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Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer
BACKGROUND AND OBJECTIVE: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499025/ https://www.ncbi.nlm.nih.gov/pubmed/31105744 http://dx.doi.org/10.3389/fgene.2019.00373 |
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author | Liu, Li Lin, Jiajing He, Hongying |
author_facet | Liu, Li Lin, Jiajing He, Hongying |
author_sort | Liu, Li |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes. METHODS AND RESULTS: An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein–protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver–operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively. CONCLUSION: This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction. |
format | Online Article Text |
id | pubmed-6499025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64990252019-05-17 Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer Liu, Li Lin, Jiajing He, Hongying Front Genet Genetics BACKGROUND AND OBJECTIVE: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes. METHODS AND RESULTS: An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein–protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver–operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively. CONCLUSION: This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction. Frontiers Media S.A. 2019-04-26 /pmc/articles/PMC6499025/ /pubmed/31105744 http://dx.doi.org/10.3389/fgene.2019.00373 Text en Copyright © 2019 Liu, Lin and He. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Liu, Li Lin, Jiajing He, Hongying Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title | Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title_full | Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title_fullStr | Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title_full_unstemmed | Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title_short | Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer |
title_sort | identification of potential crucial genes associated with the pathogenesis and prognosis of endometrial cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499025/ https://www.ncbi.nlm.nih.gov/pubmed/31105744 http://dx.doi.org/10.3389/fgene.2019.00373 |
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