Identification of Genetic Diagnostic Markers for Systemic Lupus Erythematosus

Systemic 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.

This study aimed to identify diagnostic targets with potential genetic associations to SLE by leveraging bioinformatics and the Mendelian randomization (MR) approach.

Six 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.

Differential 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.

Our study identified GBP1, IFI6, and OAS3 as core genes implicated in SLE pathogenesis, providing novel insights into its molecular mechanisms and potential therapeutic targets.