Integrating single-cell RNA-seq and machine learning to dissect polyamine metabolism in metabolic dysfunction-associated steatotic liver disease.

Background: Dysregulated immunometabolism is central to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Although polyamines contribute to cellular stress responses and immune-cell function, their cell-type-specific transcriptional associations within the hepatic immune microenvironment remain incompletely understood. Methods: We assessed polyamine metabolism in MASLD at the single-cell level using AUCell, UCell, singscore, and AddModuleScore. To find metabolism-related genes, we performed differential analyses. We then combined six machine learning methods-including LASSO, Random Forest, XGBoost, GBM, SVM, and Boruta-to identify and sort robust disease genes. We further evaluated these findings using bulk transcriptomic datasets and a CDAA-induced MASH mouse model. Results: We observed notable differences in polyamine metabolic activity among various liver cell types, with relatively higher levels detected in macrophages, cholangiocytes, and stromal cells. PSMB7 and PSMD7 emerged as proteasome-associated candidate genes that were enriched in macrophages and upregulated in MASLD. Higher expression of these genes was associated with immune-related transcriptional programs, including antigen-processing/presentation signatures and predicted intercellular communication pathways involving MIF- and TNFSF13B-related signaling. Their upregulation was further supported by bulk RNA analyses and the CDAA-induced MASH model. Conclusion: Our single-cell analysis showed clear heterogeneity in polyamine-metabolism-related states in MASLD, with macrophages emerging as a major associated cell population. PSMB7 and PSMD7 emerged as proteasome-associated candidate markers enriched in macrophage populations with elevated polyamine-metabolism scores, providing a framework for future mechanistic investigation.