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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| Veröffentlicht in: | Molecular medicine reports 2020-02, Vol.21 (2), p.557-566 |
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| creator | Zhong, Guansheng Lou, Weiyang Shen, Qinyan Yu, Kun Zheng, Yajuan |
| 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 |
| doi_str_mv | 10.3892/mmr.2019.10867 |
| format | Article |
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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.</description><identifier>ISSN: 1791-2997</identifier><identifier>EISSN: 1791-3004</identifier><identifier>DOI: 10.3892/mmr.2019.10867</identifier><identifier>PMID: 31974598</identifier><language>eng</language><publisher>Greece: Spandidos Publications</publisher><subject>17β-Estradiol ; Analysis ; Apoptosis ; Biochemistry ; Biological markers ; Biomarkers ; Breast cancer ; Cancer genetics ; Care and treatment ; Chemotherapy ; Datasets ; Disease ; DNA-directed RNA polymerase ; Estradiol ; Estrogens ; Gene expression ; Gene regulation ; Genes ; Genetic aspects ; Genomes ; Genomics ; Health aspects ; Life expectancy ; Life span ; Novels ; Ontology ; Patients ; Phenols (Class of compounds) ; Prognosis ; RNA ; Sex hormones ; Signal transduction ; Survival analysis ; Transcription ; Transcription (Genetics) ; Tumorigenesis ; Tumors ; Womens health</subject><ispartof>Molecular medicine reports, 2020-02, Vol.21 (2), p.557-566</ispartof><rights>COPYRIGHT 2020 Spandidos Publications</rights><rights>Copyright Spandidos Publications UK Ltd. 2020</rights><rights>Copyright: © Zhong et al. 2020</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-f40073f8e098aa88280142b961a1d88fe9f7493313cecaf0bae36b168dcd676b3</citedby><cites>FETCH-LOGICAL-c485t-f40073f8e098aa88280142b961a1d88fe9f7493313cecaf0bae36b168dcd676b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31974598$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhong, Guansheng</creatorcontrib><creatorcontrib>Lou, Weiyang</creatorcontrib><creatorcontrib>Shen, Qinyan</creatorcontrib><creatorcontrib>Yu, Kun</creatorcontrib><creatorcontrib>Zheng, Yajuan</creatorcontrib><title>Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis</title><title>Molecular medicine reports</title><addtitle>Mol Med Rep</addtitle><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.</description><subject>17β-Estradiol</subject><subject>Analysis</subject><subject>Apoptosis</subject><subject>Biochemistry</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Breast cancer</subject><subject>Cancer genetics</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Datasets</subject><subject>Disease</subject><subject>DNA-directed RNA polymerase</subject><subject>Estradiol</subject><subject>Estrogens</subject><subject>Gene expression</subject><subject>Gene regulation</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Health aspects</subject><subject>Life expectancy</subject><subject>Life span</subject><subject>Novels</subject><subject>Ontology</subject><subject>Patients</subject><subject>Phenols (Class of compounds)</subject><subject>Prognosis</subject><subject>RNA</subject><subject>Sex hormones</subject><subject>Signal transduction</subject><subject>Survival analysis</subject><subject>Transcription</subject><subject>Transcription (Genetics)</subject><subject>Tumorigenesis</subject><subject>Tumors</subject><subject>Womens health</subject><issn>1791-2997</issn><issn>1791-3004</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkk1rFTEUhgdRbK1uXUrAjZt7zdfkYyOU4keh4EbXIZM5GdPOJNdkpnBx41_oX-wvMaPX-kHJIiHnPc_hPbxN85zgLVOavp6mvKWY6C3BSsgHzTGRmmwYxvzh4U21lkfNk1IuMRYtbfXj5ogRLXmr1XHz7byHOAcfnJ1Diih5dAV7NECEgmxBuzSvdTuiLqTJ5ivIBfmU0ZzDboTb7zcRhtp6DajLYMuMnI0OMlpKiAMKcYYh13p9V2aagqvYaMd9CeVp88jbscCzw33SfH739tPZh83Fx_fnZ6cXG8dVO288x1gyrwBrZa1SVGHCaacFsaRXyoP2kmvGCHPgrMedBSY6IlTveiFFx06aN7-4u6WboHfVULaj2eVQDe1NssH8W4nhixnStRGaS6VEBbw6AHL6ukCZzRSKg3G0EdJSDGWcU0mIIlX68j_pZVpyNbyqWIsJkZL9UQ12BBOiT3WuW6HmVBBa57Z0ZW3vUdXTQ91jiuBD_b-vweVUSgZ_55Fgs8bF1LiYNS7mZ1xqw4u_N3Mn_50P9gNRd75o</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Zhong, Guansheng</creator><creator>Lou, Weiyang</creator><creator>Shen, Qinyan</creator><creator>Yu, Kun</creator><creator>Zheng, Yajuan</creator><general>Spandidos Publications</general><general>Spandidos Publications UK Ltd</general><general>D.A. Spandidos</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200201</creationdate><title>Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis</title><author>Zhong, Guansheng ; Lou, Weiyang ; Shen, Qinyan ; Yu, Kun ; Zheng, Yajuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c485t-f40073f8e098aa88280142b961a1d88fe9f7493313cecaf0bae36b168dcd676b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>17β-Estradiol</topic><topic>Analysis</topic><topic>Apoptosis</topic><topic>Biochemistry</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Breast cancer</topic><topic>Cancer genetics</topic><topic>Care and treatment</topic><topic>Chemotherapy</topic><topic>Datasets</topic><topic>Disease</topic><topic>DNA-directed RNA polymerase</topic><topic>Estradiol</topic><topic>Estrogens</topic><topic>Gene expression</topic><topic>Gene regulation</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Health aspects</topic><topic>Life expectancy</topic><topic>Life span</topic><topic>Novels</topic><topic>Ontology</topic><topic>Patients</topic><topic>Phenols (Class of compounds)</topic><topic>Prognosis</topic><topic>RNA</topic><topic>Sex hormones</topic><topic>Signal transduction</topic><topic>Survival analysis</topic><topic>Transcription</topic><topic>Transcription (Genetics)</topic><topic>Tumorigenesis</topic><topic>Tumors</topic><topic>Womens health</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhong, Guansheng</creatorcontrib><creatorcontrib>Lou, Weiyang</creatorcontrib><creatorcontrib>Shen, Qinyan</creatorcontrib><creatorcontrib>Yu, Kun</creatorcontrib><creatorcontrib>Zheng, Yajuan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Molecular medicine reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhong, Guansheng</au><au>Lou, Weiyang</au><au>Shen, Qinyan</au><au>Yu, Kun</au><au>Zheng, Yajuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis</atitle><jtitle>Molecular medicine reports</jtitle><addtitle>Mol Med Rep</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>21</volume><issue>2</issue><spage>557</spage><epage>566</epage><pages>557-566</pages><issn>1791-2997</issn><eissn>1791-3004</eissn><abstract>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.</abstract><cop>Greece</cop><pub>Spandidos Publications</pub><pmid>31974598</pmid><doi>10.3892/mmr.2019.10867</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 17β-Estradiol Analysis Apoptosis Biochemistry Biological markers Biomarkers Breast cancer Cancer genetics Care and treatment Chemotherapy Datasets Disease DNA-directed RNA polymerase Estradiol Estrogens Gene expression Gene regulation Genes Genetic aspects Genomes Genomics Health aspects Life expectancy Life span Novels Ontology Patients Phenols (Class of compounds) Prognosis RNA Sex hormones Signal transduction Survival analysis Transcription Transcription (Genetics) Tumorigenesis Tumors Womens health |
| title | Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis |
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