Identification of Fatty acid metabolism-Related Genes in the Progression from Non-Alcoholic Fatty Liver to Nonalcoholic fatty liver disease

Abstract Aim To elucidate the regulatory mechanisms of fatty acid metabolism-related genes (FAMRGs) and the gene expression clustering in nonalcoholic fatty liver disease (NAFLD). Methods The NAFLD dataset was sourced from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified and their specific functions analyzed. Biomarkers were identified using machine learning and Weighted Gene Co-Expression Network Analysis. CIBERSORT evaluated immune cell infiltration and the relationship between biomarkers and immune cells. Fatty acid metabolism-related DEGs (FAMRDEGs) were identified, and consensus clustering differentiated NAFLD patients into two clusters. Clinical differences between subtypes were compared. Principal component analysis calculated cluster-specific gene scores, and single sample gene set enrichment analysis assessed the proportion of immune cells between clusters. Results A total of 2124 DEGs were identified, primarily associated with immune-related pathways. Among 44 FAMRDEGs, FMO1 was identified as a biomarker for NAFLD and validated using an independent dataset, qRT-PCR, and WB. Immune cell infiltration analysis suggested that NAFLD may be co-regulated by immune cells and FMO1. Clustering of NAFLD individuals based on the 44 FAMRDEGs revealed that genes in cluster A were predominantly related to immune pathways, while those in cluster B were associated with metabolic pathways. Disease severity was higher in cluster A, which also had a larger proportion of differing immune cells compared to cluster B. Conclusion FMO1 was identified as a biomarker for NAFLD. High expression of PPT1 and PTGS2 correlated with disease severity. The identification of NAFLD subgroups based on has enhanced our knowledge of NAFLD etiology.