SLC11A1 Protein as a Key Regulator of Iron Metabolism, Ferroptosis Mediator, and Putative Therapeutic Target in Nonalcoholic Fatty Liver Disease: An Integrated Bioinformatics Analysis

Yang WangBugao Zhou^*Shanshan LILinxin ZHENGXiongfeng HuangHuiyu WANGSili LIYuhan LINYanhe XU

• Jiangxi University of Traditional Chinese Medicine, Nanchang, China

Background: Nonalcoholic fatty liver disease (NAFLD) has become one of the most prevalent chronic liver diseases worldwide, with its incidence closely linked to metabolic syndromes such as obesity and diabetes. Studies have indicated that dysregulated iron metabolism and ferroptosis play critical roles in its pathological progression, underscoring the urgent need for in-depth exploration of novel biomarkers and therapeutic strategies. Methods: This study utilized NAFLD datasets from the GEO database and applied bioinformatics approaches to identify iron metabolism and ferroptosis-related differentially expressed genes (DEGs) in NAFLD. Key regulatory proteins—ERN1, SLC11A1, MYC, TLR7, and PPARGC1A—were screened using weighted gene co-expression network analysis (WGCNA) and a machine learning algorithm (LASSO). Their correlations with immune microenvironment features were also evaluated. Validation sets confirmed the differential expression of ERN1 and SLC11A1, with area under the receiver operating characteristic curve (AUC) values of 0.855 and 0.89, respectively, and a combined AUC of 0.923. Additionally, single-cell RNA sequencing (scRNA-seq) was applied to analyze the cell type-specific expression and functional characteristics of these genes during NAFLD development. Molecular docking coupled with molecular dynamics simulations was employed to evaluate the binding patterns and dynamic stability of Resmetirom—a drug approved for the treatment of nonalcoholic fatty liver disease in adults—with the protein structures of ERN1 and SLC11A1. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to validate the expression differences of key protein biomarkers at the tissue level. Results: A total of 26 iron metabolism/ferroptosis-related DEGs significantly associated with NAFLD were identified. Machine learning algorithms confirmed ERN1, SLC11A1, MYC, TLR7, and PPARGC1A as diagnostic biomarkers. Immune microenvironment analysis elucidated correlations between the expression of these key proteins and immune cell infiltration. Molecular docking and dynamics simulations predicted that Resmetirom may exert a potential targeted effect by stably binding to the protein structures of ERN1 and SLC11A1. Experimental validation confirmed significant differential expression of ERN1 and SLC11A1 proteins in NAFLD tissues. Conclusion: This study successfully identified specific proteins related to iron metabolism/ferroptosis pathways, such as ERN1 and SLC11A1, which demonstrate significant diagnostic potential for NAFLD, with SLC11A1 potentially possessing greater diagnostic value as a biomarker.