{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:38:26Z","timestamp":1786117106104,"version":"build-2736575974"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>Background: Accurate preoperative prediction of lymph node metastasis (LNM) remains challenging in colon cancer. Ferroptosis, an iron-dependent regulated cell-death pathway, is implicated in tumor invasion and metastasis. Yet, the clinical value of routine iron-metabolism indices as indirect systemic indicators of ferroptosis-relevant biology remains underexplored. Methods: A multi-stage workflow was implemented. First, transformer-based literature mining (PubMedBERT, BioLinkBERT, BioBERT) prioritized ferroptosis-associated genes and metabolites. Second, transcriptomic validation was performed in TCGA-COAD and GSE39582 cohorts using differential expression analysis. Third, machine-learning models (logistic regression, random forest, XGBoost) were developed to predict pathologically confirmed LNM (n = 421) under three feature configurations: preoperative baseline, preoperative plus iron-metabolism markers, and a pathology-augmented model. LVI prediction was separately evaluated (n = 416) using ferroptosis-only, clinical-only, and combined sets. Results: Literature mining identified a core ferroptosis axis dominated by redox and iron-handling regulators. Transcriptomic analyses demonstrated robust tumor\u2013normal separation and consistent perturbation of key ferroptosis genes (SLC7A11, GPX4, ACSL4, PTGS2). Preoperative models achieved moderate LNM discrimination (best AUC = 0.704), whereas a postoperative pathology-augmented explanatory benchmark achieved an AUC of 0.818. Out-of-fold risk stratification identified a low-risk subgroup with 26.7% nodal positivity, compared with 44.7% in the overall cohort. For LVI prediction, models based on iron-handling indices showed modest discrimination, and their addition to clinical variables produced model-dependent numerical changes in performance (maximum \u0394AUC = +0.114). These routinely measured indices provided exploratory predictive information but should not be interpreted as direct measures of tumor ferroptosis. Conclusions: This integrative framework links ferroptosis-related biology with clinical machine learning and suggests that routine iron-handling indices may provide exploratory complementary information for LVI prediction and preoperative nodal-risk stratification, whereas postoperative pathology variables provide additional explanatory value but are not part of a deployable preoperative model.<\/jats:p>","DOI":"10.3390\/metabo16080557","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T13:03:27Z","timestamp":1786021407000},"page":"557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Guided Ferroptosis Biomarker Discovery and Routine Laboratory\u2013Based Machine Learning for Predicting Nodal Metastasis in Colon Cancer"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6174-915X","authenticated-orcid":false,"given":"Nail","family":"Besli","sequence":"first","affiliation":[{"name":"Department of Medical Biology, Hamidiye School of Medicine, University of Health Sciences, Istanbul 34668, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0583-6440","authenticated-orcid":false,"given":"Talar","family":"Vartanoglu Aktokmakyan","sequence":"additional","affiliation":[{"name":"General Surgery Department, Surp Pirgic Armenian Hospital, Istanbul 34020, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adil","family":"Koyuncu","sequence":"additional","affiliation":[{"name":"General Surgery Department, Istanbul Haseki Training and Research Hospital, University of Health Sciences, Istanbul 34130, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4407-3829","authenticated-orcid":false,"given":"Bahar","family":"Sarikamis Johnson","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Faculty of Medicine, Istanbul Atlas University, Istanbul 34403, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulkan","family":"Celik","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Faculty of Medicine, Istanbul Atlas University, Istanbul 34403, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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