Genome-Wide DNA Methylation Analysis in Cancer Research

  • Srinivasan Yegnasubramanian
  • William G. Nelson
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


DNA methylation is a central epigenetic process involved in ­establishing normal cellular gene expression patterns and genome homeostasis. Aberrations in DNA methylation, leading to abnormal gene expression patterns, have now been linked to many human diseases, and are a nearly universal feature of human cancers. Because these DNA methylation changes can be stably transmitted during clonal outgrowth of cancer cells, they can carry the same importance as mutations in the initiation and progression of human cancers. Such somatic DNA methylation changes often occur earlier and more frequently than genome mutations during ­carcinogenesis, and have therefore provided a wealth of targets for translational opportunities in cancer biomarkers for diagnosis and risk stratification. Additionally, since these DNA methylation changes are epigenetic processes that are enzymatically mediated and do not alter the underlying DNA sequence, they can potentially be reversed by pharmacological inhibition of the epigenetic machinery, providing opportunities for cancer therapy. Therefore, understanding the genome-wide patterns of DNA methylation in normal and cancer cells has become of primary interest in cancer research. In this chapter, we will first provide an overview of DNA methylation as an epigenetic process in normal physiology and in carcinogenesis. Then we will describe some of the current and up-coming technologies used in analyzing DNA methylation patterns at a genome-wide level, and consider the strengths and limitations of each of these approaches.


Next Generation Sequencing Bisulfite Sequencing Bisulfite Conversion Methylation Sensitive Restriction Enzyme Bisulfite Genomic Sequencing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Srinivasan Yegnasubramanian
    • 1
  • William G. Nelson
  1. 1.Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreUSA

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