Identification of biomarkers related to tumorigenesis and prognosis in breast cancer

BACKGROUND: The aim of the present study was to identify the central genes and prognostic index of breast cancer, and to determine the relationship between prognostic index and immune infiltration levels to provide useful information for the diagnosis and treatment of breast cancer. METHODS: The Cancer Genome Atlas breast cancer dataset and 2 microarray datasets were applied to screen overlapping differentially expressed genes (DEGs) between breast cancer tissue and normal breast tissue samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted through the Database for Annotation, Visualization, and Integrated Discovery. Protein-protein interaction (PPI) networks were used to screen hub genes of the overlapping DEGs. Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), and The Human Protein Atlas (HPA) databases were used to validate their expression. The correlation of hub genes with immune infiltration was analyzed using TISIDB software. Kaplan-Meier Plotter was used to analyze the prognosis of hub genes. RESULTS: Ten hub genes [cyclin A2 (CCNA2), cyclin dependent kinase 1 (CDK1), centromere protein F (CENPF), kinesin family member 2C (KIF2C), kinesin family member 4A (KIF4A), maternal embryonic leucine zipper kinase (MELK), PDZ binding kinase (PBK), protein regulator of cytokinesis 1 (PRC1), DNA topoisomerase II alpha (TOP2A), and TPX2 microtubule nucleation factor (TPX2)] were selected and their overexpression in breast cancer tissue was verified. All were associated with a poor prognosis for breast cancer. CDK1, CENPF, KIF2C, KIF4A, MELK, PBK, PRC1, and TPX2 were correlated with CD4 T cells in breast cancer, while TOP2A was correlated with CD8 T cells. CONCLUSIONS: The findings indicated that the 10 hub genes could be potential biomarkers for progression in breast cancer.