Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.

<h4>Background</h4>Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or...

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Autores principales:	Ting Gui, Chenhe Yao, Binghan Jia, Keng Shen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c47fe0c0c8484b58998fd63f1d35222c
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spelling	oai:doaj.org-article:c47fe0c0c8484b58998fd63f1d35222c2021-12-02T20:10:24ZIdentification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.1932-620310.1371/journal.pone.0253136https://doaj.org/article/c47fe0c0c8484b58998fd63f1d35222c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253136https://doaj.org/toc/1932-6203<h4>Background</h4>Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.<h4>Methods</h4>Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.<h4>Results</h4>A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.<h4>Conclusion</h4>Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.Ting GuiChenhe YaoBinghan JiaKeng ShenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253136 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ting Gui Chenhe Yao Binghan Jia Keng Shen Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
description	<h4>Background</h4>Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.<h4>Methods</h4>Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.<h4>Results</h4>A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.<h4>Conclusion</h4>Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.
format	article
author	Ting Gui Chenhe Yao Binghan Jia Keng Shen
author_facet	Ting Gui Chenhe Yao Binghan Jia Keng Shen
author_sort	Ting Gui
title	Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
title_short	Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
title_full	Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
title_fullStr	Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
title_full_unstemmed	Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
title_sort	identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c47fe0c0c8484b58998fd63f1d35222c
work_keys_str_mv	AT tinggui identificationandanalysisofgenesassociatedwithepithelialovariancancerbyintegratedbioinformaticsmethods AT chenheyao identificationandanalysisofgenesassociatedwithepithelialovariancancerbyintegratedbioinformaticsmethods AT binghanjia identificationandanalysisofgenesassociatedwithepithelialovariancancerbyintegratedbioinformaticsmethods AT kengshen identificationandanalysisofgenesassociatedwithepithelialovariancancerbyintegratedbioinformaticsmethods
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Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.

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