A multi-omics machine learning analysis identifies five -related genes that may help distinguish NAFLD and explain immune-mitochondrial mechanisms.
Evidence
The evidence combines GEO transcriptomic and single-cell datasets, WGCNA, 11 machine learning algorithms, immune and pathway analyses, molecular docking, and in vitro NAFLD cell-model validation.
Caveat
The findings are biomarker and mechanism signals from public datasets plus cell models, not prospective clinical validation of diagnosis or therapy.
AI simplified
OBJECTIVE: This study aimed to systematically screen for -related core genes in Non-alcoholic fatty liver disease (NAFLD), elucidate their specific molecular regulatory network, and investigate their functional mechanisms and roles within the immune microenvironment to provide novel targets for disease diagnosis and therapy.
METHODS: Multiple NAFLD transcriptomic datasets and single-cell RNA sequencing data from the GEO database were integrated. Bioinformatics analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and 11 machine learning algorithms were employed for core gene screening. Functional mechanisms and immune microenvironment characteristics were further investigated using SHAP model interpretability analysis, including detailed immune infiltration analysis, PPI network construction, GSEA, single-cell trajectory inference, and cell-cell communication analysis. Reverse network pharmacology and molecular docking predicted potential targeted compounds. In vitro experiments (Western Blot, qRT-PCR, JC-1 staining) validated core gene expression and mitophagy levels.
RESULTS: Five key genes-IGF1, MYH11, HYOU1, SPATA18, and SCD-were identified, demonstrating excellent disease discrimination across multiple cohorts (training set =0.974). These genes were significantly enriched in processes like endoplasmic reticulum stress, mitophagy, and lipid metabolism. Critically, they played crucial roles in reshaping the NAFLD immune microenvironment, characterized by increased macrophage M2 polarization and T cell infiltration, linking mitochondrial dysfunction to inflammatory response. Single-cell analysis revealed their expression heterogeneity across hepatocytes, macrophages, and T cells, along with their involvement in intercellular communication patterns. Experimental validation confirmed aberrant core gene expression and altered mitophagy levels in NAFLD cell models.
CONCLUSION: This study systematically delineates the regulatory network of mitophagy-related core genes in NAFLD and the resultant inflammatory immune microenvironment, offering novel insights and data support for elucidating disease mechanisms, developing early diagnostic biomarkers, and formulating precise therapeutic strategies.
Key numbers
0.974
Performance
Maximum achieved across multiple validation cohorts.
5
Core Genes Identified
Five core genes linked to and immune responses in NAFLD.
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