Ferroptosis has been recognized as a pivotal mechanism in the progression of liver fibrosis and nonalcoholic fatty liver disease (NAFLD), yet its role in NAFLD-induced liver fibrosis (NILF) remains poorly characterized, and reliable diagnostic biomarkers are currently lacking. To address this gap, we employed an integrated bioinformatics approach to identify ferroptosis-related biomarkers for NILF. Transcriptomic data from patients with NILF were obtained from the Gene Expression Omnibus (GEO) database, and ferroptosis-related genes were collected from FerrDb. Key module genes were screened by weighted gene co-expression network analysis (WGCNA), and their intersection with ferroptosis-related genes was subsequently analyzed via protein-protein interaction (PPI) network to identify candidate hub genes. Three machine learning algorithms—LASSO, random forest, and SVM-RFE—were applied to refine the candidate genes, leading to the identification of four central hub genes (IL1B, IL6, SOCS1, TNFAIP3). These genes demonstrated high diagnostic accuracy for NILF (AUC = 0.974). Furthermore, drug prediction analysis indicated that cycloheximide, profenamine, dexamethasone, and chromium compounds may target these hub genes, suggesting their therapeutic potential. Our study reveals novel ferroptosis-linked biomarkers with diagnostic and therapeutic relevance for NILF, offering insights for early detection and targeted intervention.

Siyu Wei, Zhengjie Li, Hongping Mo, Mingming Song, Rui Liu, Tao Pan, Xudong Wen. Identification of Ferroptosis-Related Hub Genes and Machine Learning Algorithms in NAFLD-Induced Liver Fibrosis. Frontiers in Medical Science Research (2025), Vol. 7, Issue 6: 1-14. https://doi.org/10.25236/FMSR.2025.070601.

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