Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning

Objectives: EBV is a widespread virus implicated in various diseases, including SLE. However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients with EBV infection. Methods: The datasets of SLE (GSE50772 and GSE81622) and EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analyses were conducted to identify overlapping DEGs, and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using receiver operating characteristic (ROC) curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analysed using CIBERSORTx, and clinical data were reviewed for SLE patients. Results: We identified 58 overlapping DEGs enriched in IFN-related pathways. Five overlapping DEGs ( IFI27 , TXK, RAPGEF6, PIK3IP1 and PSENEN) were selected as key genes by machine-learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27was found to be higher in CD4 CTL, CD8-na & iuml;ve and various B cell subsets of SLE patients with EBV infection. I FI27 showed significant correlation with B intermediate and CD4 CTL cells. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection. Conclusion: This study identified IFI27as a key gene for SLE patients with EBV infection, influencing CD4 CTL and B cell subtypes. These findings enhance the understanding of the molecular mechanisms linking SLE and EBV infection, providing potential targets for diagnostic and therapeutic strategies.