Metabolomic Profiling and Machine Learning‐Based Prediction of High‐Dose Methotrexate‐Induced Liver Injury in Pediatric Acute Lymphoblastic Leukemia

BACKGROUND: High-dose methotrexate (HD-MTX) is essential for pediatric acute lymphoblastic leukemia (ALL) but frequently causes hepatotoxicity. To address the critical lack of early prediction tools, this study investigated untargeted metabolomic profiles to identify predictive biomarkers and establish robust machine learning (ML) models for early clinical risk assessment and therapeutic optimization. METHODS: This study included 106 pediatric patients with ALL undergoing HD-MTX therapy. Of these, 56 developed moderate-to-severe liver injury. Untargeted plasma metabolomics was performed before and after treatment. Metabolic alterations were identified using multivariate analyses, with pathway annotation performed via KEGG enrichment. Key discriminatory metabolites were further screened via LASSO regression to identify potential biomarkers. Three ML algorithms (Random Forest, Support Vector Machine, and Bayesian logistic regression) were developed for risk prediction, integrating SHAP analysis for model interpretability and feature contribution evaluation. RESULTS: Patients with liver injury showed distinct metabolic signatures compared with controls. Prior to HD-MTX treatment, 13 metabolites-such as prominently elevated glycocholic acid-were identified as potential predictors of liver injury, with overarching pathway alterations primarily involving arginine biosynthesis and glutathione metabolism, among others. Post-treatment, 11 distinct metabolites-including arachidic acid-further distinguished the injury group, reflecting complex regulatory mechanisms and multi-pathway shifts. Crucially, all three ML models developed using pre- or post-treatment metabolic biomarkers exhibited high predictive and discriminative accuracy for hepatotoxicity (AUC > 0.900). CONCLUSIONS: These findings characterize the metabolic alterations underlying HD-MTX-induced liver injury, provide a metabolomics-based framework for early risk assessment, and facilitate the development of precision therapeutic strategies in pediatric ALL.