Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis

Objective: The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. Materials and methods: We screened and validated candidate genes associated with carcinogenesis and development of epitheli...

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Autores principales:	Wenqiong Qin, Qiang Yuan, Yi Liu, Ying Zeng, Dandan ke, Xiaoyan Dai, Yu Shuai, Jiaqi Hu, Hua Shi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Ovarian cancer Differentially expressed genes Bioinformatics analysis Kaplan–Meier curve Transcriptional factors Gynecology and obstetrics RG1-991
Acceso en línea:	https://doaj.org/article/6d3c4a3a5fef4cb5bffabbaa52dfa8f9
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spelling	oai:doaj.org-article:6d3c4a3a5fef4cb5bffabbaa52dfa8f92021-11-18T04:44:39ZIdentification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis1028-455910.1016/j.tjog.2021.09.007https://doaj.org/article/6d3c4a3a5fef4cb5bffabbaa52dfa8f92021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1028455921002473https://doaj.org/toc/1028-4559Objective: The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. Materials and methods: We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. Results: Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan–Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. Conclusion: Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.Wenqiong QinQiang YuanYi LiuYing ZengDandan keXiaoyan DaiYu ShuaiJiaqi HuHua ShiElsevierarticleOvarian cancerDifferentially expressed genesBioinformatics analysisKaplan–Meier curveTranscriptional factorsGynecology and obstetricsRG1-991ENTaiwanese Journal of Obstetrics & Gynecology, Vol 60, Iss 6, Pp 983-994 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ovarian cancer Differentially expressed genes Bioinformatics analysis Kaplan–Meier curve Transcriptional factors Gynecology and obstetrics RG1-991
spellingShingle	Ovarian cancer Differentially expressed genes Bioinformatics analysis Kaplan–Meier curve Transcriptional factors Gynecology and obstetrics RG1-991 Wenqiong Qin Qiang Yuan Yi Liu Ying Zeng Dandan ke Xiaoyan Dai Yu Shuai Jiaqi Hu Hua Shi Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
description	Objective: The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. Materials and methods: We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. Results: Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan–Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. Conclusion: Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.
format	article
author	Wenqiong Qin Qiang Yuan Yi Liu Ying Zeng Dandan ke Xiaoyan Dai Yu Shuai Jiaqi Hu Hua Shi
author_facet	Wenqiong Qin Qiang Yuan Yi Liu Ying Zeng Dandan ke Xiaoyan Dai Yu Shuai Jiaqi Hu Hua Shi
author_sort	Wenqiong Qin
title	Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
title_short	Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
title_full	Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
title_fullStr	Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
title_full_unstemmed	Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
title_sort	identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6d3c4a3a5fef4cb5bffabbaa52dfa8f9
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Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis

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