Identification of cancer-related module in protein–protein interaction network based on gene prioritization

Abstract

With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

“…The disease modules, which consist of a known group of genes found in cancers like breast, sarcoma, colorectal, leukemia, and head and neck cancer, were linked to biological processes that are unique to cancer [ 12 ]. Wu et al [ 13 ] showed that the active disease modules in breast and cervical cancer are associated with many cancer-related pathways. These studies indicate that the identification of cancer-specific disease modules can help identify novel biomarkers for therapeutic targets.…”