Identification of estrogen-related biomarkers in metabolic dysfunction-associated steatotic liver disease (MASLD) through machine learning and single-cell analysis.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a prevalent chronic liver disease linked to metabolic risk factors. Sex hormones, particularly estrogen, appear to influence MASLD development, as premenopausal women are relatively protected. This study aims to identify estrogen-related biomarkers of MASLD using integrated bioinformatics, machine learning, and single-cell RNA sequencing (scRNA-seq) approaches. METHODS: Six public transcriptomic datasets (five training, one validation) were analyzed to find differentially expressed genes (DEGs) in MASLD versus controls. Estrogen-related genes (ERGs) were obtained from the Molecular Signatures Database (MSigDB) and intersected with DEGs to define estrogen-associated DEGs (DEERGs). Machine learning algorithms-least absolute shrinkage and selection operator (LASSO) regression and extreme gradient boosting (XGBoost)-were applied to select key feature genes, which were then validated in an independent cohort. Immune cell infiltration was evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). Consensus clustering based on feature genes defined molecular subtypes. Additionally, a human liver scRNA-seq dataset was analyzed to map cell-type-specific expression and pathway activity. A competing endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA interactions was constructed for the diagnostic genes. RESULTS: Fourteen DEERGs were identified, and two diagnostic biomarkers-IGFBP2 and P4HA1-showed high diagnostic accuracy. Both genes were associated with immune infiltration and defined two estrogen-related molecular subtypes with distinct immune microenvironment features. Single-cell analysis localized IGFBP2 and P4HA1 to hepatocytes, implicating estrogen-related regulation in metabolic and fibrotic remodeling. CellChat analysis revealed weakened hepatocyte-centered signaling in MASLD. The ceRNA network suggested that multiple lncRNAs (e.g., NEAT1, MALAT1, XIST) may modulate these biomarkers via shared miRNAs. CONCLUSION: Through integrated multi-cohort analysis, we identified IGFBP2 and P4HA1 as novel estrogen-related tissue biomarkers of MASLD. Preliminary exploration suggests their potential detectability in peripheral blood, though large-scale tissue and protein-level validations are required. Our findings provide insight into sex hormone-linked mechanisms in MASLD and propose candidate molecular targets for improved risk stratification and therapy.