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Published on: January 12, 2020
Identification of Biomarkers for Ovarian Cancer Diagnosis and Prognosis by Bioinformatics Analysis and q-PCR
Qinlin Zheng1, Haiqiong Ye1, Hua Zhong1
1Department of Obstetrics and Gynecology, the Affiliated Hospital of Southwest Medical University, Southwest Medical University, Luzhou, China.
Objective:
This study aimed to identify new biomarkers for ovarian cancer screening based on bioinformatics analysis and q-PCR validation.
Methods:
In total, five independent ovarian cancer patient cohorts were included to analyze the differentially expressed genes (DEGs). Thereafter, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were employed to clarify the functions of DEGs. The selected DEGs were screened by STRING database and Cytoscape software to obtain hub genes. Additionally, univariate Cox hazard analysis revealed the relevance between hub genes and patient survival. Receiver operating characteristic (ROC) curve was used to determine the accuracy of hub genes in discriminating ovarian cancer. Finally, q-PCR analysis was conducted to validate hub gene expression in ovarian cancer tissues.
Results:
From gene expression profiles of five GEO datasets, 85 common up-regulated and 64 common down-regulated DEGs were identified. Moreover, the up-regulated DEGs were mainly enriched in mitosis related biological processes while the down-regulated DEGs were mainly enriched in amino acid metabolisms. In hub genes analysis, 10 and 9 hub genes, respectively, were obtained from up-regulated and down-regulated DEGs. High expression of hub gene FGF13 was confirmed to be significantly related to a better ovarian cancer survival. The selected hub genes, except for FGF13, showed high accuracy in the discrimination of ovarian cancer. According to q-PCR validation in ovarian cancer tissues, CDC20, CCNB1, BUB1B, KIF20A, BRIC5, CAV1, MEIS2, and CFH were finally considered as the potential diagnostic biomarkers for ovarian cancer.
Conclusions:
FGF13 is a potential prognostic biomarker for predicting patient survival outcome. In addition, CDC20, CCNB1, BUB1B, KIF20A, BIRC5, CAV1, MEIS2, and CFH are the potential biomarkers for ovarian cancer discrimination.
