmacodynamic/Microbiome Studies in GF Mice
14.5 Mechanism of Disease in Modern GF Study Designs
14.5.1 The Gut Microbiota of Preterm Infants Has a Unique Effect in the Gut of GF Animals
14.5.2 The Human Gut Microbiota from Cancer Patients Induce Cancer in GF Animals
14.5.3 The Human Microbiome Modulates Immunotherapies and Side Effects in GF Animals
14.5.4 The Variable Human Microbiota May Induce Inflammatory Bowel Disease in GF Models
14.5.5 Nutrients and Microbial Metabolites Enhance Therapeutic Efficacy of Immunomodulators
14.5.6 Germ-Free Models Enable the Study of NAFLD and Oral and Lung Cancer
14.5.7 Modern Germ-Free Models Provide Insight on Muscle-Skeletal, Mental, and Brain Health
14.5.8 Sex-dependent Microbiome-driven Vascular, Immune Cell Biology, and Disease Gender Bias
14.5.9 Single Bacterial Genes Modulate the Intestinal Phenotype in GF Models
14.5.10 Human Enteroviral Infections Induce Microbiome Changes in Humanized GF Models
14.5.11 GF Animals as Models to Study the Biology and Filtration Materials Against COVID-19
14.6 Historic Evolution of Germ-Free Housing Technologies
14.7 Portable Emerging Non-pressurized Housing GF Technology
14.8 Conclusion
References
Chapter 15: Machine Learning in Identification of Disease-Associated Microbiota
15.1 Introduction
15.2 Materials
15.2.1 Software
15.2.2 Datasets
15.3 Methods
15.3.1 Data Import
15.3.2 Data Preprocessing
15.3.3 Random Forest
15.3.4 Support Vector Machine
15.3.5 Logistic Regression
15.3.6 Multi-layer Perceptron Neural Network
15.3.7 Model Evaluation
15.3.8 Feature Aggregation
15.4 Results
15.5 Summary
References
Chapter 16: Mediation Analysis of Microbiome Data and Detection of Causality in Microbiome Studies
16.1 Introduction
16.2 Traditional Mediation Models
16.2.1 Typical Features of SEM-Based Mediation Framework
16.2.1.1 Product of Coefficients Method
16.2.1.2 Difference of Coefficients Method
16.2.1.3 Remarks
16.2.2 Counterfactual-Based Mediation Framework
16.2.2.1 Lewis´ Counterfactual Model
16.2.2.2 Rubin´s Counterfactual Framework
16.2.2.3 Counterfactual-Based Mediation Framework
Redefine Causality as a Statistical Methodology Rather than Philosophical Ontology
Redefine Causal Direct and Causal Indirect Effects
Generalize the Counterfactual Mediation Analysis
Allow for the Presence of Independent Variable-Mediator Interactions
Add No-Confounding Assumptions to Ensure a Casual Interpretation
Final Check with a Sensitivity Analysis
16.2.2.4 The Linking of Counterfactual-Based and SEM-Based Mediation Analyses
16.2.2.5 Typical Features of Counterfactual-Based Mediation Framework
16.3 Mediation Models in Omics Studies
16.3.1 Test Multiple Putative Mediators Simultaneously Based on Permutation (MultiMed)
16.3.2 Reduce High Dimensionality of Mediators Through Regularization or Penalization (HIMA)
16.3.3 Transform High-Dimensional Mediators into Low-Dimensional and Uncorrelated Mediators Using the Spectral Decomposition (...
16.3.4 Remarks
16.4 Specifically Designed Mediation Models in Microbiome Studies
16.4.1 Distance-Based Omnibus Test of Mediation Effect (MedTest)
16.4.1.1 MedTest Method
16.4.1.2 Using Distance Metrics to Reduce High Dimensionality
16.4.1.3 Remarks
16.4.2 Multivariate Omnibus Distance Mediation Analysis (MODIMA)
16.4.2.1 MODIMA Method
16.4.2.2 Permutation Testing of Mediation Effects
16.4.2.3 Remarks
16.4.3 Causal Compositional Mediation Model (CCMM)
16.4.3.1 CCMM Method
16.4.3.2 Hypothesis Testing of Mediation Effects
16.4.3.3 Remarks
16.4.4 Isometric Log-Ratio Transformation for Microbiome Mediation (IsometricLRTMM)
16.4.4.1 IsometricLRTMM Method
16.4.4.2 Inference on the Ilr-Transformed Mediation Effect
16.4.4.3 Remarks
16.4.5 Sparse Microbial Causal Mediation Model (SparseMCMM)
16.4.5.1 Casual Mediation Model
Compositional (Log-Ratio Analysis) Model
Dirichlet Regression
16.4.5.2 Hypothesis Testing of Microbiome Mediation Effects
16.4.5.3 Remarks
16.4.6 Mediation Analysis for Zero-Inflated Mediators (MedZIM)
16.4.6.1 MedZIM Method
16.4.6.2 Mediation Effect and Direct Effect Under the Counterfactual-Based Framework
16.4.6.3 Remarks
16.4.7 Nonparametric Entropy Mediation (NPEM)
16.4.7.1 NPEM Method
16.4.7.2 Hypothesis Testing of Mediation Using Mutual Information
Univariate Entropy Measure
Bivariate Entropy Measure
16.4.7.3 Remarks
16.4.8 Some Comments About Current Mediation Models for Microbiome Data Analysis
16.4.8.1 Direction of Mediation Methods in Microbiome Studies
16.4.8.2 Who Are Mediators: Microbial Taxa, Host, or Environment Factors?
16.4.8.3 Modeling Mediation Effects of Microbiome Data Is a Real Challenge
16.4.8.4 Developing Longitudinal Mediation Models for Microbiome Data Analysis Is Difficult
16.4.8.5 Multicollinearity Especially Challenges the Mediation Analysis of Microbiome Data
16.4.8.6 Model Fitting Assumptions and Modeling Issues Need to be Considered
16.4.8.7 Incorporating Multilevel SEM Modeling into Mediation Methods
16.4.8.8 Mediation Analysis Is Not Causation Analysis Yet
16.5 Detecting Causality in Microbiome Studies
16.5.1 Causality as a Philosophic Ontology or Metaphysics
16.5.2 Causality as a Methodology and Specifically a Statistical Theory of Probability
16.5.3 How to Understand Establishing Causality in Microbiome Studies
References