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Sci Rep. 2015 Aug 10;5:12981. doi: 10.1038/srep12981.

Systems biology approach to studying proliferation-dependent prognostic subnetworks in breast cancer.

Author information

1
1] Division of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA. [2] School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P R China.
2
Division of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
3
School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P R China.
4
Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
5
1] Division of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA. [2] Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.

Abstract

Tumor proliferative capacity is a major biological correlate of breast tumor metastatic potential. In this paper, we developed a systems approach to investigate associations among gene expression patterns, representative protein-protein interactions, and the potential for clinical metastases, to uncover novel survival-related subnetwork signatures as a function of tumor proliferative potential. Based on the statistical associations between gene expression patterns and patient outcomes, we identified three groups of survival prognostic subnetwork signatures (SPNs) corresponding to three proliferation levels. We discovered 8 SPNs in the high proliferation group, 8 SPNs in the intermediate proliferation group, and 6 SPNs in the low proliferation group. We observed little overlap of SPNs between the three proliferation groups. The enrichment analysis revealed that most SPNs were enriched in distinct signaling pathways and biological processes. The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors. Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner. These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

PMID:
26257336
PMCID:
PMC4530341
DOI:
10.1038/srep12981
[Indexed for MEDLINE]
Free PMC Article
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