A novel four-serum marker model for early detection and therapeutic monitoring of breast cancer.

Early diagnosis of breast cancer (BC) remains pivotal for enhancing patient outcomes, yet current imaging modalities face inherent limitations. This retrospective study, encompassing 1366 BC patients and 1186 healthy controls (HC) from Shenzhen Nanshan People's Hospital (2022-2025), developed and validated a serum tumor marker-based predictive model to address this gap. Eight markers-CEA, AFP, CA199, CA242, CA15-3, CA125, FER, and NSE-were analyzed, with multivariate logistic regression identifying AFP, CA242, CA15-3, and NSE as independent diagnostic predictors. The resulting four-marker panel demonstrated robust performance, achieving an AUC of 0.90 (95% CI: 0.89-0.92) with 77.3% sensitivity and 89.7% specificity in the development cohort (n = 1535), and sustained efficacy in validation (AUC = 0.82, 87.5% sensitivity, 72.8% specificity; n = 761). The model effectively stratified early-stage (T1/T2: 86.49% accuracy) versus advanced-stage (T3/T4: 92.00%) tumors, lymph node involvement (N0: 86.49%; N+: 88.00%), metastatic status (M0: 87.32%; M+: 85.71%), and molecular subtypes (Luminal A: 91.33%; Luminal B: 82.35%; HER2+: 84.61%; triple-negative: 87.75%). Notably, longitudinal risk score trends correlated with therapeutic response, declining in remission and rising with progression. These findings collectively highlight the model's dual utility as a high-accuracy diagnostic tool for early BC detection and a dynamic biomarker for monitoring treatment efficacy. This novel model may provide an auxiliary approach in current screening paradigms, underscoring its transformative potential in oncology practice.