Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan-Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.