Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data

Fan Zhang, Chunyan Ren, Hengqiang Zhao, Lei Yang, Fei Su, Ming Ming Zhou, Junwei Han, Eric A. Sobie, Martin J. Walsh

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)71620-71634
Number of pages15
JournalOncotarget
Volume7
Issue number44
DOIs
StatePublished - 2016

Keywords

  • GTPase
  • Landmark for cancer prognosis
  • Oxidative damage
  • Triple-negative breast cancer
  • Ubiquitination

Fingerprint

Dive into the research topics of 'Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data'. Together they form a unique fingerprint.

Cite this