High-efficient Screening Method for Identification of Key Genes in Breast Cancer Through Microarray and Bioinformatics

Background/Aim: The aim of the present study was to identify key pathways and genes in breast cancer and develop a new method for screening key genes with abnormal expression based on bioinformatics. Materials and Methods: Three microarray datasets GSE21422, GSE42568 and GSE45827 were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were analyzed using GEO2R. The gene ontology (GO) and pathway enrichment analysis were established through DAVID database. The protein-protein interaction (PPI) network was performed through the Search Tool for the Retrieval of Interacting Genes (STRING) database and managed by Cytoscape. The overall survival (OS) analysis of the 4 genes including AURKA, CDH1, CDK1 and PPARG that had higher degrees in this network was uncovered Kaplan-Meier analysis. Results: A total of 811 DEGs were identified in breast cancer, which were enriched in biological processes, including cell cycle, mitosis, vessel development and lipid metabolic. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the up-regulated DEGs were particularly involved in cell cycle, progesterone-mediated oocyte maturation and leukocyte transendothelial migration, while the down-regulated DEGs were mainly involved in regulation of lipolysis, fatty acid degradation and glycerolipid metabolism. Through PPI network analysis, 14 hub genes were identified. Among them, the high expression of AURKA, CDH1 and CDK1 were associated with worse OS of breast cancer patients; while the high expression of PPARG was linked with better OS. Conclusion: The present study identified key pathways and genes involved in breast cancer which are potential molecular targets for breast cancer treatment and diagnosis.