ArticleGenetics research2026
Identification of Genetic Diagnostic Markers for Systemic Lupus Erythematosus.
Article in Genetics research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Identification of Genetic Diagnostic Markers for Systemic Lupus Erythematosus.Genetics research · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystemic lupus erythematosus (SLE) is a complex and heterogeneous systemic autoimmune disease associated with poor treatment outcomes. While previous studies have indicated a genetic predisposition to SLE, the underlying mechanisms remain poorly understood.
objectiveThis study aimed to identify diagnostic targets with potential genetic associations to SLE by leveraging bioinformatics and the Mendelian randomization (MR) approach.
methodsSix datasets (GSE30153, GSE39088, GSE50635, GSE50772, GSE61635, and GSE110169) were obtained from the GEO database for differential expression analysis to identify differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was then performed, and the most relevant module was intersected with the DEGs to identify candidate genes with potential diagnostic value. Subsequently, machine learning algorithms were applied to screen diagnostic genes, and their performance was evaluated using receiver operating characteristic (ROC) curves and confusion matrices. MR analysis was conducted to identify diagnostic genes with genetic associations. A protein-protein interaction (PPI) network was constructed to identify core genes. Finally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed.
resultsDifferential expression analysis identified 244 DEGs, and WGCNA revealed a highly relevant module. Intersecting this module with the DEGs produced 136 candidate genes. Machine learning algorithms and MR analysis further refined the selection, identifying five diagnostic genes: GBP1, IFI6, KLHDC8B, OAS3, and ZCCHC2, all of which were shown to be well-aligned with their respective drugs. The PPI network highlighted GBP1, IFI6, and OAS3 as core genes, which showed significant correlations with immune cell infiltration.
conclusionsOur study identified GBP1, IFI6, and OAS3 as core genes implicated in SLE pathogenesis, providing novel insights into its molecular mechanisms and potential therapeutic targets.
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