An integrated framework for cancer module identification


Copy Number Aberration (CNA) is a hallmark of cancer and Gene Expression (GE) changes are likely to be driven by CNA. Several studies i.e. TCGA research on ovarian cancer showed that genetic alterations and gene expression changes occurs simultaneously in RB signalling pathways. Therefore, the integration of CNA and GE data sets is of great importance specially at module-level rather than gene-level. Such integrated data-driven information can be further filtered with protein-protein interaction (PPI) information since, it is a well known fact that, PPI hub genes are more likely to be related with cancer progression and malignance. Thus, a framework which integrates pair-wise information of CNA, GE, and PPI for cancer module identification can be of great importance in order to explain complex relationships among genes in cancer development.