Integrative Bioinformatics and Machine Learning Identify Novel Diagnostic Biomarkers and Molecular Mechanisms in Sjögren's Syndrome.

Background: Sjögren's syndrome (SS) is a chronic autoimmune disorder characterized by significant diagnostic challenges due to nonspecific symptoms and a lack of reliable biomarkers, often resulting in delayed diagnosis and suboptimal patient management. Objective: This study is aimed at identifying novel diagnostic biomarkers and elucidating the molecular mechanisms underlying SS pathogenesis through integrative bioinformatics and machine learning approaches. Methods: We analyzed three peripheral blood transcriptomic datasets (GSE51092, GSE66795, and GSE84844) comprising a total of 351 SS patients and 91 healthy controls. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and 12 machine learning algorithms were employed to identify robust diagnostic biomarkers. Immune cell infiltration was assessed using CIBERSORT, and single-cell RNA sequencing data (GSE157278) were analyzed to validate cell-type-specific expression patterns. Drug repurposing analysis was conducted using the L1000FWD platform. Results: We identified 12 hub genes (EPSTI1, IFIH1, CXCL10, TNFSF10, GBP5, PARP9, IFI44, LAP3, IFIT2, IFI44L, PARP12, and OAS1) with exceptional diagnostic performance (AUC = 0.994 in training, 0.838 in internal validation, and 0.825 in external validation). These biomarkers showed significant correlations with clinical indicators including ANA, Ro/SSA, and La/SSB ( Conclusion: Our integrative approach identifies 12 robust diagnostic biomarkers for SS, offering new insights into disease mechanisms and highlighting potential therapeutic targets for this challenging autoimmune disorder.