Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data
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Sandeep K. Singhal1,*, Nawaid Usmani1,*, Stefan Michiels2,3, Otto Metzger-Filho4, Kamal S. Saini5, Olga Kovalchuk6,7,*, Matthew Parliament1,*
1Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
2Service de Biostatistique et d’Epidémiologie, Gustave Roussy, Villejuif, France
3INSERM U1018, CESP, Université Paris-Sud, Villejuif, France
4Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
5Quantum Health Analytics SPRL, Liège, Belgium
6Department of Biological Sciences, University of Lethbridge, Lethbridge, Canada
7Canada Cancer and Aging Research Laboratories Ltd., Lethbridge, Canada
*These authors contributed equally to the work
Matthew Parliament, e-mail: firstname.lastname@example.org
Keywords: DNA methylation, breast cancer, epigenetics, expression, microarray
Received: August 11, 2015 Accepted: November 16, 2015 Published: December 08, 2015
Until recently, an elevated disease risk has been ascribed to a genetic predisposition, however, exciting progress over the past years has discovered alternate elements of inheritance that involve epigenetic regulation. Epigenetic changes are heritably stable alterations that include DNA methylation, histone modifications and RNA-mediated silencing. Aberrant DNA methylation is a common molecular basis for a number of important human diseases, including breast cancer. Changes in DNA methylation profoundly affect global gene expression patterns. What is emerging is a more dynamic and complex association between DNA methylation and gene expression than previously believed. Although many tools have already been developed for analyzing genome-wide gene expression data, tools for analyzing genome-wide DNA methylation have not yet reached the same level of refinement.
Here we provide an in-depth analysis of DNA methylation in parallel with gene expression data characteristics and describe the particularities of low-level and high-level analyses of DNA methylation data. Low-level analysis refers to pre-processing of methylation data (i.e. normalization, transformation and filtering), whereas high-level analysis is focused on illustrating the application of the widely used class comparison, class prediction and class discovery methods to DNA methylation data. Furthermore, we investigate the influence of DNA methylation on gene expression by measuring the correlation between the degree of CpG methylation and the level of expression and to explore the pattern of methylation as a function of the promoter region.
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