Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis
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. Us...
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PeerJ Inc.
2025-01-01
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| author | Ming Gao Qing Liu Lingyu Zhang Fatema Tabak Yifei Hua Wei Shao Yangyang Li Li Qian Yu Liu |
| author_facet | Ming Gao Qing Liu Lingyu Zhang Fatema Tabak Yifei Hua Wei Shao Yangyang Li Li Qian Yu Liu |
| author_sort | Ming Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-49bc262f38d14629ad61b790958a59572025-01-11T15:05:15ZengPeerJ Inc.PeerJ2167-83592025-01-0113e1866010.7717/peerj.18660Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysisMing GaoQing LiuLingyu ZhangFatema TabakYifei HuaWei ShaoYangyang LiLi QianYu LiuPurpose 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.https://peerj.com/articles/18660.pdfType 1 diabetesBioinformatic gene analysisBiomarkersGene expression omnibusExtracellular protein |
| spellingShingle | Ming Gao Qing Liu Lingyu Zhang Fatema Tabak Yifei Hua Wei Shao Yangyang Li Li Qian Yu Liu Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis PeerJ Type 1 diabetes Bioinformatic gene analysis Biomarkers Gene expression omnibus Extracellular protein |
| title | Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis |
| title_full | Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis |
| title_fullStr | Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis |
| title_full_unstemmed | Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis |
| title_short | Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis |
| title_sort | identification of crucial extracellular genes as potential biomarkers in newly diagnosed type 1 diabetes via integrated bioinformatics analysis |
| topic | Type 1 diabetes Bioinformatic gene analysis Biomarkers Gene expression omnibus Extracellular protein |
| url | https://peerj.com/articles/18660.pdf |
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