ArticleDiabetes & metabolism journal2022
Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis.
Article in Diabetes & metabolism journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 19 citations in OpenAlex.
- Peripheral Immune Reprogramming Characterizes a Low C-Peptide Subgroup of Type 2 Diabetes: Transcriptomic Profiling of Peripheral Blood Mononuclear Cells and Systemic Inflammation.Journal of inflammation research · 2026Article
- Analysis of vitamin D-induced immunomodulatory gene expression changes in type 1 diabetes: from computational prediction to experimental validation.Molecular biology reports · 2025Article
- Hypoxia-associated genes and metabolic abnormalities in peripheral blood mononuclear cells of type 1 diabetes mellitus patients.Hereditas · 2025Article
- Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetesPeerJ · 2025Article
- Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot.iScience · 2024Article
- Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer.BMC genomics · 2024Article
- Advanced Delivery Strategies for Immunotherapy in Type I Diabetes Mellitus.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- Combining bioinformatics and machine learning algorithms to identify and analyze shared biomarkers and pathways in COVID-19 convalescence and diabetes mellitus.Frontiers in endocrinology · 2023Article
- Transcriptional analysis of human peripheral blood mononuclear cells stimulated byFrontiers in cellular and infection microbiology · 2023Article
- Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches.BioMed research international · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM.
methodsCommon differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb).
resultsWe found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM.
conclusionThese results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.
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