生物信息学方法鉴定ER阴性乳腺癌关键基因及预测潜在治疗药物

Objective: To explore the key genes and potential therapeutic drugs for ER-negative breast cancer by bioinformatics. Methods: The gene expression profile of breast cancer (GSE22219) was downloaded from the Gene Expression Omnibus (GEO). Principal components analysis (PCA) of GSE22219, and analyses of differentially expressed genes (DEGs) between the ER-negative and ER-positive subjects and Gene Ontology (GO) analysis were performed by R software. We analyzed The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Protein-Protein Interaction (PPI) network using STRING. The hub genes were identified using Cytoscape and analyzed using online programs. Drugbank analysis was used to find small molecular compounds as potential therapeutic agents to target the DEGs. Results: We detect 69 DEGs and 8 hub genes between the ER-negative and ER-positive subjects. We found the most significant KEGG pathway of DEGs was aldosterone-regulated sodium reabsorption. The Gene Ontology (GO) analysis indicated that the most significantly enriched in prostate gland morphogenesis. Totally 21 small molecular compounds were identified as potential therapeutic agents for ER-negative breast cancer. Conclusion: The bioinformatical analysis combined with drug database can help us find potential therapeutic agents to treat diseases. This method is a new paradigm which can guide future research on drugs.