Detection of Epigenomic Network Community Oncomarkers

TL;DR

This paper introduces a network-based approach to identify prognostic cancer biomarkers from DNA methylation data, emphasizing the role of epigenetic interactions in cancer prognosis.

Contribution

The study presents a novel methodology for inferring genomic networks from methylation data and identifying prognostic biomarkers called network community oncomarkers.

Findings

Successfully applied to breast cancer dataset

Identified prognostic network communities

Demonstrated potential for large-scale genomic studies

Abstract

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset.