Systems Biology of MicroRNAs in Cancer

Description

This book provides an update on the latest development in the field of microRNAs in cancer research with an emphasis on translational research. Since the early 2000s, microRNAs have been recognized as important and ubiquitous regulators of gene expression. Soon it became evident that their deregulation can cause human diseases including cancer. This book focuses on the emerging opportunities for the application of microRNA research in clinical practice. In this context, computer models are presented that can help to identify novel biomarkers, e.g. in circulating microRNAs, and tools that can help to design microRNA-based therapeutic interventions. Other chapters evaluate the role of microRNAs in immunotherapy, immune responses and drug resistance. Covering key topics on microRNAs in cancer research this book is a valuable resource for both emerging and established microRNA researchers who want to explore the potential of microRNAs as therapeutic targets or co-adjuvants in cancer therapies. 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