Analysis of the relationships between interferon-stimulated genes and anti-SSA/Ro 60 antibodies in primary Sjögren's syndrome patients via multiomics and machine learning methods.

BACKGROUND

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands. Interferon-stimulated genes (ISGs) are often upregulated in patients with pSS, and anti-SSA/Ro 60 antibodies are among the main items detected in pSS. However, the relationship between ISGs and anti-SSA/Ro 60 antibodies in pSS remains unclear. Therefore, our study aimed to explore the relationships between ISGs and anti-SSA/Ro 60 antibodies and identify key genes involved in pSS.

METHODS

Transcriptome sequencing and microarray data of PBMC, parotid and labial glands tissues from pSS patients (1341 samples) were downloaded from the GEO database and used in this analysis. A linear model was used to analyze the relationship between ISGs and anti-SSA/Ro 60 antibodies. ELISA was used to measure the levels of anti-SSA/Ro 60 antibodies in the whole blood of pSS patients. Machine learning methods were used to construct a diagnostic model, and further validation was conducted with external datasets and RT-qPCR.

RESULTS

After differential analysis of the PBMCs and tissues, 22 and 160 DEGs, respectively, were identified. There was a positive correlation between ISGs and anti-SSA/Ro 60 antibodies in the PBMCs of pSS patients, and there was a significant positive correlation between ISG expression in the PBMCs and tissues of pSS patients. The expression levels of ISGs were increased in the PBMCs and tissues of pSS patients with positive anti-SSA/Ro 60 antibody results, and the ESSDAI, focus scores and CD45 levels were increased. Finally, the expression of EPSTI1, EIF2AK2, IFI44, RSAD2, and OAS2 in PBMCs can be used to diagnose pSS.

CONCLUSION

In patients with pSS, a positive correlation was observed between ISGs and anti-SSA/Ro 60 antibodies. The expression of ISGs reflects the disease severity and pathology scores of pSS patients. Through bioinformatics analysis combined with our clinical data, we identified five key genes with potential diagnostic value for pSS.