Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data
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Fan Zhang1,*, Chunyan Ren2,*, Hengqiang Zhao1, Lei Yang1, Fei Su1, Ming-Ming Zhou2, Junwei Han1, Eric A. Sobie2, Martin J. Walsh2
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China
2Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
*These authors have contributed equally to this work
Martin J. Walsh, email: [email protected]
Eric A. Sobie, email: [email protected]
Junwei Han, email: [email protected]
Keywords: triple-negative breast cancer, landmark for cancer prognosis, GTPase, ubiquitination, oxidative damage
Received: July 27, 2016 Accepted: September 22, 2016 Published: September 27, 2016
Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics.
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