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Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.

Juan Feng·Tianhang Zhao·Sien Lai·Yiyang Huang·Yufen Xie·Guojun Chen
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Abstract

Diabetes is a global public health problem with various complications, which can lead to disability and mortality. This study identified potential diagnostic markers for diabetes and explored the immunometabolic mechanisms in the pathological process. The gene expression of 17 diabetes cases and 16 normal controls were obtained from the Gene Expression Omnibus (GEO) database. The "limma" package was employed for screening differentially expressed genes (DEGs). Gene functions and enriched pathways of DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Candidate key genes were screened using the least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. The diagnostic effectiveness of identified markers was further verified via the receiver operating characteristic (ROC) curve. The compositional patterns of immune cell infiltration and signaling pathway enrichment associated with key genes were explored via single sample Gene Set Enrichment Analysis (ssGSEA) and GSEA analysis, respectively. Possible miRNAs interacting with key genes were predicted via miRcode database. B2M, FTL, SH3BGRL3, and SOD2 were recognized as diagnostic markers for diabetes based on LASSO regression and the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm. Analysis of immune cell infiltration demonstrated that the four key genes were related to B cells, neutrophils, macrophages, and CD8+ T cells. The diagnostic value of B2M, FTL, and SOD2 for diabetes was higher than that of SH3BGRL3 according to the ROC curve. Validation experiments indicated that the mRNA expression of B2M and FTL was increased in liver tissues of diabetic mice. B2M and FTL can act as diagnostic markers for diabetes and contribute to new understandings of the disease's molecular mechanisms.

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HumansGene Expression ProfilingGenetic MarkersCase-Control StudiesBiomarkersDiabetes MellitusROC CurveMicroRNAsSupport Vector MachineAnimalsJournal Article

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Juan Feng, Tianhang Zhao, Sien Lai, Yiyang Huang, Yufen Xie, Guojun Chen. 2026-09-07. Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.. https://doi.org/10.1590/1414-431x2026e15577

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