Advancing Breast Cancer Biomarkers: A Centromere-Related Gene Signature Integrated with Single-Cell Analysis for Prognostic Prediction

Breast cancer is the most common malignancy among women worldwide and exhibits marked heterogeneity. Among its various subtypes, triple-negative breast cancer (TNBC) is associated with an inferior prognosis. Although molecular stratification tools such as Oncotype DX and MammaPrint have been adopted in clinical settings, prognostic models based on chromosomal instability remain inadequate. The centromere protein (CENP) family, as a key regulator of genomic stability, has been closely linked to tumor progression due to its aberrant expression. In this study, we integrated multi-omics data—including RNA transcriptomic profiles and single-cell RNA sequencing—and employed weighted gene co-expression network analysis (WGCNA) to identify core gene modules associated with CENPA. A prognostic risk model was developed using Cox regression analysis and the LASSO algorithm. Validation in independent cohorts demonstrated that the model effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly reduced five-year survival (p < 0.001). Furthermore, the single-cell analysis revealed that CENPA-high subpopulations were enriched in proliferative tumor cells and were associated with an immunosuppressive tumor microenvironment. This study is the first to systematically construct a CENP-based prognostic model for breast cancer, providing novel molecular biomarkers and potential therapeutic targets for personalized treatment. The biological function of the key molecule MMP1 in breast cancer was further validated through both in vitro and in vivo experiments.