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Integration of Omics Approaches and Systems Biology for Clinical Applications


Integration of Omics Approaches and Systems Biology for Clinical Applications


Wiley Series on Mass Spectrometry 1. Aufl.

von: Antonia Vlahou, Fulvio Magni, Harald Mischak, Jerome Zoidakis

151,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 24.01.2018
ISBN/EAN: 9781119183976
Sprache: englisch
Anzahl Seiten: 384

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Beschreibungen

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 uncover 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.
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 3Katerina 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 13Brigitte 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 26Constantinos 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 49Daniel 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 67Theofilos 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 93Szymon 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 113Prathibha 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 129Agnieszka 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 156Andrew 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 173Ryan 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 196Eleni?]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 207Elena 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 217Stella 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 233Magdalena 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 248Gaia 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 274Amel 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 286Magdalena 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 303Franco 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 319Alberto 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 327Marco 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 347Maria 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
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.
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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