Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The micr...

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Autores principales:	Chao Li, Zhantong Hong, Miaoling Ou, Xiaodan Zhu, Linghua Zhang, Xingkun Yang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/e02161b5f763439a8b4aafac8858bb9b
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spelling	oai:doaj.org-article:e02161b5f763439a8b4aafac8858bb9b2021-11-08T02:36:10ZIntegrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis2314-614110.1155/2021/6673655https://doaj.org/article/e02161b5f763439a8b4aafac8858bb9b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6673655https://doaj.org/toc/2314-6141Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.Chao LiZhantong HongMiaoling OuXiaodan ZhuLinghua ZhangXingkun YangHindawi LimitedarticleMedicineRENBioMed Research International, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Chao Li Zhantong Hong Miaoling Ou Xiaodan Zhu Linghua Zhang Xingkun Yang Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
description	Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.
format	article
author	Chao Li Zhantong Hong Miaoling Ou Xiaodan Zhu Linghua Zhang Xingkun Yang
author_facet	Chao Li Zhantong Hong Miaoling Ou Xiaodan Zhu Linghua Zhang Xingkun Yang
author_sort	Chao Li
title	Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
title_short	Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
title_full	Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
title_fullStr	Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
title_full_unstemmed	Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis
title_sort	integrated mirna-mrna expression profiles revealing key molecules in ovarian cancer based on bioinformatics analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/e02161b5f763439a8b4aafac8858bb9b
work_keys_str_mv	AT chaoli integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis AT zhantonghong integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis AT miaolingou integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis AT xiaodanzhu integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis AT linghuazhang integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis AT xingkunyang integratedmirnamrnaexpressionprofilesrevealingkeymoleculesinovariancancerbasedonbioinformaticsanalysis
_version_	1718443144921481216

Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

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