Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis

Ming Gao; Qing Liu; Lingyu Zhang; Fatema Tabak; Yifei Hua; Wei Shao; Yangyang Li; Li Qian; Yu Liu

doi:10.7717/peerj.18660

Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis

PeerJ. 2025 Jan 9:13:e18660. doi: 10.7717/peerj.18660. eCollection 2025.

Authors

Ming Gao¹, Qing Liu¹, Lingyu Zhang¹, Fatema Tabak¹, Yifei Hua¹, Wei Shao¹, Yangyang Li¹, Li Qian¹, Yu Liu¹

Affiliation

¹ Department of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.

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: GZMB, CCL4, TNF, MMP9, CCL5, IFNG, CXCL1, GNLY, and LCN2. CTD analyses showed that LCN2, IFNG, and TNF had higher levels in NT1D and hypoglycemia; while TNF, IFNG and MMP9 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 LCN2, IFNG, TNF, and MMP9, may be potential diagnostic biomarkers for NT1D. Our findings provide new insights into the molecular mechanisms and novel therapeutic targets of NT1D.

Keywords: Bioinformatic gene analysis; Biomarkers; Extracellular protein; Gene expression omnibus; Type 1 diabetes.

MeSH terms

Biomarkers* / metabolism
Computational Biology* / methods
Databases, Genetic
Diabetes Mellitus, Type 1* / diagnosis
Diabetes Mellitus, Type 1* / genetics
Diabetes Mellitus, Type 1* / metabolism
Gene Expression Profiling
Gene Ontology
Gene Regulatory Networks
Humans
Prognosis
Protein Interaction Maps* / genetics

Substances

Biomarkers

Grants and funding

This research was funded by the National Natural Science Foundation of China (No. 82070849). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.