Systems Biology of MicroRNAs in Cancer

Contents
About the Editors
Chapter 1: The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective
1.1 Biological Facts About miRNA Biogenesis and Function
1.1.1 miRNA Biogenesis
1.1.2 miRNA Function
1.2 The Role of miRNAs in Cancer Progression, Diagnosis, and Therapy
1.2.1 Genome-Level Alterations in miRNAs
1.2.2 Oncogenic and Tumor-Suppressive miRNAs
1.2.3 miRNAs in Cancer Diagnostics and Therapy
1.3 miRNAs in Cancer Gene Regulatory Networks
1.3.1 miRNA Clusters: Groups of Similarly Regulated miRNAs
1.3.2 Target Hubs: Genes Regulated by Many miRNAs
1.3.3 miRNA Cooperativity: Synergistic Gene Regulation by Multiple miRNAs
1.3.4 Network Motifs: miRNA-Enriched Feedback and Feedforward Loops
1.4 Bioinformatics and Systems Approaches as the ``Lifeline´´ to Navigate miRNA Networks
References
Chapter 2: Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies
2.1 Introduction
2.2 Circulating miRNAs
2.3 Function of Circulating miRNAs
2.4 Circulating ncRNAs as Cancer Biomarkers
2.4.1 MiRNAs
2.4.2 LncRNAs and Other ncRNAs
2.5 Methods and Challenges to Detect Circulating miRNAs
2.6 Bioinformatics Analyses
2.6.1 Databases of miRNA: Target RNA Interaction
2.6.2 Databases of Transcription Factor: miRNA Gene Interaction
2.6.3 Construction of Regulatory Networks
2.6.4 Databases of miRNAs with Cancer Biomarker Potential
2.6.5 Application of Neural Networks and Meta-Analysis to Identify Robust Biomarkers
2.7 Conclusions
References
Chapter 3: Regulation of Immune Cells by microRNAs and microRNA-Based Cancer Immunotherapy
3.1 Introduction
3.2 Innate Immunity
3.2.1 Macrophages
3.2.2 Dendritic Cells
3.2.3 Natural Killer Cells
3.3 Adaptive [Acquired] Immunity
3.3.1 T-Helper Cells
3.3.2 Cytotoxic T Cells
3.3.3 Regulatory T Cells
3.4 miRNA-Mediated Regulation in the Tumor Microenvironment
3.5 Immune Checkpoint Molecules
3.6 miRNAs as Potential Targets for Immunotherapy
3.7 Challenges in miRNA-Based Therapy
3.8 Conclusions
References
Chapter 4: Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation
4.1 Introduction of miRNAs and Their Role in Cancer
4.2 miRNA Target Prediction Tools
4.2.1 Common Features for miRNA Target Prediction
4.2.2 Machine Learning Based Algorithms for miRNA Target Prediction
4.3 Best Practices for miRNA Target Prediction
4.3.1 Workflow to Detect miRNA-mRNA Target Sites
4.3.2 Validation of miRNA Target Prediction
4.4 miRNA-Based Therapeutics for Cancer
References
Chapter 5: Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research
5.1 Human miRNA Regulation
5.2 The Scope and Organization of the Chapter
5.3 Repositories for miRNA: Catalogs and Genome Browsers
5.3.1 miRNA Gene Catalogs
5.3.2 miRNAs in Genome Browsers
5.4 Gateways for miRNAs: Integrative Platforms
5.5 miRNA Gene Regulation: TFs and Cellular Context
5.6 miRNA-Target Prediction: Experiments and Validations
5.6.1 miRNA-Target Prediction Tools and Resources
5.6.2 miRNA-Target Prediction Validation Databases
5.7 miRNA-Target Databases: Networks and Pathways
5.8 miRNA Sponging: ceRNA and lncRNA Interactions
5.9 Genomic miRNA Databases: Variations and isomiRs
5.10 miRNA Dysregulation: Diseases, Cancer, and Signaling
5.10.1 Disease-Related miRNA Databases
5.10.2 Cancer-Related miRNA Databases
5.11 Summary and Future Perspectives
References
Chapter 6: Bioinformatics Methods for Modeling microRNA Regulatory Networks in Cancer
6.1 Introduction
6.2 Bioinformatics Methods for miRNA-Gene Regulatory Networks
6.3 Bioinformatics Methods for Analyzing miRNA-miRNA Regulatory Networks
6.3.1 Sequence Similarity: Complementary Base Pairing
6.3.2 Higher-Order Chromatin Conformation
6.3.3 Co-regulated Genes
6.3.4 Functional Similarity
6.3.5 Disease Phenotype
6.4 Bioinformatics Methods for Identifying miRNA-Mediated ceRNA Networks
6.5 Future Directions
References
Chapter 7: Analysis of the p53/microRNA Network in Cancer
7.1 Introduction to p53 Biology
7.2 p53 and the miRNA World: Current State of the Art
7.2.1 The miR-34 Genes
7.2.2 The miR-200 Family
7.2.3 The miR-192 Family
7.2.4 Additional p53-Regulated miRNAs
7.2.5 Direct Regulation of p53 Expression by miRNAs
7.2.6 Indirect Regulation of p53 by miRNAs
7.2.7 Direct Involvement of p53 in miRNA Processing and Maturation
7.2.8 The p53 Relatives p63 and p73 in the Regulation of miRNAs
7.3 Alterations of the p53/miRNA Network in Human Cancer
7.3.1 Cancer-Specific Alteration of the miR-15/16 Encoding dLEU2 Gene
7.3.2 Cancer-Specific Alterations of the miR-34 Family
7.3.3 Cancer-Specific Alterations of the miR-200 Family
7.3.4 Cancer-Specific Alterations of the miR-192 Family
7.3.5 Other p53-Induced miRNAs Inactivated in Cancer
7.3.6 Mutations in the miRNA Processing Machinery in Cancer
7.4 Approaches to Study p53-Regulated miRNAs and Their Targets
7.4.1 Identification of p53-Regulated miRNAs
7.4.2 Confirmation of Direct Regulation by p53 Using ChIP Approaches
7.4.3 Identification of miRNA Targets
7.4.4 Follow-Up Analysis
7.4.5 Outlook
References
Chapter 8: Machine Learning Using Gene-Sets to Infer miRNA Function
8.1 Introduction
8.2 Statistical Preliminaries
8.2.1 Linear Modeling and Penalized Regression
8.2.2 Gene Signature Quality Control
8.2.3 Rank Product Statistic
8.3 Approach to Choosing Representative Gene Signatures
8.4 Evidence Across Tissue Types for miRNA Associations to Key Gene Signatures
8.5 Hallmarks-Associated miRNA Preferentially Regulate Tumor Suppressor Genes
8.6 Conclusions and Future Directions
References
Chapter 9: miRNA:miRNA Interactions: A Novel Mode of miRNA Regulation and Its Effect On Disease
9.1 Introduction
9.2 Discovery of miRNA:miRNA Interactions
9.3 Direct miRNA:miRNA Interactions
9.3.1 Pri-miRNA:miRNA
9.3.2 Direct Binding between Mature miRNAs
9.4 Indirect miRNA:miRNA Interactions
9.4.1 The Role of Transcriptional Regulation
9.4.2 The Role of the Biogenesis Components
9.5 Global miRNA:miRNA Interactions
9.5.1 The Wide Effect of a Small Set of miRNAs on Cell Functioning
9.6 miRNA:miRNA Dysregulation
9.7 How Can miRNA:miRNA Interactions Be Utilised for Cancer Therapy?
9.8 Current Limitations to miRNA:miRNA Discovery
9.8.1 Cell Specificity
9.8.2 Identification
9.9 Conclusions
References
Chapter 10: ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively o...
10.1 Introduction
10.2 The clustMMRA Pipeline
10.3 ClustMMRA Usage
10.4 Application to Pediatric Cancers
10.5 Discussion
10.6 Code Availability
References
Chapter 11: 3D Modeling of Non-coding RNA Interactions
11.1 Introduction
11.2 Structure Modeling of ncRNAs
11.3 Modeling of miRNA-mRNA-Argonaute complexes
11.4 Computational Pipeline for the Structural Modeling of ncRNAs, Proteins, and Their Interactions
11.5 Advances in Modeling Protein-ncRNA Interactions Using Deep Learning
11.6 Case Study
11.6.1 SLC16A1-AS1 lncRNA Interacts with Transcription Factor E2F1 and Modulates Its Activities
11.6.1.1 Retrieval of Sequence and Secondary Structure Prediction of SLC16A1-AS1
11.6.1.2 Preparation and Optimization of the SLC16A1-AS1 and E2F1 Tertiary Structure
11.6.1.3 Prediction and Prioritization of E2F1 Binding Sites on SLC16A1-AS1 lncRNA
11.6.1.4 Molecular Docking Between SLC16A1-AS1 and E2F1
11.6.1.5 Molecular Dynamics Simulation of Best Binding Poses of SLC16A1-AS1 lncRNA and E2F1
11.6.1.6 Modeling of the SLC16A1-AS1/E2F1 Complex on the Promoter Site of the MCT1 Gene
11.7 Future Directions for the 3D Interactions of ncRNAs
References