Evidence mapPaperPMID 41408348Full record

ArticleEuropean journal of medical research2025

Identification and validation of biomarkers related to mitochondria-associated endoplasmic reticulum membranes in type 2 diabetes mellitus using peripheral blood transcriptomics.

Sufen Li, Yanqiong Yan, Qianjun Luo, Ruifei Tian, Jiahe Yan

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Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Sufen LiDepartment of Endocrinology, Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518000, China.
Yanqiong YanDepartment of Endocrinology, Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518000, China. 13670165047@163.com.
Qianjun LuoDepartment of Endocrinology, Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518000, China.
Ruifei TianDepartment of Endocrinology, Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518000, China.
Jiahe YanDepartment of Endocrinology, Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518000, China.

Funding

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6 · The paper itself

Abstract

backgroundThe 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.

methodsData 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.

resultsThree 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.

conclusionThe 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.

Indexed as

Diabetes Mellitus, Type 2Endoplasmic ReticulumMitochondriaTranscriptomeBiomarkersGene Expression ProfilingHumansBiomarkersBiomarkersImmune infiltration analysisMachine learningMitochondria-associated endoplasmic reticulum membranesType 2 diabetes mellitus

Identifiers

PMID41408348
PMCPMC12822018

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.