{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T19:38:19Z","timestamp":1790105899153,"version":"4.0.1"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012547","name":"Natural Science Foundation of Guangxi Zhuang Autonomous Region","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Primary Sj\u00f6gren\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three candidate genes (\n                      <jats:italic>CD48<\/jats:italic>\n                      ,\n                      <jats:italic>CD69<\/jats:italic>\n                      , and\n                      <jats:italic>TIGIT<\/jats:italic>\n                      ) were identified, showing significant upregulation in pSS (individual AUC &amp;gt; 0.80). The combined diagnostic model achieved an AUC of 0.924, with AUC &amp;gt; 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 &amp;gt; 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      <jats:italic>CD48<\/jats:italic>\n                      ,\n                      <jats:italic>CD69<\/jats:italic>\n                      , and\n                      <jats:italic>TIGIT<\/jats:italic>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1700831","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T05:17:35Z","timestamp":1765862255000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["CD48, CD69, and TIGIT as diagnostic biomarkers for primary Sj\u00f6gren\u2019s syndrome: an integrated machine learning and multi-disease discrimination validation 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