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Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer.
- Source :
-
PloS one [PLoS One] 2018 Nov 01; Vol. 13 (11), pp. e0206665. Date of Electronic Publication: 2018 Nov 01 (Print Publication: 2018). - Publication Year :
- 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.<br />Competing Interests: The authors have declared that no competing interests exist.
- Subjects :
- Cell Proliferation
Computational Biology
Epithelial-Mesenchymal Transition physiology
Female
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Hypoxia genetics
Hypoxia metabolism
Models, Statistical
RNA metabolism
Software
Carcinoma genetics
Carcinoma metabolism
Endometrial Neoplasms genetics
Endometrial Neoplasms metabolism
Neoplasm Metastasis genetics
Neoplasm Metastasis physiopathology
Subjects
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 13
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 30383835
- Full Text :
- https://doi.org/10.1371/journal.pone.0206665