Open Access

Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics

  • Authors:
    • Danni Yuan
    • Haohan Zhou
    • Hongyu Sun
    • Rui Tian
    • Meihui Xia
    • Liankun Sun
    • Yanan Liu
  • View Affiliations

  • Published online on: May 22, 2020     https://doi.org/10.3892/ol.2020.11672
  • Pages: 1345-1359
  • Copyright: © Yuan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The emergence of resistance to chemotherapy drugs in patients with ovarian cancer is still the main cause of low survival rates. The present study aimed to identify key genes that may provide treatment guidance to reduce the incidence of drug resistance in patients with ovarian cancer. Original data of chemotherapy sensitivity and chemoresistance of ovarian cancer were obtained from the Gene Expression Omnibus dataset GSE73935. Differentially expressed genes (DEGs) between sensitive and resistant ovarian cancer cell lines were screened by Empirical Bayes methods. Overlapping DEGs between four chemoresistant groups were identified by Venn map analysis. Protein‑protein interaction networks were also constructed, and hub genes were identified. The hub genes were verified by in vitro experiments as well as The Cancer Genome Atlas data. Results from the present study identified eight important genes that may guide treatment decisions regarding chemotherapy regimens for ovarian cancer, including epidermal growth factor‑like repeats and discoidin I‑like domains 3, NRAS proto‑oncogene, hyaluronan and proteoglycan link protein 1, activated protein C receptor, CD53, cyclin‑dependent kinase inhibitor 2A, insulin‑like growth factor 1 receptor and roundabout guidance receptor 2 genes. Their expressions were found to have an impact on the prognosis of different treatment groups (cisplatin, paclitaxel, cisplatin + paclitaxel, cisplatin + doxorubicin and cisplatin + topotecan). The results indicated that these genes may minimise the occurrence of ovarian cancer drug resistance and may provide effective treatment options for patients with ovarian cancer.

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August-2020
Volume 20 Issue 2

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
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Spandidos Publications style
Yuan D, Zhou H, Sun H, Tian R, Xia M, Sun L and Liu Y: Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics. Oncol Lett 20: 1345-1359, 2020
APA
Yuan, D., Zhou, H., Sun, H., Tian, R., Xia, M., Sun, L., & Liu, Y. (2020). Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics. Oncology Letters, 20, 1345-1359. https://doi.org/10.3892/ol.2020.11672
MLA
Yuan, D., Zhou, H., Sun, H., Tian, R., Xia, M., Sun, L., Liu, Y."Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics". Oncology Letters 20.2 (2020): 1345-1359.
Chicago
Yuan, D., Zhou, H., Sun, H., Tian, R., Xia, M., Sun, L., Liu, Y."Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics". Oncology Letters 20, no. 2 (2020): 1345-1359. https://doi.org/10.3892/ol.2020.11672