Abstract: Objective · To identify hub genes and key pathways in breast cancerbioinformatics analysis that integrated gene data with clinical survival analysis. Methods · Three gene profilings downloaded Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) in breast cancer. Kaplan-Meier plotter was used to identify the DEGs that were significantly associated with overall survival in breast cancer. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Next, hub genes were identified the protein-protein interaction (PPI) network. Oncomine and the Human Protein Atlas (HPA) database were used to validate the of the hub genes. The s of hub genes in MDA-MB-231 cells and MCF-10A cells were detectedquantitative real-time PCR (qPCR). Results · Among the DEGs, 262 genes were significantly correlated with overall survival of breast cancer patients. The results of GO functional analysis and KEGG pathway analysis showed that these genes were associated with nuclear division, cell division and chromosome segregation, and were mainly enriched on the pathways such as cell cycle, FoxO signaling pathway and oocyte meiosis. PPI network construction identified ten hub genes. They were all highly expressed in breast cancer, which were validatedthe databases. The results of qPCR showed that 8 out of 10 hub genes were highly expressed in breast cancer cells. Conclusion · The hub genes and key pathways involved in the development of breast cancer are identifiedsurvival-based bioinformatics analysis, which are mainly associated with cell cycle regulation and cell division.

Key words: breast cancer, bioinformatics, hub gene, survival analysis, biomarker