Identification of Chemoresistance-Associated Key Genes and Pathways in High-Grade Serous Ovarian Cancer by Bioinformatics Analyses

Yong Wu; Lingfang Xia; Qinhao Guo; Jun Zhu; Yu Deng; Xiaohua Wu

doi:10.2147/CMAR.S251622

Identification of Chemoresistance-Associated Key Genes and Pathways in High-Grade Serous Ovarian Cancer by Bioinformatics Analyses

Cancer Manag Res. 2020 Jun 30:12:5213-5223. doi: 10.2147/CMAR.S251622. eCollection 2020.

Authors

Yong Wu^#^{1

2}, Lingfang Xia^#^{1

2}, Qinhao Guo^#^{1

2}, Jun Zhu^{1

2}, Yu Deng^{2

3}, Xiaohua Wu^{1

2}

Affiliations

¹ Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
³ Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: High-grade serous ovarian cancer (HGSOC) is the leading cause of death among gynecological malignancies. This is mainly attributed to its high rates of chemoresistance. To date, few studies have investigated the molecular mechanisms underlying this resistance to treatment in ovarian cancer patients. In this study, we aimed to explore these molecular mechanisms using bioinformatics analysis.

Methods: We analyzed microarray data set GSE51373, which included 16 platinum-sensitive HGSOC samples and 12 platinum-resistant control samples. Differentially expressed genes (DEGs) were identified using RStudio. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DAVID, and a DEG-associated protein-protein interaction (PPI) network was constructed using STRING. Hub genes in the PPI network were identified, and the prognostic value of the top ten hub genes was evaluated. MGP, one of the hub genes, was verified by immunohistochemistry.

Results: All samples were confirmed to be of high quality. A total of 109 DEGs were identified, and the top ten enriched GO terms and four KEGG pathways were obtained. Specifically, the PI3K-AKT signaling pathway and the Rap1 signaling pathway were identified as having significant roles in chemoresistance in HGSOC. Furthermore, based on the PPI network, KIT, FOXM1, FGF2, HIST1H4D, ZFPM2, IFIT2, CCNO, MGP, RHOBTB3, and CDC7 were identified as hub genes. Five of these hub genes could predict the prognosis of HGSOC patients. Positive immunostaining signals for MGP were observed in the chemoresistant samples.

Conclusion: Taken together, the findings of this study may provide novel insights into HGSOC chemoresistance and identify important therapeutic targets.

Keywords: bioinformatics analysis; chemoresistance; gene expression profiling; high-grade serous ovarian cancer.

Grants and funding

This work was supported in part by grants from National Natural Science Foundation of China (No. 81672569) and the Science and Technology Commission of Shanghai Municipality (17411963000; JGYXLJ1502).