Gene expression: protein interaction systems network modeling identifies transformation-associated molecules and pathways in ovarian cancer.

Abstract

Multiple, dissimilar genetic defects in cancers of the same origin contribute to heterogeneity in tumor phenotypes and therapeutic responses of patients, yet the associated molecular mechanisms remain elusive. Here, we show at the systems level that serous ovarian carcinoma is marked by the activation of interconnected modules associated with a specific gene set that was derived from three independent tumor-specific gene expression data sets. Network prediction algorithms combined with preestablished protein interaction networks and known functionalities affirmed the importance of genes associated with ovarian cancer as predictive biomarkers, besides "discovering" novel ones purely on the basis of interconnectivity, whose precise involvement remains to be investigated. Copy number alterations and aberrant epigenetic regulation were identified and validated as significant influences on gene expression. More importantly, three functional modules centering on c-Myc activation, altered retinoblastoma signaling, and p53/cell cycle/DNA damage repair pathways have been identified for their involvement in transformation-associated events. Further studies will assign significance to and aid the design of a panel of specific markers predictive of individual- and tumor-specific pathways. In the parlance of this emerging field, such networks of gene-hub interactions may define personalized therapeutic decisions.