AI-Guided Ferroptosis Biomarker Discovery and Routine Laboratory-Based Machine Learning for Predicting Nodal...

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-normal 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 ΔAUC = +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.