Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer

PLoS One. 2018 Nov 1;13(11):e0206665. doi: 10.1371/journal.pone.0206665. eCollection 2018.

Abstract

We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma / genetics*
  • Carcinoma / metabolism*
  • Cell Proliferation
  • Computational Biology
  • Endometrial Neoplasms / genetics*
  • Endometrial Neoplasms / metabolism*
  • Epithelial-Mesenchymal Transition / physiology
  • Female
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks
  • Humans
  • Hypoxia / genetics
  • Hypoxia / metabolism
  • Models, Statistical
  • Neoplasm Metastasis / genetics*
  • Neoplasm Metastasis / physiopathology*
  • RNA / metabolism
  • Software

Substances

  • RNA

Grants and funding

This study was conducted with the support from the Research Council of Norway, the Norwegian Cancer Society, Helse-Vest, the University of Bergen and the Bergen Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.