Identification of Crucial Extracellular Genes As Potential Biomarkers in Newly Diagnosed Type 1 Diabetes Integrated Bioinformatics Analysis

Overview

Journal PeerJ

Date 2025 Jan 13

PMID 39802181

Authors

Ming Gao

Qing Liu

Lingyu Zhang

Fatema Tabak

Yifei Hua

Wei Shao

Yangyang Li

Li Qian

Yu Liu

Affiliations

Soon will be listed here.

Abstract

Purpose: In this study, we aimed to study the role of extracellular proteins as biomarkers associated with newly diagnosed Type 1 diabetes (NT1D) diagnosis and prognosis.

Patients And Methods: We retrieved and analyzed the GSE55098 microarray dataset from the Gene Expression Omnibus (GEO) database. Using R software, we screened out the extracellular protein-differentially expressed genes (EP-DEGs) through several protein-related databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to describe the role and function of these EP-DEGs. We used the STRING database to construct the interaction of proteins, Cytoscape software to visualize the protein-protein interaction (PPI) networks, and its plugin CytoHubba to identify the crucial genes between PPI networks. Finally, we used the comparative toxicogenomics database (CTD) to evaluate the connection between NT1D with the potential crucial genes and we validated our conclusions with another dataset (GSE33440) and some clinical samples.

Results: We identified 422 DEGs and 122 EP-DEGs from a dataset that includes (12) NT1D patients compared with (10) healthy people. Protein digestion and absorption, toll-like receptor signaling, and T cell receptor signaling were the most meaningful pathways defined by KEGG enrichment analyses. We recognized nine important extracellular genes: , and . CTD analyses showed that , and had higher levels in NT1D and hypoglycemia; while and increased in hyperglycemia. Further verification showed that LCN2, MMP9, TNF and IFNG were elevated in NT1D patients.

Conclusion: The nine identified key extracellular genes, particularly , and , may be potential diagnostic biomarkers for NT1D. Our findings provide new insights into the molecular mechanisms and novel therapeutic targets of NT1D.

References

De George D, Ge T, Krishnamurthy B, Kay T, Thomas H . Inflammation versus regulation: how interferon-gamma contributes to type 1 diabetes pathogenesis. Front Cell Dev Biol. 2023; 11:1205590. PMC: 10244651. DOI: 10.3389/fcell.2023.1205590. View

Sarkar S, Elliott E, Henry H, Diaz Ludovico I, Melchior J, Frazer-Abel A . Systematic review of type 1 diabetes biomarkers reveals regulation in circulating proteins related to complement, lipid metabolism, and immune response. Clin Proteomics. 2023; 20(1):38. PMC: 10512508. DOI: 10.1186/s12014-023-09429-6. View

Barrett T, Wilhite S, Ledoux P, Evangelista C, Kim I, Tomashevsky M . NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2012; 41(Database issue):D991-5. PMC: 3531084. DOI: 10.1093/nar/gks1193. View

Sayers E, Cavanaugh M, Clark K, Pruitt K, Schoch C, Sherry S . GenBank. Nucleic Acids Res. 2021; 50(D1):D161-D164. PMC: 8690257. DOI: 10.1093/nar/gkab1135. View

Han D, Cai X, Wen J, Matheson D, Skyler J, Kenyon N . Innate and adaptive immune gene expression profiles as biomarkers in human type 1 diabetes. Clin Exp Immunol. 2012; 170(2):131-8. PMC: 3482359. DOI: 10.1111/j.1365-2249.2012.04650.x. View

Xiang Y, Zhou P, Li X, Huang G, Liu Z, Xu A . Heterogeneity of altered cytokine levels across the clinical spectrum of diabetes in China. Diabetes Care. 2011; 34(7):1639-41. PMC: 3120206. DOI: 10.2337/dc11-0039. View

Wang R, Zhou Y, Zhang Y, Li S, Pan R, Zhao Y . A Three-gene-based Type 1 Diabetes Diagnostic Signature. Curr Pharm Des. 2020; 27(24):2827-2833. DOI: 10.2174/1381612826666201217143403. View

Zipris D . Innate immunity and its role in type 1 diabetes. Curr Opin Endocrinol Diabetes Obes. 2008; 15(4):326-31. DOI: 10.1097/MED.0b013e3283073a46. View

Lin J, Lu Y, Wang B, Jiao P, Ma J . Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus. J Transl Med. 2021; 19(1):319. PMC: 8314644. DOI: 10.1186/s12967-021-02991-3. View

10.

Peeters S, Engelen L, Buijs J, Chaturvedi N, Fuller J, Schalkwijk C . Plasma levels of matrix metalloproteinase-2, -3, -10, and tissue inhibitor of metalloproteinase-1 are associated with vascular complications in patients with type 1 diabetes: the EURODIAB Prospective Complications Study. Cardiovasc Diabetol. 2015; 14:31. PMC: 4355971. DOI: 10.1186/s12933-015-0195-2. View

11.

Meagher C, Arreaza G, Peters A, Strathdee C, Gilbert P, Mi Q . CCL4 protects from type 1 diabetes by altering islet beta-cell-targeted inflammatory responses. Diabetes. 2007; 56(3):809-17. DOI: 10.2337/db06-0619. View

12.

Li X, Liao M, Guan J, Zhou L, Shen R, Long M . Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis. Diabetes Metab J. 2022; 46(3):451-463. PMC: 9171163. DOI: 10.4093/dmj.2021.0018. View

13.

Uhlen M, Fagerberg L, Hallstrom B, Lindskog C, Oksvold P, Mardinoglu A . Proteomics. Tissue-based map of the human proteome. Science. 2015; 347(6220):1260419. DOI: 10.1126/science.1260419. View

14.

Atkinson M, Eisenbarth G, Michels A . Type 1 diabetes. Lancet. 2013; 383(9911):69-82. PMC: 4380133. DOI: 10.1016/S0140-6736(13)60591-7. View

15.

Cooper M, Stewart P . Corticosteroid insufficiency in acutely ill patients. N Engl J Med. 2003; 348(8):727-34. DOI: 10.1056/NEJMra020529. View

16.

Kobayashi M, Kaneko-Koike C, Abiru N, Uchida T, Akazawa S, Nakamura K . Genetic deletion of granzyme B does not confer resistance to the development of spontaneous diabetes in non-obese diabetic mice. Clin Exp Immunol. 2013; 173(3):411-8. PMC: 3949628. DOI: 10.1111/cei.12134. View

17.

Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M . New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2018; 47(D1):D590-D595. PMC: 6324070. DOI: 10.1093/nar/gky962. View

18.

Vemulawada C, Renavikar P, Crawford M, Steward-Tharp S, Karandikar N . Disruption of IFNγ, GZMB, PRF1, or LYST Results in Reduced Suppressive Function in Human CD8+ T Cells. J Immunol. 2024; 212(11):1722-1732. PMC: 11105984. DOI: 10.4049/jimmunol.2300388. View

19.

Becker M, Peters L, Myint T, Smurlick D, Powell A, Brusko T . Immune engineered extracellular vesicles to modulate T cell activation in the context of type 1 diabetes. Sci Adv. 2023; 9(22):eadg1082. PMC: 10765990. DOI: 10.1126/sciadv.adg1082. View

20.

Koulmanda M, Bhasin M, Awdeh Z, Qipo A, Fan Z, Hanidziar D . The role of TNF-α in mice with type 1- and 2- diabetes. PLoS One. 2012; 7(5):e33254. PMC: 3350520. DOI: 10.1371/journal.pone.0033254. View