Development and validation of a 14-CpG DNA methylation signature and drug targets for prognostic prediction in breast cancer.

Background: Breast cancer (BC) is the most prevalent cancer among women and a leading cause of cancer-related deaths worldwide. Emerging evidence suggests that DNA methylation, a well-studied epigenetic modification, regulates various cellular processes critical for cancer development and progression and holds promise as a biomarker for cancer diagnosis and prognosis, potentially enhancing the efficacy of precision therapies.

Methods: We developed a robust prognostic model for BC based on DNA methylation and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We analyzed the association of the model with clinicopathological features, survival outcomes, and chemotherapy drug sensitivity.

Results: A set of 216 differentially methylated CpGs was identified by intersecting three datasets (TCGA, GSE22249, and GSE66695). Using univariate Cox proportional hazard and LASSO Cox regression analyses, we constructed a 14-CpG model significantly associated with progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) in BC patients. Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) analysis, and nomogram validation confirmed the clinical value of the signature. The Cox analysis showed a significant association between the signature and PFI and DSS in BC patients. KM analysis effectively distinguished high-risk from low-risk patients, while ROC analysis demonstrated high sensitivity and specificity in predicting BC prognosis. A nomogram based on the signature effectively predicted 5- and 10-year PFI and DSS. Additionally, combining our model with clinical risk factors suggested that patients in the I-II & M⁺ subgroup could benefit from adjuvant chemotherapy regarding PFI, DSS, and OS. Gene Ontology (GO) functional enrichment and KEGG pathway analyses indicated that the top 3,000 differentially expressed genes (DEGs) were enriched in pathways related to DNA replication and repair and cell cycle regulation. Patients in the high-risk group might benefit from drugs targeting DNA replication and repair processes in tumor cells.

Conclusion: The 14-CpG model serves as a useful biomarker for predicting prognosis in BC patients. When combined with TNM staging, it offers a potential strategy for individualized clinical decision-making, guiding personalized therapeutic regimen selection for clinicians.