  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.
