Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis

Xiaoyi Qi; Qianwen Huang; Shijia Wang; Liangxian Qiu; Xiongbiao Chen; Kunfu Ouyang; Yanjun Chen

doi:10.3389/fmed.2023.1185303

Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis

Front Med (Lausanne). 2023 Aug 31:10:1185303. doi: 10.3389/fmed.2023.1185303. eCollection 2023.

Authors

Xiaoyi Qi^{1

2}, Qianwen Huang¹, Shijia Wang¹, Liangxian Qiu¹, Xiongbiao Chen¹, Kunfu Ouyang³, Yanjun Chen¹

Affiliations

¹ Departments of Cardiology, Peking University Shenzhen Hospital, Shenzhen, China.
² Medical College, Shantou University, Shantou, China.
³ Department of Cardiovascular Surgery, Peking University Shenzhen Hospital, Shenzhen, China.

Abstract

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.

Keywords: GEO database; Sjögren’s syndrome; atherosclerosis; bioinformatics; immune infiltration.