Identification and validation of KIF20A for predicting prognosis and treatment outcomes in patients with breast cancer

Breast cancer is a leading cause of cancer-related deaths among women globally. It is imperative to explore novel biomarkers to predict breast cancer treatment response as well as progression. Here, we collected six breast cancer samples and paired normal tissues for high-throughput sequencing. By differential expression analysis, we found 1687 DEGs and identified the top 10 hub genes, including TOP2A, CDK1, BUB1B, KIF11, CCNA2, BUB1, CCNB1, KIF20A, DLGAP5 and CDC20. Univariate and multivariate Cox analyses on the METABRIC database and GSE96058 dataset demonstrated that KIF20A was an independent prognostic predictor for overall survival. KIF20A was positively correlated with cell cycle phases, including the cell cycle process, cycle G2 M phase transition and cell cycle DNA replication initiation. Single-cell analyses revealed that KIF20A was enriched in fibroblasts and endothelial within breast cancer stroma. Meanwhile, multidrug resistance (MDR) genes ABCB1, ABCC1 and ABCG2 were co-expressed with KIF20A in fibroblasts and endothelial cells within the stroma. MTABRIC database confirmed that high expression of KIF20A was positively correlated with treatment efficacy in patients with breast cancer. In conclusion, KIF20A could be served as a predictive biomarker for breast cancer prognosis and treatment outcomes. KIF20A may play a significant role by regulating cell cycle progression and modulating stromal progression in breast cancer. Our findings provided novel molecular insights that can guide personalized treatment strategies in breast cancer.