Identification of common ferroptosis-related genes in metabolic dysfunction-associated steatotic liver disease and atherosclerosis using the support vector machine-recursive feature elimination algorithm

BACKGROUND: This study aims to identify common ferroptosis-related key genes involved in both Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) and Atherosclerosis (AS) through the Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm. The goal is to uncover shared pathogenic mechanisms between the two diseases and explore potential therapeutic targets and drug candidates.METHODS: Microarray data related to MASLD (GSE89632) and AS (GSE43292) were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis of ferroptosis-related genes (DE-FRGs) was conducted using the “limma” R package, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the “clusterProfiler” R package. The SVM-RFE algorithm was applied to identify key feature genes common to both MASLD and AS. The diagnostic efficacy of individual key genes was evaluated using Receiver Operating Characteristic (ROC) curve analysis, and a logistic regression model was constructed to validate their diagnostic performance. A drug-gene regulatory network was constructed using the DGIdb database, and a ceRNA regulatory network was predicted using public databases.RESULTS: GO and KEGG enrichment analyses revealed 5 shared biological processes (BP) and 21 common KEGG pathways between MASLD and AS. The SVM-RFE algorithm identified 8 common ferroptosis-related key genes, including BRDT, SLC16A1, CDCA3, MAP3K14, USP11, PRKAA2, GRIA3, and PLIN2. A drug-gene regulatory network comprising 25 targeted drugs and 3 key feature genes was constructed, alongside a ceRNA regulatory network consisting of 181 lncRNAs, 192 miRNAs, and 7 mRNAs.CONCLUSIONS: This study identified 8 key ferroptosis-related genes using the SVM-RFE algorithm, elucidating their role in shared pathogenic mechanisms between MASLD and AS. Metformin and the flavonoid compound Hesperadin were found to improve both MASLD and AS through multiple mechanisms, providing new therapeutic targets and strategies for combined treatment approaches.