Bioinformatics analysis of key genes associated with the prognosis of breast cancer

作者：Kun Zhou, Daolai Huang, Hui-Chao Ruan, Xianghua Wu · 发表于：journal of nutritional oncology · 年份：2023 · DOI：10.1097/jn9.0000000000000024 · 被引用次数：2 · 研究领域：Ferroptosis and cancer prognosis、Bioinformatics and Genomic Networks、RNA modifications and cancer

Abstract Background 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 3 gene expression profile datasets, namely, GSE42568, GSE86374, and GSE71053. To identify differentially expressed genes (DEGs), the GEO2R online tool was employed. Subsequently, a functional 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 examination and validation utilizing 3 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 , and 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 9 crucial genes and the overall survival of individuals diagnosed with breast cancer. Moreover, these 9 key genes exhibited significantly increased expression across different molecular subtypes of breast cancer according to the UAL...