Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics
Abstract Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes an...
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doaj-6b67ec384c72482ca97b9bf932548fbb2021-07-18T11:39:20ZengBMCJournal of Ovarian Research1757-22152021-07-0114111210.1186/s13048-021-00837-6Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformaticsCuicui Dong0Xin Tian1Fucheng He2Jiayi Zhang3Xiaojian Cui4Qin He5Ping Si6Yongming Shen7Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Medical Laboratory, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Department of Clinical Lab, The Children’s Hospital of Tianjin (Children’s Hospital of Tianjin University)Abstract Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis. Methods The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes. Results In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database. Conclusion Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.https://doi.org/10.1186/s13048-021-00837-6Ovarian CancerGene Expression OmnibusBioinformatics AnalysisHub Genes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cuicui Dong Xin Tian Fucheng He Jiayi Zhang Xiaojian Cui Qin He Ping Si Yongming Shen |
spellingShingle |
Cuicui Dong Xin Tian Fucheng He Jiayi Zhang Xiaojian Cui Qin He Ping Si Yongming Shen Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics Journal of Ovarian Research Ovarian Cancer Gene Expression Omnibus Bioinformatics Analysis Hub Genes |
author_facet |
Cuicui Dong Xin Tian Fucheng He Jiayi Zhang Xiaojian Cui Qin He Ping Si Yongming Shen |
author_sort |
Cuicui Dong |
title |
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
title_short |
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
title_full |
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
title_fullStr |
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
title_full_unstemmed |
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
title_sort |
integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics |
publisher |
BMC |
series |
Journal of Ovarian Research |
issn |
1757-2215 |
publishDate |
2021-07-01 |
description |
Abstract Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis. Methods The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes. Results In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database. Conclusion Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study. |
topic |
Ovarian Cancer Gene Expression Omnibus Bioinformatics Analysis Hub Genes |
url |
https://doi.org/10.1186/s13048-021-00837-6 |
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