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Bioinformatics and Machine Learning-Based Analysis of Key Ferroptosis-Related Genes in Major Depressive Disorder
Received date: 2025-09-10
Revised date: 2026-03-26
Accepted date: 2026-04-20
Online published: 2026-06-29
Objective： To identify key ferroptosis-related genes （FRGs） in major depressive disorder （MDD） and construct a diagnostic model using bioinformatics and machine learning， aiming to provide novel biomarkers for the clinical auxiliary diagnosis of MDD.Methods： MDD datasets were obtained from the GEO database. After standardization and batch effect correction， differentially expressed genes （DEGs） were screened. The intersection of DEGs with the ferroptosis-related gene set from FerrDb defined MDD-specific FRGs， which underwent functional enrichment analysis （GO and KEGG）. Hub-FRGs were subsequently identified through the combined application of support vector machine （SVM）， random forest （RF）， and least absolute shrinkage and selection operator （LASSO） regression. A logistic regression diagnostic model was constructed and evaluated using receiver operating characteristic （ROC） curve analysis， followed by validation in an independent set and immune infiltration analysis.Results： Twenty-nine FRGs were obtained， primarily enriched in biological functions and pathways related to oxidative stress， inflammation， and metabolism. Three genes—MAFG， LCN2， and G6PD—were selected as Hub-FRGs. The diagnostic model achieved an AUC of 0.932 in the training set and an AUC of 0.867 in the GSE76826 validation set. Immune infiltration analysis revealed significant correlations of macrophages， CD4⁺ T cells， and CD8⁺ T cells with the Hub-FRGs.Conclusion： The identified ferroptosis-related hub genes （MAFG， LCN2， and G6PD） possess the potential to serve as diagnostic biomarkers for MDD， providing new insights for the diagnosis and treatment of MDD.
Key words： Bioinformatics； ; Machine learning； ; Depression； ; Ferroptosis
LIU Jijun, ZHANG Hui
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Bioinformatics and Machine Learning-Based
Analysis of Key Ferroptosis-Related Genes in Major Depressive Disorder
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