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.
- Focused description of NMR spectroscopy needed by cancer biologists who are starting to use metabolomics
- Current overview of knowledge related to the cancer metabolic phenotype from the perspective of metabolomics applications
- Information about the best practices in NMR metabolomics experimentation and data preprocessing as applied to different sample types
About the Author
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.
Table of ContentsBiology-Cancer metabolic phenotype; Experimental methodology; Metabolomics NMR data pre-processing – analysis of individual spectrum; Metabolomics data analysis – processing and analysis of a dataset; Dynamic metabolic profiling and metabolite network and pathways modelling; Biomarker discovery; NMR metabolomics application by cancer type; Perspectives