Integration of Omics Approaches and Systems Biology for Clinical Applications

Herausgeber: Vlahou, Antonia; Zoidakis, Jerome; Mischak, Harald; Magni, Fulvio

• Gebundenes Buch

Produktbeschreibung

Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to explore the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of Omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. * Describes a range of state of the art omics analytical platforms * Covers all aspects of the systems biology approach-from sample preparation to data integration and bioinformatics analysis * Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of Omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

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Produktdetails

• Verlag: Wiley
• Seitenzahl: 384
• Erscheinungstermin: 21. Februar 2018
• Englisch
• Abmessung: 279mm x 221mm x 25mm
• Gewicht: 1270g
• ISBN-13: 9781119181149
• ISBN-10: 1119181143
• Artikelnr.: 54587676

Autorenporträt

ANTONIA VLAHOU is Co-director of the Proteomics Research Unit at the Biomedical Research Foundation, Academy of Athens. FULVIO MAGNI is a Full Professor at the Faculty of Medicine and Surgery, University Milano-Bicocca. HARALD MISCHAK is Professor for Proteomics and Systems Medicine at the University of Glasgow and is the Director of Mosaiques diagnostics. JEROME ZOIDAKIS is a Research Scientist at the Proteomics Research Unit at the Biomedical Research Foundation, Academy of Athens.

Inhaltsangabe

List of Contributors xv
Preface xix
Acknowledgement xx
Part I Platforms for Molecular Data Acquisition and Analysis 1
1 Clinical Data Collection and Patient Phenotyping 3
Katerina Markoska and Goce Spasovski
1.1 Clinical Data Collection 3
1.1.1 Data Collection for Clinical Research 3
1.1.2 Clinical Data Management 3
1.1.3 Creating Data Forms 4
1.1.3.1 Different Data Forms According to the Type of Study 4
1.1.4 Case Report Form (CRF) 5
1.1.4.1 CRF Standards Characterization 5
1.1.4.2 Electronic and Paper CRFs 6
1.1.5 Methods and Forms for Clinical Data Collection and/or Extraction from
Patient's Records 6
1.1.5.1 Electronic Health Records (EHRs) 6
1.1.6 Data Collection Workflow 7
1.1.6.1 Defining Baseline and Follow?]Up Data 7
1.1.6.2 Medical Coding 7
1.1.6.3 Errors in Data Collection and Missing Data 8
1.1.6.4 Data Linkage, Storage, and Validation 8
1.2 Patient Phenotyping 8
1.2.1 Approaches in Defining Patient Phenotype 9
1.2.2 Phenotyping CKD Patients 9
1.3 Concluding Remarks 10
References 10
2 Biobanking, Ethics, and Relevant Legal Issues 13
Brigitte Lohff, Thomas Illig, and Dieter Tröger
2.1 Introduction 13
2.2 Brief Historical Derivation to the Ethical Guidelines in Medical
Research 13
2.2.1 1900: Directive to the Head of the Hospitals, Polyclinics, and Other
Hospitals 14
2.2.2 1931: Guidelines for Novel Medical Treatments and Scientific
Experimentation 14
2.2.3 1947: The Nuremberg Code 14
2.2.4 1964: The Declaration of Helsinki 14
2.2.5 The Declaration of Helsinki and Research on Human Materials and Data
15
2.2.6 2013: Current Valid Declaration of Helsinki in the 7th Revision 15
References 15
2.3 Biobanking: Definition, Role, and Guidelines of National and
International Biobanks 16
2.3.1 Introduction 16
2.3.2 Definition of Biobanks 17
2.3.3 Human Biobank Types 17
2.3.4 Clinical Biobanks 17
2.3.5 Governance in HUB 18
2.3.6 Epidemiological Biobanks 18
2.3.7 Quality of Samples 19
2.3.8 Harmonization and Cooperation of Biobanks 19
2.3.9 Situation in Germany 20
2.3.10 Situation in Europe and Worldwide 20
2.3.11 Definition of Ownership, Access Rights, and Governance of Biobanks
20
2.3.12 IT in Biobanks 21
2.3.13 Financial Aspects and Sustainability 21
2.3.14 Conclusion 21
References 22
2.4 Tasks of Ethics Committees in Research with Biobank Materials 23
2.4.1 General Basic Concept 23
2.4.1.1 The Application Procedure 23
2.4.2 About the Respective Ethics Commissions 23
2.4.3 The Establishment of Biobanks 24
Further Reading 24
3 Nephrogenetics and Nephrodiagnostics: Contemporary Molecular Approaches
in the Genomics Era 26
Constantinos Deltas
3.1 Introduction 26
3.2 Applications of Molecular Diagnostics 27
3.3 Aims of Present?]Day Molecular Genetic Investigations 28
3.4 Material Used for Genetic Testing 28
3.5 Clinical, Genetic, and Allelic Heterogeneity 29
3.6 Oligogenic Inheritance 31
3.7 ADPKD, Phenotypic Heterogeneity, and Genetic Modifiers 32
3.8 Collagen IV Nephropathies, Genetic and Phenotypic Heterogeneity, and
Genetic Modifiers 33
3.9 CFHR5 Nephropathy, Phenotypic Heterogeneity, and Genetic Modifiers 36
3.10 Unilocus Mutational and Phenotypic Diversity (UMPD) 38
3.11 Next?]Generation Sequencing (NGS) 39
3.12 Conclusions 40
Acknowledgments 41
References 41
4 The Use of Transcriptomics in Clinical Applications 49
Daniel M. Borràs and Bart Janssen
4.1 Introduction 49
4.2 Clinical Applications of Transcriptomics: Cases and Potential Examples
53
4.2.1 PCR Applications 53
4.2.2 Microarrays 55
4.2.3 Sequencing 57
4.2.4 Discussion 60
References 63
Further Reading 66
5 miRNA Analysis 67
Theofilos Papadopoulos, Julie Klein, Jean?]Loup Bascands, and Joost P.
Schanstra
5.1 miRNA Biogenesis, Function, and Annotation 67
5.2 Annotation of miRNAs 69
5.3 miRNAs: Location, Stability, and Research Methods 69
5.3.1 miRNA Analysis and Tissue Distribution 69
5.3.2 miRNAs in Body Fluids 69
5.3.3 Stability of miRNAs 71
5.3.4 Methods to Study miRNAs 71
5.3.4.1 Sampling 71
5.3.4.2 Extraction Protocols 71
5.3.4.3 miRNA Detection Techniques 72
5.3.4.4 Data Processing and Molecular Integration 73
5.3.4.5 In Vitro Target Validation 77
5.4 Use of miRNA In Vivo 79
5.4.1 Chemically Modified miRNAs 82
5.4.2 miRNA Sponges or Decoys 82
5.4.3 Modified Viruses 82
5.4.4 Microvesicles 82
5.4.5 The Polymers 83
5.4.6 Inorganic Nanoparticles 83
5.5 miRNAs as Potential Therapeutic Agents and Biomarkers: Lessons Learned
So Far 83
5.5.1 miRNAs as Potential Therapeutic Agents 83
5.5.2 miRNAs as Potential Biomarkers 84
5.5.2.1 Cancer 84
5.5.2.2 Metabolic and Cardiovascular Diseases 84
5.5.2.3 Miscellaneous Diseases 84
5.6 Conclusion 84
References 85
6 Proteomics of Body Fluids 93
Szymon Filip and Jerome Zoidakis
6.1 Introduction 93
6.2 General Workflow for Obtaining High?]Quality Proteomics Results 93
6.3 Body Fluids 95
6.3.1 Blood 95
6.3.1.1 Plasma 95
6.3.1.2 Serum 96
6.3.2 Urine 96
6.3.3 Cerebrospinal Fluid (CSF) 96
6.3.4 Saliva 96
6.4 Sample Collection and Storage 97
6.5 Sample Preparation for MS/MS Analysis 97
6.5.1 Protein Separation 97
6.5.1.1 Electrophoresis?]Based Methods 98
6.5.1.2 Liquid Chromatography Methods 98
6.5.2 Sample Preparation for MS/MS (Tryptic Digestion) 102
6.5.3 Separation of Peptides 102
6.6 Analytical Instruments 103
6.7 Data Processing and Bioinformatics Analysis 103
6.7.1 Peptide and Protein Identification 103
6.7.2 Protein Quantitation 103
6.7.3 Data Normalization (Example of Label?]Free Proteomics Using Ion
Intensities) 104
6.7.4 Statistics in Proteomics Analysis 105
6.8 Validation of Findings 105
6.9 Clinical Applications of Body Fluid Proteomics 106
6.10 Conclusions 109
References 109
7 Peptidomics of Body Fluids 113
Prathibha Reddy, Claudia Pontillo, Joachim Jankowski, and Harald Mischak
7.1 Introduction 113
7.2 Clinical Application of Peptidomics 113
7.3 Different Types of Body Fluids Used in Biomarker Research 113
7.3.1 Blood 113
7.3.2 Urine 114
7.4 Sample Preparation and Separation Methods for Mass Spectrometric
Analysis 115
7.4.1 Depletion Strategies 115
7.4.1.1 Ultrafiltration 115
7.4.1.2 Precipitation 116
7.4.1.3 Liquid Chromatography 116
7.4.1.4 Capillary Electrophoresis 116
7.4.1.5 Instrumentation 117
7.5 Identification of Peptides and Their Posttranslational Modifications
117
7.6 Urinary Peptidomics for Clinical Application 118
7.6.1 Kidney Disease 118
7.6.2 Urogenital Cancers 119
7.6.3 Blood Peptides as Source of Biomarkers 120
7.6.4 Proteases and Their Role in Renal Diseases and Cancer 120
7.7 Concluding Remarks 122
References 122
8 Tissue Proteomics 129
Agnieszka Latosinska, Antonia Vlahou, and Manousos Makridakis
8.1 Introduction 129
8.2 Tissue Proteomics Workflow 130
8.3 Tissue Sample Collection and Storage 132
8.4 Sample Preparation 133
8.4.1 Homogenization of Fresh?]Frozen Tissue 133
8.4.1.1 Mechanical Methods of Tissue Homogenization 135
8.4.1.2 Chemical Methods of Tissue Homogenization 136
8.4.2 LCM 136
8.4.3 Protein Digestion 137
8.5 Overcoming Tissue Complexity and Protein Dynamic Range: Separation
Techniques 138
8.5.1 Subcellular Fractionation 139
8.5.2 Gel?]Based Approaches 139
8.5.3 Gel?]Free Approaches 140
8.6 Instrumentation 141
8.6.1 LTQ Orbitrap 141
8.6.2 LTQ Orbitrap Velos 142
8.6.3 Q Exactive 142
8.7 Quantitative Proteomics 143
8.8 Functional Annotation of Proteomics Data 144
8.9 Application of MS?]Based Tissue Proteomics in Bladder Cancer Research
145
8.10 Conclusions 148
References 148
9 Tissue MALDI Imaging 156
Andrew Smith, Niccolò Mosele, Vincenzo L'Imperio, Fabio Pagni, and Fulvio
Magni
9.1 Introduction 156
9.1.1 MALDI?]MSI: General Principles 157
9.2 Experimental Procedures 159
9.2.1 Sample Handling: Storage, Embedding, and Sectioning 159
9.2.2 Matrix Application 160
9.2.3 Spectral Processing 162
9.2.3.1 Baseline Removal 162
9.2.3.2 Smoothing 164
9.2.3.3 Spectral Normalization 164
9.2.3.4 Spectral Realignment 166
9.2.3.5 Generating an Overview Spectrum 166
9.2.3.6 Peak Picking 166
9.2.4 Data Elaboration 168
9.2.4.1 Unsupervised Data Mining 168
9.2.4.2 Supervised Data Mining 168
9.2.5 Correlating MALDI?]MS Images with Pathology 169
9.3 Applications in Clinical Research 169
References 171
10 Metabolomics of Body Fluids 173
Ryan B. Gill and Silke Heinzmann
10.1 Introduction to Metabolomics 173
10.2 Analytical Techniques 174
10.2.1 NMR 174
10.2.1.1 Sample Preparation for Urine 175
10.2.1.2 Sample Preparation for Blood 177
10.2.1.3 Sample Preparation for Tissue 177
10.2.1.4 Instrumental Setup 177
10.2.2 MS 178
10.2.2.1 Ionization 178
10.2.2.2 Mass Analyzers 179
10.2.2.3 Coupled Separation Methods 179
10.2.2.4 MS Sample Pretreatment Techniques 180
10.2.3 Protein Removal (PPT) 181
10.2.4 LLE 182
10.2.5 Solid?]Phase Extraction (SPE) 182
10.3 Statistical Tools and Systems Integration 182
10.3.1 Post?]Measurement Spectral Processing 183
10.3.2 Spectral Alignment 183
10.3.3 Normalization and Scaling 184
10.3.4 Peak Versus Feature Detection 184
10.3.5 Data Analysis 184
10.3.6 Unsupervised 184
10.3.7 Supervised 185
10.3.8 Spectral Databases and Metabolite Identification 185
10.3.9 Pathway Analysis 186
10.3.10 Validation and Performance Assessment 186
10.3.11 Application into Systems Biology 187
10.4 Metabolomics in CKD 187
10.4.1 Uremic Toxins and New Biomarkers of eGFR and CKD Stage 187
10.4.2 Dimethylarginine 188
10.4.3 p?]Cresol Sulfate (PCS) 188
10.4.4 Indoxyl Sulfate (IS) 188
10.4.5 Gut Microbiota 189
10.4.6 Osmolytes 190
10.5 Conclusions 190
References 191
11 Statistical Inference in High?]Dimensional Omics Data 196
Eleni?]Ioanna Delatola and Mohammed Dakna
11.1 Introduction 196
11.2 From Raw Data to Expression Matrices 196
11.3 Brief Introduction R and Bioconductor 197
11.4 Feature Selection 197
11.5 Sample Classification 199
11.6 Real Data Example 200
11.7 Multi?]Platform Data Integration 200
11.7.1 Early?]Stage Integration 201
11.7.2 Late?]Stage Integration 201
11.7.3 Intermediate?]Stage Integration 202
11.7.4 Intermediate?]Stage Integration: Matrix Factorization 202
11.7.5 Intermediate?]Stage Integration: Unsupervised Methods 202
11.8 Discussion and Further Challenges 202
References 203
12 Epidemiological Applications in ?]Omics Approaches 207
Elena Critselis and Hiddo Lambers Heerspink
12.1 Overview: Importance of Study Design and Methodology 207
12.2 Principles of Hypothesis Testing 207
12.2.1 Definition of Research Hypotheses and Clinical Questions 207
12.2.2 Hypothesis Testing in Relation to Types of Biomarkers Under
Assessment 208
12.3 Selection of Appropriate Epidemiological Study Design for Hypothesis
Testing 208
12.4 Types of Epidemiological Study Designs 209
12.4.1 Observational Studies 209
12.4.1.1 Cross?]Sectional Studies 209
12.4.1.2 Case?]Control Studies 210
12.4.1.3 Cohort Studies 211
12.4.1.4 Health Economics Assessment 211
12.5 Selection of Appropriate Statistical Analyses for Hypothesis Testing
211
12.6 Summary 212
References 213
Part II Progressing Towards Systems Medicine 215
13 Introduction into the Concept of Systems Medicine 217
Stella Logotheti and Walter Kolch
13.1 Medicine of the Twenty?]First Century: From Empirical Medicine and
Personalized Medicine to Systems Medicine 217
13.2 The Emerging Concept of Systems Medicine 218
13.2.1 The Need for Establishment of Systems Medicine and the Field of
Application 218
13.2.2 Bridging the Gap: From Systems Biology to Systems Medicine 219
13.2.3 Attempting a Definition 220
13.2.4 The Network?]Within?]a?]Network Approach in Systems Medicine 220
13.2.4.1 Great Expectations for Systems Medicine: The P4 Vision 221
13.2.4.2 How Systems Medicine Will Transform Healthcare 222
13.2.4.3 The Five Pillars of Systems Medicine 223
13.2.4.4 The Stakeholders of Systems Medicine 223
13.2.4.5 The Key Areas for Successful Implementation 223
13.2.4.6 Improvement of the Design of Clinical Trials 223
13.2.4.7 Development of Methodology and Technology, with Emphasis on
Modeling 224
13.2.4.8 Generation of Data 224
13.2.4.9 Investment on Technological Infrastructure 224
13.2.4.10 Improvement of Patient Stratification 224
13.2.4.11 Cooperation with the Industry 224
13.2.4.12 Defining Ethical and Regulatory Frameworks 224
13.2.4.13 Multidisciplinary Training 225
13.3 Networking Among All Key Stakeholders 225
13.4 Coordinated European Efforts for Dissemination and Implementation 225
13.5 The Contributions of Academia in Systems Medicine 226
13.6 Data Generation: Omics Technologies 226
13.7 Data Integration: Identifying Disease Modules and Multilayer Disease
Modules 227
13.8 Modeling: Computational and Animal Disease Models for Understanding
the Systemic Context of a Disease 228
13.9 Examples and Success Stories of Systems Medicine?]Based Approaches 228
13.10 Limitations, Considerations, and Future Challenges 229
References 230
14 Knowledge Discovery and Data Mining 233
Magdalena Krochmal and Holger Husi
14.1 Introduction 233
14.2 Knowledge Discovery Process 233
14.2.1 Defining the Concept and Goals 234
14.2.2 Data Preparation/Preprocessing 235
14.2.3 Database Systems 236
14.2.4 Data Mining Tasks and Methods 236
14.2.4.1 Statistics 238
14.2.4.2 Machine Learning 239
14.2.4.3 Text Mining 241
14.2.5 Pattern Evaluation 242
14.3 Data Mining in Scientific Applications 242
14.3.1 Genomics Data Mining 243
14.3.2 Proteomics Data Mining 243
14.4 Bioinformatics Data Mining Tools 244
14.5 Conclusions 244
References 245
15 -Omics and Clinical Data Integration 248
Gaia De Sanctis, Riccardo Colombo, Chiara Damiani, Elena Sacco, and Marco
Vanoni
15.1 Introduction 248
15.2 Data Sources 249
15.3 Integration of Different Data Sources 252
15.4 Integration of Different ?]Omics Data 252
15.4.1 Integrating Transcriptomics and Proteomics 252
15.4.2 Integrating Transcriptomics and Interactomics 253
15.4.3 Integrating Transcriptomics and Metabolic Pathways 254
15.5 Visualization of Integrated ?]Omics Data 255
15.6 Integration of ?]Omics Data into Models 260
15.6.1 Multi?]Omics Data Integration into Genome?]Scale Constraint?]Based
Models 262
15.7 Data Integration and Human Health 263
15.7.1 Applications to Metabolic Diseases 263
15.7.2 Applications to Cancer Research 264
15.8 Conclusions 265
References 265
16 Generation of Molecular Models and Pathways 274
Amel Bekkar, Julien Dorier, Isaac Crespo, Anne Niknejad, Alan Bridge, and
Ioannis Xenarios
16.1 Introduction 274
16.2 PKN Construction Through Expert Biocuration 274
16.3 Modeling and Simulating the Dynamical Behavior of Networks 276
16.3.1 Logic Models 276
16.3.1.1 Boolean Networks 276
16.3.1.2 Probabilistic Boolean Networks (PBN) 278
16.3.1.3 Multiple Value Modeling 278
16.3.1.4 Fuzzy Logic?]Based Modeling 278
16.3.1.5 Contextualization of PKNs Using Experimental Data 279
16.3.1.6 Ordinary Differential Equations 280
16.3.1.7 Piecewise Linear Differential Equations 280
16.3.1.8 Constraint?]Based Modeling 281
16.3.1.9 Hybrid Models 282
16.4 Conclusions 283
References 283
17 Database Creation and Utility 286
Magdalena Krochmal, Katryna Cisek, and Holger Husi
17.1 Introduction 286
17.2 Database Systems 286
17.2.1 Introduction to Databases 286
17.2.2 Data Life Cycle and Objectives of Database Systems 286
17.2.3 Advantages and Limitations 288
17.2.4 Database Design Models 288
17.2.5 Development Life Cycle 291
17.2.6 Database Transactions, Structured Query Language (SQL) 292
17.2.7 Data Analysis and Visualization 292
17.3 Biological Databases 293
17.3.1 Development Life Cycle 294
17.3.1.1 Data Extraction 294
17.3.1.2 Semantic Tools for ?]Omics 294
17.3.2 Existing Biological Repositories 295
17.3.2.1 Information Sources for ?]Omics 295
17.3.2.2 Renal Information Sources for ?]Omics 296
17.3.3 Application in Research 297
17.3.3.1 Data Mining on Large Multi?]Omics Datasets 297
17.3.3.2 Multi?]Omics Tools for Researchers 297
17.3.3.3 Limitations of Multi?]Omics Tools 297
17.3.3.4 Future Outlook for Multi?]Omics 298
17.4 Conclusions 298
References 298
Part III Test Cases CKD and Bladder Carcinoma 301
18 Kidney Function, CKD Causes, and Histological Classification 303
Franco Ferrario, Fabio Pagni, Maddalena Bolognesi, Elena Ajello, Vincenzo
L'Imperio, Cristina Masella, and Giovambattista Capasso
18.1 Introduction 303
18.2 The Evaluation of Glomerular Filtration Rate 303
18.3 Causes of CKD 305
18.3.1 Histological Classification of CKD 307
18.4 Assessment of Disease Progression and Response to Therapy for the
Individual: Interval Renal Biopsy 310
18.5 Recent Advances: Pathology at the Molecular Level 310
18.6 Digital Pathology 313
18.7 Conclusions 315
References 315
19 CKD: Diagnostic and Other Clinical Needs 319
Alberto Ortiz
19.1 The Evolving Concept of Chronic Kidney Disease 319
19.2 A Growing Epidemic 320
19.3 Increasing Mortality from Chronic Kidney Disease 321
19.4 The Issue of Cause and Etiologic Therapy 322
19.5 Unmet Medical Needs: Biomarkers and Therapy 323
19.6 Conclusions 324
Acknowledgments 324
References 324
20 Molecular Model for CKD 327
Marco Fernandes, Katryna Cisek, and Holger Husi
20.1 Introduction 327
20.2 Data?]Driven Approaches and Multiomics Data Integration 327
20.2.1 Database Resources 328
20.2.2 Software Tools and Solutions 330
20.2.2.1 Gene Ontology (GO) and Pathway?]Term Enrichment 331
20.2.2.2 Disease-Gene Associations 331
20.2.2.3 Resolving Molecular Interactions (Protein-Protein Interaction,
Metabolite-Reaction-Protein-Gene) 332
20.2.2.4 Transcription Factor(TF)?]Driven Modules and microRNA-Target
Regulation 332
20.2.2.5 Pathway Visualization and Mapping 333
20.2.2.6 Data Harmonization: Merging and Mapping 333
20.2.3 Computational Drug Discovery 334
20.2.3.1 High?]Throughput Virtual Screening (HTVS) 334
20.2.3.2 Advantages and Limitations of HTVS 334
20.3 Chronic Kidney Disease (CKD) Case Study 335
20.3.1 Dataspace Description: Demographics and Omics Platforms Information
337
20.3.2 Dataspace Description: No. of Associated Molecules Per Omics
Platform 337
20.3.3 Data Reduction by Principal Component Analysis (PCA) 338
20.3.4 Gene Ontology (GO) and Pathway?]Term Clustering 339
20.3.5 Interactome Analysis: PPIs and Regulatory Interactions 342
20.3.5.1 Protein-Protein Interactions (PPIs) 342
20.3.5.2 Regulatory Interactions 343
20.3.6 Interactome Analysis: Metabolic Reactions 343
20.4 Final Remarks 343
Acknowledgments 343
Conflict of Interest Statement 343
References 345
21 Application of Omics and Systems Medicine in Bladder Cancer 347
Maria Frantzi, Agnieszka Latosinska, Murat Akand, and Axel S. Merseburger
21.1 Introduction 347
21.2 Bladder Cancer Pathology and Clinical Needs 348
21.2.1 Epidemiological Facts and Histological Classification 348
21.2.2 Current Diagnostic Means 348
21.2.3 Treatment Options 349
21.2.4 Recurrence and Progression 349
21.2.5 Molecular Classification 350
21.2.6 Biomarkers for Bladder Cancer 350
21.2.7 Considerations on Patient Management 351
21.2.8 Defining the Disease?]Associated Clinical Needs 351
21.3 Systems Medicine in Bladder Cancer 351
21.3.1 Omics Datasets for Biomarker Research 353
21.3.1.1 Diagnostic Biomarkers for Disease Detection/Monitoring 353
21.3.1.2 Prognostic Signatures 354
21.3.1.3 Predictive Molecular Profiles 355
21.3.1.4 Molecular Sub?]Classification 356
21.4 Outlook 357
Acknowledgments 357
References 358
Index 361