Identification of hub genes in triple-negative breast cancer by integrated bioinformatics analysis

Background: Triple negative breast cancer (TNBC) is usually aggressive and accompanied by a poor prognosis. The molecular biological mechanism of TNBC pathogenesis is still unclear, and requires more detailed research. The aim of this study was to screen and verify potential biomarkers of TNBC, and provide new clues for the treatment and diagnosis of TNBC. Methods: In this work, GSE76250 was downloaded from the Gene Expression Omnibus (GEO) database and included 165 TNBC samples and 33 paired normal breast tissues. The R software and its related software package were used for data processing and analysis. Compared with normal tissues, genes with a false discovery rate (FDR) <0.01 and log fold change (logFC) >= 1 or <=-1 were identified as differentially expressed genes (DEGs) by limma package. Survival prognoses were analyzed by Kaplan-Meier plotter database. Results: In total, 160 up-regulated and 180 down-regulated genes were identified. The biological mechanism of enrichment analysis presented that DEGs were significantly enriched in chromosome segregation, extracellular matrix, and extracellular matrix structural constituent, among others. A total of 8 hub genes (CCNB1, CDK1, TOP2A, MKI67, TTK, CCNA2, BUB1, and PLK1) were identified by the protein-protein interaction network (PPIN) and Cytoscape software. Survival prognosis of these hub genes showed that they were negatively correlated with overall survival. Conclusions: The 8 hub genes and pathways that were identified might be involved in tumorigenesis and become new candidate biomarkers for TNBC treatment.