Plasma metabolomics combined with machine learning for postoperative prognostic stratification in hormone receptor-positive/ human epidermal growth factor receptor 2-negative early breast cancer

Background and aims Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2(-)) breast cancer (BC) accounts for the majority of BC cases. Although early-stage patients generally have favorable outcomes, recurrence and metastasis substantially worsen prognosis. We aimed to evaluate whether plasma metabolomics combined with machine learning could predict postoperative outcomes in HR+/HER2(-) early BC. Methods A total of 178 patients with HR+/HER2(-) BC were prospectively enrolled. Preoperative plasma samples were analyzed using untargeted metabolomics based on liquid chromatography-mass spectrometry. Differential metabolites between patients with short (<5 years) and long (>= 5 years) disease-free survival (DFS) were identified. Using least absolute shrinkage and selection operator feature selection followed by random forest classification, a prognostic model was constructed in a discovery cohort (n = 125) and validated in an independent test cohort (n = 53). Results A total of 209 metabolites were annotated, of which 100 were significantly altered between groups. Key dysregulated metabolic pathways included linoleic acid and galactose metabolism. A 10-metabolite prognostic model (10-PM) achieved an area under the receiver operating characteristic (AUROC) curve of 0.907 (95% CI: 0.883-0.925) in the test cohort, outperforming conventional clinicopathological models. The 10-PM model effectively stratified patients into high- and low-risk groups with significantly different DFS and overall survival, and remained an independent predictor in multivariable Cox regression analysis. Conclusion We developed and validated a 10-PM model based on plasma metabolomics that accurately predicts short DFS in HR+/HER2(-) BC. This model outperforms conventional clinicopathological indicators, offering a specific, blood-based tool to stratify high-risk patients and guide individualized postoperative management. (c) 2026 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.