Uncovering potential new biomarkers and immune infiltration characteristics in primary Sojgren's syndrome by integrated bioinformatics analysis

Abstract

Background: Primary Sojgren's syndrome (pSS) is known as autoimmune disease of endocrine system. This study aimed to identify potential biomarkers for pSS using integrated bioinformatics analysis and explore the relationship between differentially expressed genes (DEGs) and immune infiltration. Methods: Three pSS datasets (GSE7451, GSE23117 and GSE40611) from the Gene Expression Omnibus (GEO) database were integrated. All the datasets were processed by affy in R (version 4.0.3).Results: A total of 16 immune cells and 13 immune functions infiltration scores were obtained. The top immune cell and immune function were "activated" dendritic cells (aDCs) and major histocompatibility complex (MHC) class I. Correlation analysis showed the top correlation among 16 immune cells were B cells and tumor infiltrating lymphocytes (TIL), check-point and T cell co-stimulation, respectively. In comparisons of immune score, aDCs (0.657 vs 0.594, P < 0.001), B cells (0.492 vs 0.434, P = 0.004), macrophages (0.631 vs 0.601, P = 0.010), inflammation-promoting (0.545 vs 0.478, P < 0.001), Type I interferon (IFN) Reponse (0.728 vs 0.625, P < 0.001) and so on were higher in pSS than control group. In correlation analysis, the up-regulation of IFIT1 gene was strongly correlated with Type I IFN Reponse with a correlation coefficient of 0.87. The ROC curve of 5 genes showed that the area under curve (AUC) was 0.891. In the verification model, the AUC was 0.881. In addition, DO analysis supported the association between DEGs and pSS. Conclusions: In summary, pSS has a variety of DEGs in immune infiltration, which is worthy of the attention from clinicians.