{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T16:17:59Z","timestamp":1787761079952,"version":"build-2784847793"},"reference-count":51,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Blood &amp; Cancer"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>High\u2010dose methotrexate (HD\u2010MTX) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 106 pediatric patients with ALL undergoing HD\u2010MTX therapy. Of these, 56 developed moderate\u2010to\u2010severe 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Patients with liver injury showed distinct metabolic signatures compared with controls.\u00a0Prior to HD\u2010MTX treatment, 13 metabolites\u2014such as prominently elevated glycocholic acid\u2014were identified as potential predictors of liver injury, with overarching pathway alterations primarily involving arginine biosynthesis and glutathione metabolism, among others. Post\u2010treatment, 11 distinct metabolites\u2014including arachidic acid\u2014further distinguished the injury group, reflecting complex regulatory mechanisms and multi\u2010pathway shifts. Crucially, all three ML models developed using pre\u2010 or post\u2010treatment metabolic biomarkers exhibited high predictive and discriminative accuracy for hepatotoxicity (AUC &gt; 0.900).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings characterize the metabolic alterations underlying HD\u2010MTX\u2010induced liver injury, provide a metabolomics\u2010based framework for early risk assessment, and facilitate the development of precision therapeutic strategies in pediatric ALL.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/1545-5017.70424","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:25:57Z","timestamp":1784834757000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolomic Profiling and Machine Learning\u2010Based Prediction of High\u2010Dose Methotrexate\u2010Induced Liver Injury in Pediatric Acute Lymphoblastic Leukemia"],"prefix":"10.1002","volume":"73","author":[{"given":"Kai","family":"Guo","sequence":"first","affiliation":[{"name":"National Center for Clinical Laboratories Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing Hospital\/National Center of Gerontology  Beijing China"},{"name":"National Center for Clinical Laboratories Chinese Academy of Medical Sciences &amp; Peking Union Medical College  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Laboratory Medicine Hebei Yanda Lu Daopei Hospital  Langfang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8241-7361","authenticated-orcid":false,"given":"Xiaoran","family":"Feng","sequence":"additional","affiliation":[{"name":"National Center for Clinical Laboratories 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