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The following is a summary of “Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation,” published in the January 2025 issue of Endocrinology by Wu et al.
Type 2 diabetes (T2DM) and metabolic fatty liver disease (MAFLD) frequently coexist, forming a complex interplay within the metabolic syndrome, and a strong association between these conditions is well-established.
Researchers conducted a retrospective study to identify potential biomarkers for diagnosing both T2DM and MAFLD.
They performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on public data from the Gene Expression Omnibus database to identify genes linked to both T2DM and MAFLD. Protein-protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to explore T2DM-related MAFLD genes and mechanisms. Candidate biomarkers were screened using machine learning algorithms and 12 cytoHubba algorithms. A diagnostic model for T2DM-related MAFLD was constructed and evaluated. Immune cell infiltration in MAFLD was investigated using the CIBERSORT method, examining the immunological significance of central genes. Whole blood from patients with T2DM-related MAFLD, MAFLD, and healthy individuals was collected to validate hub gene expression alongside high-fat, high-glucose cell models.
The results showed that differential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The T2DM-peripheral blood mononuclear cells/liver dataset revealed 91 T2DM-associated secreted proteins, PPI analysis highlighted 2 crucial modules of T2DM-related pathogenic genes in MAFLD, consisting of 49 nodes, indicating involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only genes shared between MAFLD key genes and T2DM-associated secreted proteins, forming the basis for highly accurate diagnostic models. Additionally, high-fat, high-glucose cell models were successfully developed, and TNFRSF1A and SERPINB2 expression patterns were validated in both patient blood and cellular models. Immune dysregulation in MAFLD was observed, with TNFRSF1A and SERPINB2 strongly associated with immune regulation.
Investigators concluded that SERPINB2 and TNFRSF1A influenced the development of T2DM-associated MAFLD, suggesting the potential as valuable biomarkers for improved diagnosis and prediction in patients with both T2DM and MAFLD.
Source: frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1512503/full
