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

• Main Authors: Si M, Zhang J, Cao J, Xie Z, Shu S, Zhu Y, Lang J
• Format: Article
• Language: English
• Published: Dove Medical Press 2019-11-01
• Series: OncoTargets and Therapy
• Subjects: high-grade serous ovarian cancer; integrated analysis; bioinformatic analysis; differentially expressed genes; survival; biomarker
• Online Access: https://www.dovepress.com/integrated-analysis-to-identify-molecular-biomarkers-of-high-grade-ser-peer-reviewed-article-OTT

Manfei Si,1 Junji Zhang,1 Jianzhong Cao,2 Zhibo Xie,3 Shan Shu,1 Yapei Zhu,1 Jinghe Lang1 1Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Department of Vascular Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of ChinaCorrespondence: Jinghe LangDepartment of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, People’s Republic of ChinaTel [phone]Email [e-mail]: 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: high-grade serous ovarian cancer, integrated analysis, bioinformatic analysis, differentially expressed genes, survival, biomarker