The Prognostic 97 Chemoresponse Gene Signature in Ovarian Cancer

Sci Rep. 2017 Aug 29;7(1):9689. doi: 10.1038/s41598-017-08766-5.

Abstract

Patient diagnosis and care would be significantly improved by understanding the mechanisms underlying platinum and taxane resistance in ovarian cancer. Here, we aim to establish a gene signature that can identify molecular pathways/transcription factors involved in ovarian cancer progression, poor clinical outcome, and chemotherapy resistance. To validate the robustness of the gene signature, a meta-analysis approach was applied to 1,020 patients from 7 datasets. A 97-gene signature was identified as an independent predictor of patient survival in association with other clinicopathological factors in univariate [hazard ratio (HR): 3.0, 95% Confidence Interval (CI) 1.66-5.44, p = 2.7E-4] and multivariate [HR: 2.88, 95% CI 1.57-5.2, p = 0.001] analyses. Subset analyses demonstrated that the signature could predict patients who would attain complete or partial remission or no-response to first-line chemotherapy. Pathway analyses revealed that the signature was regulated by HIF1α and TP53 and included nine HIF1α-regulated genes, which were highly expressed in non-responders and partial remission patients than in complete remission patients. We present the 97-gene signature as an accurate prognostic predictor of overall survival and chemoresponse. Our signature also provides information on potential candidate target genes for future treatment efforts in ovarian cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor
  • Computational Biology / methods
  • Databases, Genetic
  • Drug Resistance, Neoplasm / genetics*
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Gene Ontology
  • Gene Regulatory Networks
  • Humans
  • Kaplan-Meier Estimate
  • Neoplasm Grading
  • Neoplasm Staging
  • Ovarian Neoplasms / genetics*
  • Ovarian Neoplasms / mortality*
  • Ovarian Neoplasms / pathology
  • Prognosis
  • Proportional Hazards Models
  • Reproducibility of Results
  • Transcriptome*

Substances

  • Biomarkers, Tumor