Abstract
Primary Sjögren's syndrome (pSS) is a chronic inflammatory autoimmune disease, which mainly damages patients' exocrine glands. Sensitive early diagnostic indicators and effective treatments for pSS are lacking. Using machine learning methods to find diagnostic markers and effective therapeutic ways for pSS is of great significance. In our study, first, 1643 differentially expressed genes (DEGs; 737 were upregulated and 906 were downregulated) were ultimately screened out and analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes based on the datasets from the Gene Expression Omnibus. Then, support vector machine, least absolute shrinkage and selection operator regression, random forest, and weighted correlation network analysis were used to screen out feature genes from DEGs. Subsequently, the intersection of the feature genes was taken to screen 10 genes as hub genes. Meanwhile, the analysis of the diagnostic efficiency of 10 hub genes showed their good diagnostic value for pSS, which was validated through immunohistochemistry on the paraffin sections of the labial gland. Subsequently, a multi-factor regulatory network and correlation analysis of hub genes were performed, and the results showed that ELAVL1 and IGF1R were positively correlated with each other but both negatively correlated with the other seven hub genes. Moreover, several meaningful results were detected through the immune infiltration landscape. Finally, we used molecular docking to screen potential therapeutic compounds of pSS based on the hub genes. We found that the small molecules DB08006, DB08036, and DB15308 had good docking scores with ELAVL1 and IGF1R simultaneously. Our study might provide effective diagnostic biomarkers and new therapeutic ideas for pSS.