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

Abstract

Epstein-Barr Virus (EBV) is a widespread virus implicated in various diseases, including Systemic Lupus Erythematosus (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. The datasets of SLE (GSE50772 and GSE81622) or EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analysis were conducted to identify overlapping DEGs and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using ROC curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analyzed using CIBERSORTx, and clinical data were reviewed for SLE patients. We identified 58 overlapping DEGs enriched in interferon-related pathways. Five overlapping DEGs (IFI27, TXK, RAPGEF6, PIK3IP1, PSENEN) were selected as key genes by machine learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27 was found higher in CD4 CTL, CD8 naïve and various B cell subsets of SLE patients with EBV infection. IFI27 showed significant correlation with B intermediate and CD4 CT. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection. This study identifies IFI27 as 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.