{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T22:24:56Z","timestamp":1793658296815,"version":"4.5.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      This study explores the prognostic value of the\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      gene in systemic lupus erythematosus (SLE) through bioinformatics analysis and evaluates its role in disease diagnosis and progression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Gene expression datasets related to SLE (GSE17755, GSE50772, and GSE61635) were obtained from the GEO (Gene Expression Omnibus) database. Intersection analysis was performed using the Jvenn tool with a screening threshold of |log2FC| &amp;gt; 1 and P&amp;lt; 0.05 to identify differentially expressed genes (DEGs). The resulting DEGs were then cross-referenced with immune-related genes in the GeneCards database (relevance score &amp;gt; 8) to further prioritize candidates with immunological relevance. Peripheral blood from 360 SLE patients and 360 healthy controls was collected for\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      expression analysis via RT-qPCR. Disease activity was evaluated using the SLEDAI score, and patients were grouped accordingly. Pearson and Spearman correlation analysis to investigate the relationship between\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      and immune indicators. Logistic regression and\n                      <jats:italic>ROC<\/jats:italic>\n                      analyses were conducted to assess diagnostic and prognostic value. Kaplan-Meier analysis evaluated survival outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Bioinformatics analysis identified six SLE-related DEGs, namely\n                      <jats:italic>ISG15<\/jats:italic>\n                      ,\n                      <jats:italic>HERC5<\/jats:italic>\n                      ,\n                      <jats:italic>TNFAIP6<\/jats:italic>\n                      ,\n                      <jats:italic>IFIT3<\/jats:italic>\n                      ,\n                      <jats:italic>OASL<\/jats:italic>\n                      , and\n                      <jats:italic>CLEC4E.<\/jats:italic>\n                      Further intersection with immune-related genes from the GeneCards database (relevance score &amp;gt; 8) ultimately highlighted\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      as the key gene for clinical validation. The expression level of\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      was significantly higher in SLE patients compared with healthy controls.\n                      <jats:italic>ROC<\/jats:italic>\n                      analysis showed good diagnostic performance (AUC = 0.7744).\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      expression was higher in active SLE, and multivariate analysis identified\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      , C3, C4, ANA, and anti-dsDNA as independent predictors of disease activity.\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      demonstrated moderate diagnostic value for distinguishing active from inactive disease (AUC = 0.6360). Higher\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      expression was associated with worse prognosis (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.0002). The combined diagnostic performance with other biomarkers (C3, C4, ANA, anti-dsDNA) showed a remarkable AUC of 0.9407.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      <jats:italic>CLEC4E<\/jats:italic>\n                      is a potential biomarker for SLE diagnosis, disease activity assessment, and prognosis evaluation.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1617878","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T05:36:26Z","timestamp":1759210586000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["CLEC4E as a molecular biomarker in systemic lupus erythematosus: integrating bioinformatics and clinical data to assess its prognostic 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