ArticleFrontiers in endocrinology2025
Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Senescence-Related SOCS1, SOCS2, and GADD45G Identify an Immune-Associated Molecular Signature in Metabolic Dysfunction-Associated Steatotic Liver Disease.Biomolecules · 2026Article
- Glycogen metabolic dysfunction in T2DM with MASLD: linking α-hydroxybutyrate to GYS2 downregulation.Frontiers in nutrition · 2026Article
- Metabolic dysfunction-associated fatty liver disease exacerbates hematoma expansion in intracerebral hemorrhage: an explainable machine learning approach.Frontiers in endocrinology · 2026Article
- Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.BMC medical informatics and decision making · 2025Article
- Exploring the biological functions and immune regulatory roles of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM combined with MAFLD: integrated bioinformatics and single-cell analysis.Frontiers in immunology · 2025Article
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10 authors.
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Abstract
Background: Type 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify potential biomarkers for diagnosing both conditions. Methods: We performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on publicly available data on the two diseases in the Gene Expression Omnibus database to find genes related to both conditions. We utilised protein-protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes to identify T2DM-associated MAFLD genes and potential mechanisms. Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. Finally, we collected whole blood from patients with T2DM-related MAFLD, MAFLD patients and healthy individuals, and used high-fat, high-glucose combined with high-fat cell models to verify the expression of hub genes. Results: Differential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The differential expression analysis of the T2DM-peripheral blood mononuclear cells/liver dataset screened 91 T2DM-associated secreted proteins. PPI analysis revealed two important modules of T2DM-related pathogenic genes in MAFLD, which contained 49 nodes, suggesting their involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only coexisting genes shared between MAFLD key genes and T2DM-related secreted proteins, enabling the construction of highly accurate diagnostic models for both disorders. Additionally, high-fat, high-glucose combined with high-fat cell models were successfully produced. The expression patterns of TNFRSF1A and SERPINB2 were verified in patient blood and our cellular model. Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation. Conclusion: The sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. These genes may significantly influence the development of T2DM-associated MAFLD, offering new diagnostic options for patients with T2DM combined with MAFLD.
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