Analysis of healthy and tumour DNA methylation distributions in kidney‐renal‐clear‐cell‐carcinoma using Kullback–Leibler and Jensen–Shannon distance measures. Issue 3 (12th May 2017)

• Record Type: Journal Article
• Authors: Ramakrishnan, Nithya; Bose, Ranjan
• Is Part Of: IET systems biology. Volume 11:Issue 3(2017)
• Page Start: 99
• Page End: 104
• Publication Date: 2017-05-12
• Subjects: cancer -- tumours -- DNA -- genetics -- molecular biophysics; tumour DNA methylation distributions -- kidney‐renal‐clear‐cell‐carcinoma -- Kullback–Leibler distance measure -- Jensen–Shannon distance measure -- epigenetic phenomenon -- methyl groups -- cytosines -- hyper‐methylation -- probability density function distribution -- average healthy methylation distribution; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2016.0052
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper‐methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique 'average healthy methylation distribution' based on the methylation values of several healthy samples. They then obtain the Kullback–Leibler and Jensen–Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.