Objective:We sought to identify potential therapeutic targets for breast cancer patients by employing a bioinformatics analysis to screen for genes linked with an unfavorable prognosis.Methods:The Gene Expression Omni...Objective:We sought to identify potential therapeutic targets for breast cancer patients by employing a bioinformatics analysis to screen for genes linked with an unfavorable prognosis.Methods:The Gene Expression Omnibus(GEO)database was utilized to obtain three gene expression profile datasets,namely GSE42568,GSE86374,and GSE71053.To identify differentially expressed genes(DEGs),the GEO2R online tool was employed.Subsequently,a func-tional enrichment analysis was conducted.Moreover,a protein-protein interaction network was established using STRING,and DEGs were subjected to module analysis via Cytoscape software to identify pivotal genes.Additionally,the selected pivotal genes underwent further ex-amination and validation utilizing three databases:GEPIA,UALCAN,and Kaplan-Meier Plotter.Results:A total of 121 DEGs were detected,comprising 74 genes with increased expression and 47 genes with decreased expression.Ten key genes were identified:HMMR,RRM2,CDK1,TOP2A,AURKA,CCNB1,MAD2L1,KIF2C,BUB1B,UBE2C.Validation in the GEPIA database revealed high expression levels for all key genes except CDK1.A survival analysis conducted using the Kaplan-Meier Plotter database revealed noteworthy associations between nine crucial genes and the overall survival(OS)of individuals diagnosed with breast cancer.Moreover,these nine key genes exhibited significantly increased expression across different molecular subtypes of breast cancer according to the UALCAN data platform.Conclusions:We identified nine crucial genes significantly linked to the onset,progression,and unfavorable prognosis of breast cancer,providing potential targets for novel treatment options and biomarkers to predict patient outcomes.展开更多
基金funded by the National Natural Science Program Foundation of China(No.81260341)the Guangxi Natural Science Program Foundation(No.2017JJA10173).
文摘Objective:We sought to identify potential therapeutic targets for breast cancer patients by employing a bioinformatics analysis to screen for genes linked with an unfavorable prognosis.Methods:The Gene Expression Omnibus(GEO)database was utilized to obtain three gene expression profile datasets,namely GSE42568,GSE86374,and GSE71053.To identify differentially expressed genes(DEGs),the GEO2R online tool was employed.Subsequently,a func-tional enrichment analysis was conducted.Moreover,a protein-protein interaction network was established using STRING,and DEGs were subjected to module analysis via Cytoscape software to identify pivotal genes.Additionally,the selected pivotal genes underwent further ex-amination and validation utilizing three databases:GEPIA,UALCAN,and Kaplan-Meier Plotter.Results:A total of 121 DEGs were detected,comprising 74 genes with increased expression and 47 genes with decreased expression.Ten key genes were identified:HMMR,RRM2,CDK1,TOP2A,AURKA,CCNB1,MAD2L1,KIF2C,BUB1B,UBE2C.Validation in the GEPIA database revealed high expression levels for all key genes except CDK1.A survival analysis conducted using the Kaplan-Meier Plotter database revealed noteworthy associations between nine crucial genes and the overall survival(OS)of individuals diagnosed with breast cancer.Moreover,these nine key genes exhibited significantly increased expression across different molecular subtypes of breast cancer according to the UALCAN data platform.Conclusions:We identified nine crucial genes significantly linked to the onset,progression,and unfavorable prognosis of breast cancer,providing potential targets for novel treatment options and biomarkers to predict patient outcomes.
