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Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Authors Zhao Y, Pi J, Liu L, Yan W, Ma S, Hong L

Received 29 September 2020

Accepted for publication 4 December 2020

Published 26 January 2021 Volume 2021:13 Pages 707—721

DOI https://doi.org/10.2147/CMAR.S282529

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Antonella D'Anneo


Yuzi Zhao,1 Jie Pi,1 Lihua Liu,2 Wenjie Yan,1 Shufang Ma,3 Li Hong1

1Department of Gynaecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Department of Gynaecology and Obstetrics, Huanggang Huangzhou Maternity and Child Health Care Hospital, Huanggang, People’s Republic of China; 3Reproductive Medicine Center, Wuhan Kangjian Women and Infants Hospital, Wuhan, People’s Republic of China

Correspondence: Li Hong
Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan, Hubei 430060, People’s Republic of China
Tel +86-13476814853
Email orange826@163.com

Background: This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.
Methods: Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.
Results: A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.
Conclusion: Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.

Keywords: ovarian cancer, bioinformatics, cell cycle, hub genes, survival, prognosis

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