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Biomarkers for diagnosing type 1 diabetes mellitus through comprehensive bioinformatics analysis

bioinformatics
molecular-biology
diabetes-and-endocrinology
immunology

Abstract

Background : Type 1 diabetes mellitus (T1DM) is a chronic disease that significantly impacts patients’ quality of life. Its prevalence is rising globally each year. Our study aims to find potential biomarkers associated with T1DM by comprehensive bioinformatics analysis, further enhancing T1DM early diagnosis and treatment.
Methods: Transcriptome datasets from T1DM patients and the control group were from the GEO database. DEGs were were analyzed by GO enrichment, KEGG enrichment, and PPI network analysis. The hub genes were identified using ELISA on the clinical samples. The immune cell infiltration and diagnostic potential of the hub genes were evaluated by CIBERSORT and ROC analysis.
Results: 20 up-regulated and 8 down-regulated DEGs were identified in the GEO database. Functional enrichment analysis showed that immune activation plays an important role in T1DM. The hub genes, CTSG and LTF , were further confirmed through validation within clinical samples. The ROC curve analysis demonstrated that the two hub genes showed good diagnostic ability for the disease.
Conclusions: The results indicate that CTSG and LTF may serve as promising diagnostic biomarkers and therapeutic targets for T1DM.
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