Eur J Med Res. 2025 Dec 17.
BACKGROUND: The pathological hallmarks of Type 2 diabetes mellitus (T2DM) are impaired insulin sensitivity and insufficient insulin secretion. As the primary insulin source, pancreatic β-cell decline or dysfunction is key to T2DM progression. Disrupted mitochondria-associated endoplasmic reticulum membranes (MAMs) may compromise β-cell viability and function. This study aimed to identify MAMs-related biomarkers in T2DM pathogenesis.
METHODS: Data were obtained from public databases, and the biomarkers related to MAMs in T2DM were identified by differential expression analysis, WGCNA, supervised machine learning, and expression validation. Subsequently, a nomogram for predicting the prevalence of T2DM was developed, and the performance was evaluated. Additionally, we conducted immune infiltration analysis, GSEA, and molecular docking were performed to analyze the underlying mechanisms of the identified biomarkers. Finally, RT-qPCR was used to further validate the expression trends of these biomarkers.
RESULTS: Three key biomarkers-DUSP26, SLC15A1, and TBX1-were discovered, and the nomogram developed using these markers exhibited strong predictive accuracy for T2DM risk. Interestingly, these biomarkers were predominantly associated with the olfactory transduction pathway and neuroactive ligand-receptor interactions. Additionally, five distinct immune cell types were identified (p < 0.05). Among these, Th2 cells showed the highest positive correlation with activated CD4 T cells (r = 0.45), whereas activated dendritic cells displayed the strongest negative correlation with activated CD4 T cells (r = -0.42). Furthermore, all 3 biomarkers displayed favorable binding abilities with all 3 therapeutic agents for T2DM (< -5.0 kcal/mol), suggesting the potential of biomarkers in the treatment of T2DM. Ultimately, the trend of 3 biomarker expression in the clinical samples was consistent with the GSE184050 and GSE15932, with up-regulated expression, revealing the reliability of biomarker identification.
CONCLUSION: The biomarkers DUSP26, SLC15A1, and TBX1 related to MAMs in T2DM were identified, which supplied a theoretical basis for T2DM-related mechanistic studies and clinical treatment.
Keywords: Biomarkers; Immune infiltration analysis; Machine learning; Mitochondria-associated endoplasmic reticulum membranes; Type 2 diabetes mellitus