摘要
Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancersubtype characterized by the absence of expression of estrogen receptor (ER), progesteronereceptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC exhibitsresistance to hormone and HER2-targeted therapy, along with a higher incidence ofrecurrence and poorer prognosis. Therefore, exploring the molecular features of TNBC andconstructing prognostic models are of significant importance for personalized treatmentstrategies. Methods: In this research, bioinformatics approaches were utilized to screendifferentially expressed genes in 405 TNBC cases and 128 normal tissue samples from 8 GEOdatasets. Key core genes and signaling pathways were further identified. Additionally, aprognostic model incorporating seven genes was established using clinical and pathologicalinformation from 169 TNBC cases in the TCGA dataset, and its predictive performance wasevaluated. Results: Functional analysis revealed dysregulated biological processes such asDNA replication, cell cycle, and mitotic chromosome separation in TNBC. Protein-proteininteraction network analysis identified ten core genes, including BUB1, BUB1B, CDK1,CDC20, CDCA8, CCNB1, CCNB2, KIF2C, NDC80, and CENPF. A prognostic model consistingof seven genes (EXO1, SHCBP1, ABRACL, DMD, THRB, DCDC2, and APOD) was establishedusing a step-wise Cox regression analysis. The model demonstrated good predictiveperformance in distinguishing patients' risk. Conclusion: This research provides importantinsights into the molecular characteristics of TNBC and establishes a reliable prognosticmodel for understanding its pathogenesis and predicting prognosis. These findingscontribute to the advancement of personalized treatment for TNBC.
