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Role of IFIT1 and IFIT3 in systemic lupus erythematosus: modeling a diagnosis and exploring immune regulation
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DOI：10.1007/s10238-026-02265-6.png)
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Abstract
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Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by multi-organ involvement and a protracted clinical course. Current diagnostic strategies, which rely heavily on clinical symptoms and serology, are often insufficient for early detection. Therefore, highly accurate diagnostic biomarkers are urgently needed to facilitate early intervention and optimize personalized treatment strategies. D atasets GSE61635 and GSE135779 were integrated to identify differentially expressed genes. Weighted gene co-expression network analysis (WGCNA) was performed to isolate the module with the strongest clinical relevance. Mendelian randomization and single‑cell RNA‑seq were used to identify key disease‑relevant genes. A diagnostic model was then constructed, and gene set variation analysis (GSVA), along with gene set enrichment analysis (GSEA), was conducted to elucidate the underlying molecular pathways. IFIT1 and IFIT3 were identified as 2 core genes highly expressed in monocytes and T cells of SLE patients. Functional enrichment analysis revealed that these genes were enriched in immune-related pathways, metabolic pathways related to inflammation and genomic stability. The diagnostic model showed good accuracy, with an area under the curve (AUC) of 0.974 on the training set and 0.912 on the validation set. IFIT1 and IFIT3 represent promising biomarkers for diagnosing SLE and appear to mediate key immune and metabolic disturbances. Furthermore, the developed model serves as an accurate and reliable instrument for early diagnosis and personalized therapy. Large-scale clinical studies are warranted to further validate these findings and evaluate their clinical application.
Keywords:
Systemic lupus erythematosus
Single-cell sequencing
Mendelian randomization
Expression quantitative trait loci
Weighted gene co-expression network analysis
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