Uncovering CD248, MMP28, and SLC16A10 in primary Sjögren's disease: a machine learning-driven SHAP approach for CD4+ T cell-associated biomarker discovery

Our bioinformatic study identifies CD248, MMP28 and SLC16A10 as candidate biomarkers and therapeutic targets for SjD, with their dysregulation specifically enriched in CD4+ T cell subsets, unveiling a previously underappreciated mechanism in SjD pathogenesis. naive and memory CD4+ T cells emerge as key contributors to inflammatory cascades, with azathioprine, leflunomide, methotrexate, hydroxychloroquine, iguratimod, pilocarpine, and cevimeline predicted to bind potently to these targets. This integrative multi-omics framework, combining machine learning, SHAP, and molecular docking, presents a promising approach for autoimmune disease diagnostics and early therapeutic intervention, although future experimental validation is essential to confirm its translational potential.