Background The Nonalcoholic fatty liver disease (NAFLD) is a major chronic liver condition, with its pathology remaining elusive. Consequently, a thorough exploration of the underlying mechanisms driving NAFLD progression is essential for a comprehensive understanding of this metabolism-associated disorder. Methods The scRNA-seq data from liver specimens of healthy and NAFLD subjects from GSE182365 was examined in the current study. High-dimensional weighted gene co-expression network analysis and seven machine learning (ML) approaches were employed to identify and confirm a genetic profile that precisely detects this population. Subsequently, we utilized MCP counter and non-negative matrix factorization to explore immune cell infiltration patterns. Lastly, the comparative expression of seven optimal feature genes in vivo and in vitro were confirmed through wet experiments. Results Single-cell sequencing findings unveiled a distinct fibroblast subset, differentially infiltrated in NAFLD and control cohorts, expressing genes linked to NAFLD pathogenesis. HdWGCNA and ML algorithms screened and identified the essential signature defining this cohort, comprising 7 genes, with function enrichment analysis indicating their strong link to the cell cycle pathway. Notably, we established a connection between these 7 genes and immune cell infiltration and patient variability. Concurrently, wet experiments illustrated the actual expression disparities of our seven genes in NAFLD and normal models both in vivo and in vitro . Conclusions Our investigation has uncovered a distinct fibroblast population closely associated with NAFLD pathogenesis. The related genes, including CCL2, CRYAB, HSBP1, IFIT3, PSMB10, RBX1, and SPARC, with potential promoting or inhibitory roles in NAFLD pathogenesis, were subsequently identified and represented a crucial step in developing prospective clinical diagnostic method for this condition.
Zhang et al. (2026) studied this question.