Bioinformatics Analysis for Identification of Key Genes in Salivary Gland and the Potential of a Combination of Biomarkers for the Diagnosis of SS.

Abstract

PurposeSjögren’s syndrome (SS) is a systemic autoimmune disease mainly characterized by dysfunction of exocrine glands. Studies on diagnosis models specific for SS patients are very limited. We aimed to use gene expression datasets from salivary glands to identify aberrant differentially expressed genes (DEGs) and pathways by bioinformatics and validate candidate genes by clinical minor labial gland biopsy (MSGB) samples, and finally build a combined gene quantitative diagnosis model of SS.Patients and MethodsOriginal datasets GSE23117, GSE7451, and GSE127952 were obtained from the Gene Expression Omnibus database (GEO) and integrated and analyzed for differentially expressed genes in SS salivary glands. ClueGO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of upregulated and downregulated DEGs were performed, and protein–protein interaction (PPI) networks were constructed using the STRING and Cytoscape database. H&E staining and immunohistochemistry were used to validate the expression levels of four hub genes in salivary glands. Finally, a receiver operating characteristic (ROC) curve of the combined diagnosis of four hub genes was analyzed in SS patients and non-SS patients in order to explore the diagnostic efficacy of these genes compared with conventional FS in SS.ResultsFifty-three upregulated genes and fifteen downregulated genes were identified. We analyzed the expression and function of four hub genes via H&E, immunohistochemistry, and ROC analysis. We then evaluated and verified the diagnosis value of four hub genes, STAT1, MNDA, IL10RA, and CCR1 in MSGB of SS and non-SS. A combined diagnosis model of four indicators was established to identify patients’ discrete data on the foci size (AUC=0.915).ConclusionThe expression of STAT1, MNDA, and IL10RA may be potential biological indicators for SS diagnosis. Compared with FS, a combined diagnosis model of quantitative gene expression could potentially contribute to improving the sensitivity and specificity of MSGB of SS.