Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

Abstract Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignori...

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Autores principales:	Esra Gov, Kazim Yalcin Arga
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8e6d35ccb86d42a1a3f3e3444c72b144
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spelling	oai:doaj.org-article:8e6d35ccb86d42a1a3f3e3444c72b1442021-12-02T11:52:21ZDifferential co-expression analysis reveals a novel prognostic gene module in ovarian cancer10.1038/s41598-017-05298-w2045-2322https://doaj.org/article/8e6d35ccb86d42a1a3f3e3444c72b1442017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05298-whttps://doaj.org/toc/2045-2322Abstract Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.Esra GovKazim Yalcin ArgaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Esra Gov Kazim Yalcin Arga Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
description	Abstract Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.
format	article
author	Esra Gov Kazim Yalcin Arga
author_facet	Esra Gov Kazim Yalcin Arga
author_sort	Esra Gov
title	Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
title_short	Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
title_full	Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
title_fullStr	Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
title_full_unstemmed	Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
title_sort	differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8e6d35ccb86d42a1a3f3e3444c72b144
work_keys_str_mv	AT esragov differentialcoexpressionanalysisrevealsanovelprognosticgenemoduleinovariancancer AT kazimyalcinarga differentialcoexpressionanalysisrevealsanovelprognosticgenemoduleinovariancancer
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Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

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