Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level.

ABSTRACT:

Background

Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.

Results

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic

SUBMITTER: Li W 

PROVIDER: S-EPMC3443057 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature