Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence.

ABSTRACT:

Objective

Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared.

Design

The authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, t

SUBMITTER: Chen JL 

PROVIDER: S-EPMC3128407 | biostudies-literature | 2011 Jul-Aug

REPOSITORIES: biostudies-literature