CD48, CD69, and TIGIT as diagnostic biomarkers for primary Sjögren’s syndrome: an integrated machine learning and multi-disease discrimination validation study

Background: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disorder. However, current diagnostic methods remain limited, necessitating the exploration of non-invasive diagnostic markers with higher specificity. Methods: This study integrated two GEO expression datasets to identify differentially expressed genes (DEGs) specific to pSS (distinct from SLE) and applied LASSO, XGBoost, RF, and SVM-RFE algorithms to screen candidate genes. Correlation and interaction network analyses were performed, followed by construction and validation of a diagnostic nomogram. The model's differential diagnostic ability was validated in IgG4-RD, RA, SLE, and SSc cohorts. Additionally, candidate genes and the diagnostic model were experimentally validated using RT-qPCR in clinical samples. Results: ) were identified, showing significant upregulation in pSS (individual AUC > 0.80). The combined diagnostic model achieved an AUC of 0.924, with AUC > 0.90 in validation sets, efficiently distinguishing pSS from IgG4-RD, RA, SLE, and SSc. RT-qPCR confirmed their high expression in pSS, with the model yielding AUC 0.875 (accuracy/precision > 0.85). Notably, combining these candidate genes with erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) yielded an AUC of 0.876 and a specificity of 83.3%, outperforming conventional markers such as ANA, anti-SSA, and anti-SSB antibodies. Conclusions: were identified as potential diagnostic markers for pSS. The combined model significantly enhanced diagnostic accuracy and effectively differentiated pSS from other autoimmune conditions. Integration with ESR/CRP substantially improved specificity compared to conventional serological markers.