Bioinformatics analysis reveling prognostic roles of CDK1 in breast cancer

Abstract Background The current treatment of breast cancer (BC) commonly adopts endocrine therapy and immunotherapy. And the acquired resistance to those therapies remains a major challenge. Recent evidences indicate that the alternation in tumor microenvironment is a critical factor for cancer therapeutic resistance. Thus, identification of genes that impact tumor micro-environment could contribute to early diagnosis of BC and development of potential therapeutic options. Methods In this study, 3 datasets were acquired from the GEO database to identify the differentially expressed genes (DEGs) between BC tissues and normal tissues. The GO functional and KEGG pathway enrichment of the DEGs were analyzed by using the DAVID database, followed by the construction of a protein-protein interaction (PPI) network. CytoHubba and MCODE were used to select hub genes. GEPIA, Kaplan-Meier Plotter, and UALCAN were used for key gene screening and investigating its effect on patients’ outcome. Results In total, 183 up-regulated and 85 down-regulated DEGs were identified. We identified 10 key genes, including CDK1, CEP55, CENPF, PRC1, TOP2A, NCAPG, KIF4A, CCNB1, ASPM, and KIF20A, and the expression of those genes was correlated well with overall survival of BC patients. Further, the expression level of CDK1 was positively correlated with macrophages, CD4 + T cells, CD8 + T cells and special MDSCs, and negatively correlated with CAFs cells. Conclusions Results of this study may reveal that CDK1 could influence clinicopathological parameters and immune micro-environment, especial immune cell infiltration including myeloid derived suppressor cells (MDSCs), which could serve as a prognostic biomarker in BC patients.