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Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis
BACKGROUND: Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aime...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506082/ https://www.ncbi.nlm.nih.gov/pubmed/37727764 http://dx.doi.org/10.3389/fmed.2023.1185303 |
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author | Qi, Xiaoyi Huang, Qianwen Wang, Shijia Qiu, Liangxian Chen, Xiongbiao Ouyang, Kunfu Chen, Yanjun |
author_facet | Qi, Xiaoyi Huang, Qianwen Wang, Shijia Qiu, Liangxian Chen, Xiongbiao Ouyang, Kunfu Chen, Yanjun |
author_sort | Qi, Xiaoyi |
collection | PubMed |
description | BACKGROUND: Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data. METHODS: Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software’s “limma” packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein–protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted. RESULTS: Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses. CONCLUSION: This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target. |
format | Online Article Text |
id | pubmed-10506082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105060822023-09-19 Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis Qi, Xiaoyi Huang, Qianwen Wang, Shijia Qiu, Liangxian Chen, Xiongbiao Ouyang, Kunfu Chen, Yanjun Front Med (Lausanne) Medicine BACKGROUND: Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data. METHODS: Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software’s “limma” packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein–protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted. RESULTS: Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses. CONCLUSION: This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target. Frontiers Media S.A. 2023-08-31 /pmc/articles/PMC10506082/ /pubmed/37727764 http://dx.doi.org/10.3389/fmed.2023.1185303 Text en Copyright © 2023 Qi, Huang, Wang, Qiu, Chen, Ouyang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Qi, Xiaoyi Huang, Qianwen Wang, Shijia Qiu, Liangxian Chen, Xiongbiao Ouyang, Kunfu Chen, Yanjun Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title | Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title_full | Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title_fullStr | Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title_full_unstemmed | Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title_short | Identification of the shared mechanisms and common biomarkers between Sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
title_sort | identification of the shared mechanisms and common biomarkers between sjögren’s syndrome and atherosclerosis using integrated bioinformatics analysis |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506082/ https://www.ncbi.nlm.nih.gov/pubmed/37727764 http://dx.doi.org/10.3389/fmed.2023.1185303 |
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