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

• Published in: PloS one 2018-11, Vol.13 (11), p.e0206665-e0206665
• Main Authors: Kusonmano, Kanthida, Halle, Mari K, Wik, Elisabeth, Hoivik, Erling A, Krakstad, Camilla, Mauland, Karen K, Tangen, Ingvild L, Berg, Anna, Werner, Henrica M J, Trovik, Jone, Øyan, Anne M, Kalland, Karl-Henning, Jonassen, Inge, Salvesen, Helga B, Petersen, Kjell
• Format: Article
• Language: English
• Subjects: Biology; Biology and Life Sciences; Biomarkers; Brain cancer; Cancer; Carcinoma; Carcinoma - genetics; Carcinoma - metabolism; Cell cycle; Cell Proliferation; Computational Biology; Computer and Information Sciences; Computer networks; Data collection; Endometrial cancer; Endometrial Neoplasms - genetics; Endometrial Neoplasms - metabolism; Endometrium; Epithelial-Mesenchymal Transition - physiology; Female; Gene expression; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes; Genomes; Gynecology; Hospitals; Humans; Hyperplasia; Hypoxia; Hypoxia - genetics; Hypoxia - metabolism; Informatics; Lesions; Medicine and Health Sciences; Mesenchyme; Metastases; Metastasis; Models, Statistical; Neoplasm Metastasis - genetics; Neoplasm Metastasis - physiopathology; Obstetrics; Permutations; Physical Sciences; Protein interaction; Proteins; RNA - metabolism; Software; Source code; Statistical analysis; Statistical significance; Target recognition; Therapeutic applications; Tumors; Uterine cancer
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0206665

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.