Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer

Manfei Si; Junji Zhang; Jianzhong Cao; Zhibo Xie; Shan Shu; Yapei Zhu; Jinghe Lang

doi:10.2147/OTT.S228678

Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer

Onco Targets Ther. 2019 Nov 21:12:10057-10075. doi: 10.2147/OTT.S228678. eCollection 2019.

Authors

Manfei Si¹, Junji Zhang¹, Jianzhong Cao², Zhibo Xie³, Shan Shu¹, Yapei Zhu¹, Jinghe Lang¹

Affiliations

¹ Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
² Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
³ Department of Vascular Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

Abstract

Purpose: Ovarian cancer is the leading cause of gynecologic cancer-related death worldwide. Early diagnosis of ovarian cancer can significantly improve patient prognosis. Hence, there is an urgent need to identify key diagnostic and prognostic biomarkers specific for ovarian cancer. Because high-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and accounts for the majority of deaths, we identified potential biomarkers for the early diagnosis and prognosis of HGSOC.

Methods: Six datasets (GSE14001, GSE18520, GSE26712, GSE27651, GSE40595, and GSE54388) were downloaded from the Gene Expression Omnibus database for analysis. Differentially expressed genes (DEGs) between HGSOC and normal ovarian surface epithelium samples were screened via integrated analysis. Hub genes were identified by analyzing protein-protein interaction (PPI) network data. The online Kaplan-Meier plotter was utilized to evaluate the prognostic roles of these hub genes. The expression of these hub genes was confirmed with Oncomine datasets and validated by quantitative real-time PCR and Western blotting.

Results: A total of 103 DEGs in patients with HGSOC-28 upregulated genes and 75 downregulated genes-were successfully screened. Enrichment analyses revealed that the upregulated genes were enriched in cell division and cell proliferation and that the downregulated genes mainly participated in the Wnt signaling pathway and various metabolic processes. Ten hub genes were associated with HGSOC pathogenesis. Seven overexpressed hub genes were partitioned into module 1 of the PPI network, which was enriched in the cell cycle and DNA replication pathways. Survival analysis revealed that MELK, CEP55 and KDR expression levels were significantly correlated with the overall survival of HGSOC patients (P < 0.05). The RNA and protein expression levels of these hub genes were validated experimentally.

Conclusion: Based on an integrated analysis, we propose the further investigation of MELK, CEP55 and KDR as promising diagnostic and prognostic biomarkers of HGSOC.

Keywords: bioinformatic analysis; biomarker; differentially expressed genes; high-grade serous ovarian cancer; integrated analysis; survival.