Multi-network approach to identify differentially methylated gene communities in cancer.

BACKGROUND AND OBJECTIVE: High-throughput Next Generation Sequencing tools have generated immense quantity of genome-wide methylation and expression profiling data, resulting in an unprecedented opportunity to unravel the epigenetic regulatory mechanisms underlying cancer. Identifying differentially methylated regions within gene networks is an important step towards revealing the cancer epigenome blueprint. Approaches that integrate gene methylation and expression profiles assume their negative correlation and build a single scaffold network to cluster. However, the exact regulatory mechanism between gene expression and methylation is not precisely deciphered.

METHODS: A consensus-based clustering framework, namely, Differentially Methylated Gene Communities based on Multi-network (DMGC-M) is proposed, that takes multiple gene networks as input and builds a community structure out of evidences from all network types.

RESULTS: Experiments on six cancer datasets from The Cancer Genome Atlas (TCGA) reveal that multi-network approaches produce more discriminative gene communities than integrated approaches.

CONCLUSION: The proposed method will be useful to a number of researchers who seek to identify epigenetic dysregulations in pathways or molecular networks. The findings can also advance recent research efforts in Molecular Pathologic Epidemiology.