Identification of Glycolysis-Related Genes in MAFLD and Their Immune Infiltration Implications: A Multi-Omics Analysis with Experimental Validation.

Background: Metabolic-associated fatty liver disease (MAFLD) is characterized by metabolic syndrome and immune infiltration, with glycolysis pathway activation emerging as a pivotal contributor. This study aims to identify glycolysis-associated key genes driving MAFLD progression and elucidate their crosstalk with immune infiltration through bioinformatics analysis and experimental validation. Methods: Integrative multi-omics analysis was performed on bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets from MAFLD patients and controls. Differential expression analysis and WGCNA were employed to pinpoint glycolysis-correlated key genes. The relationship with immune infiltration was analyzed using single-cell and spatial transcriptomics technologies. Machine learning was applied to identify feature genes for matching shared TFs and miRNAs. External cohort validation and in vivo experiments (methionine choline-deficient diet murine models) were conducted for biological confirmation. Results: Five glycolysis-associated key genes (ALDH3A1, CDK1, DEPDC1, HKDC1, SOX9) were identified and validated as MAFLD discriminators. Single-cell analysis revealed that the hepatocyte-fibroblast-macrophage axis constitutes the predominant glycolysis-active niche. Spatial transcriptomics showed that CDK1, SOX9, and HKDC1 were colocalized with the monocyte-derived macrophage marker CCR2. Using four machine learning models, four feature genes were identified, along with their common transcription factors YY1 and FOXC1, and the miRNA "hsa-miR-590-3p". External datasets and experimental validation confirmed that the key genes were upregulated in MAFLD samples. Conclusions: In this study, we identified five glycolysis-related key genes in MAFLD and explored their relationship with immune infiltration, providing new insights for diagnosis and metabolism-directed immunomodulation strategies in MAFLD.