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

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 proteineprotein 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, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival. Measurement Using an information-theoretic metric, the ...

• Type: Journal
• Year: 2011
• Where: JAMIA
• Authors: James L. Chen, Jianrong Li, Walter M. Stadler, Yves A. Lussier
• Keywords: Algorithms, Cancer Genes, JAMIA 2011, Prostate Cancer Gene, Protein Analysis