Identification of a seven-gene signature and establishment of a prognostic nomogram predicting overall survival of triple-negative breast cancer

Abstract

Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype characterized by the absence of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC exhibits resistance to hormone and HER2-targeted therapy, along with a higher incidence of recurrence and poorer prognosis. Therefore, exploring the molecular features of TNBC and constructing prognostic models are of significant importance for personalized treatment strategies. Methods: In this research, bioinformatics approaches were utilized to screen differentially expressed genes in 405 TNBC cases and 128 normal tissue samples from 8 GEO datasets. Key core genes and signaling pathways were further identified. Additionally, a prognostic model incorporating seven genes was established using clinical and pathological information from 169 TNBC cases in the TCGA dataset, and its predictive performance was evaluated. Results: Functional analysis revealed dysregulated biological processes such as DNA replication, cell cycle, and mitotic chromosome separation in TNBC. Protein-protein interaction network analysis identified ten core genes, including BUB1, BUB1B, CDK1, CDC20, CDCA8, CCNB1, CCNB2, KIF2C, NDC80, and CENPF. A prognostic model consisting of seven genes (EXO1, SHCBP1, ABRACL, DMD, THRB, DCDC2, and APOD) was established using a step-wise Cox regression analysis. The model demonstrated good predictive performance in distinguishing patients’ risk. Conclusion: This research provides important insights into the molecular characteristics of TNBC and establishes a reliable prognostic model for understanding its pathogenesis and predicting prognosis. These findings contribute to the advancement of personalized treatment for TNBC.