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