NMR Metabolomics in Cancer Research

Erscheinungsjahr 2012
ISBN: 978-1-907568-84-8
Verlag: Woodhead Publishing

Buch, Englisch, 446 Seiten, Gewicht: 810 g

The application of nuclear magnetic resonance (NMR) metabolomics in cancer research requires an understanding of the many possibilities that NMR metabolomics can offer, as well as of the specific characteristics of the cancer metabolic phenotype and the open questions in cancer research. NMR metabolomics in cancer research presents a detailed account of the NMR spectroscopy methods applied to metabolomics mixture analysis along with a discussion of their advantages and disadvantages. Following an overview of the potential use of NMR metabolomics in cancer research, the book begins with an examination of the cancer metabolic phenotype and experimental methodology, before moving on to cover data pre-processing and data analysis. Chapters in the latter part of the book look at dynamic metabolic profiling, biomarker discovery, and the application of NMR metabolomics for different types of cancer, before a concluding chapter discusses future perspectives in the field.

Autoren/Hrsg.

Cuperlovic-Culf, Miroslava

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Magnetresonanztomographie, Computertomographie (MRT, CT)

Inhaltsverzeichnis

List of figures

List of tables

Abbreviations

About the author

Chapter 1: Introduction

Abstract:

1.1 Possibilities of NMR metabolomics in cancer research

Chapter 2: Biology - cancer metabolic phenotype

Abstract:

2.1 Cancer metabolic phenotype

2.2 Drugs aimed at cancer metabolic phenotype

Chapter 3: Experimental methodology

Abstract:

3.1 Fundamentals of NMR spectroscopy

3.2 1D NMR in mixture analysis

3.3 Multidimensional NMR in metabolomics

3.4 Arrayed NMR experiments

3.5 Isotope labeling for NMR analysis

3.6 Diagnostic magnetic resonance tools based on cancer metabolic phenotype and cancer-induced metabolic changes

Chapter 4: Metabolomics NMR data preprocessing - analysis of individual spectrum

Abstract:

4.1 NMR spectrum preprocessing

4.2 Qualitative metabolomics

4.3 STOCSY

4.4 Quantitative metabolomics

4.5 Novel metabolite structure analysis

Chapter 5: Metabolomics data analysis - processing and analysis of a dataset

Abstract:

5.1 Normalization and scaling

5.2 Biases and confounders in metabolomics

5.3 Data analysis

5.4 Data integration - polyomics analysis

Chapter 6: Dynamic metabolic profiling and metabolite network and pathways modeling

Abstract:

6.1 Dynamic metabolic data analysis

6.2 Computational models of metabolic networks

Chapter 7: Biomarker discovery

Abstract:

7.1 Feature (biomarkers) selection

7.2 Validation of results in supervised analysis

Chapter 8: NMR metabolomics application by cancer type

Abstract:

8.1 Breast cancer

8.2 Brain cancers

8.3 Lung cancer metabolomics analysis and results

8.4 Metabolomics analysis of biofluids in cancer research

Chapter 9: Perspectives

Index

Über Autor(innen)

Cuperlovic-Culf, Miroslava
Dr Miroslava Cuperlovic-Culf is a Research Officer at the National Research Council of Canada Institute for Information Technology, an Adjunct Professor of Chemistry at Mount Allison University and an Adjunct Researcher at the Atlantic Cancer Research Institute. She has a range of publications and patents resulting from research in NMR spectral analysis and modelling, bioinformatics data analysis and computational biology for cellular pathway modelling, as applied to cancer research.

Dr Miroslava Cuperlovic-Culf is a Research Officer at the National Research Council of Canada Institute for Information Technology, an Adjunct Professor of Chemistry at Mount Allison University and an Adjunct Researcher at the Atlantic Cancer Research Institute. She has a range of publications and patents resulting from research in NMR spectral analysis and modelling, bioinformatics data analysis and computational biology for cellular pathway modelling, as applied to cancer research.