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Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis

Triple-negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) ana...

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Autores principales: Zhong, Guansheng, Lou, Weiyang, Shen, Qinyan, Yu, Kun, Zheng, Yajuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947886/
https://www.ncbi.nlm.nih.gov/pubmed/31974598
http://dx.doi.org/10.3892/mmr.2019.10867
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author Zhong, Guansheng
Lou, Weiyang
Shen, Qinyan
Yu, Kun
Zheng, Yajuan
author_facet Zhong, Guansheng
Lou, Weiyang
Shen, Qinyan
Yu, Kun
Zheng, Yajuan
author_sort Zhong, Guansheng
collection PubMed
description Triple-negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome-wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non-TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top-ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan-Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.
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spelling pubmed-69478862020-01-13 Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis Zhong, Guansheng Lou, Weiyang Shen, Qinyan Yu, Kun Zheng, Yajuan Mol Med Rep Articles Triple-negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome-wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non-TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top-ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan-Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis. D.A. Spandidos 2020-02 2019-12-06 /pmc/articles/PMC6947886/ /pubmed/31974598 http://dx.doi.org/10.3892/mmr.2019.10867 Text en Copyright: © Zhong et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Zhong, Guansheng
Lou, Weiyang
Shen, Qinyan
Yu, Kun
Zheng, Yajuan
Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title_full Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title_fullStr Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title_full_unstemmed Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title_short Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
title_sort identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947886/
https://www.ncbi.nlm.nih.gov/pubmed/31974598
http://dx.doi.org/10.3892/mmr.2019.10867
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