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

Journal article

Authors/Editors

KANTHIDA KUSONMANO

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Publication Details

Author list: Kusonmano K., Halle M.K., Wik E., Hoivik E.A., Krakstad C., Mauland K.K., Tangen I.L., Berg A., Werner H.M.J., Trovik J., Oyan A.M., Kalland K.-H., Jonassen I., Salvesen H.B., Petersen K.

Publisher: Public Library of Science

Publication year: 2018

Journal: PLoS ONE (1932-6203)

Volume number: 13

Issue number: 11

ISSN: 1932-6203

eISSN: 1932-6203

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055905064&doi=10.1371%2fjournal.pone.0206665&partnerID=40&md5=2011b51d8bb6a42815f609db7f49e669

Languages: English-Great Britain (EN-GB)

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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. ฉ 2018 Kusonmano et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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