Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems. In SLE, T-cell subsets are closely associated with autoimmunity and organ inflammation. Detecting T-cell exhaustion (TEX)-related biomarkers is crucial for early SLE diagnosis and offers new insights into disease mechanisms. Transcriptomic data for SLE were collected from public databases, and TEX-related genes were obtained from the literature. Biomarkers were identified using analyses such as differential expression analysis and machine learning algorithms. The potential mechanisms of action of these biomarkers were further investigated, including nomogram construction, gene set enrichment analysis, immune microenvironment analysis, molecular regulatory network analysis, molecular docking, and reverse transcription-quantitative polymerase chain reaction validation. IFIT3, IFIH1, CXCL10, and STAT2 were identified as biomarkers. The nomogram demonstrated good predictive performance. These biomarkers were mainly involved in biological processes such as ribosome biogenesis and negative regulation of viral genome replication. Four significantly different immune cell types were identified, including monocytes and resting memory CD4 T cells. The biomarkers were found to be regulated by several transcription factors and microRNAs. Furthermore, the 4 biomarkers exhibited high binding affinity for several drugs; in particular, the binding energy of STAT2 with bisphenol A was -7.0 kcal/mol. Finally, reverse transcription-quantitative polymerase chain reaction results showed that all 4 biomarkers were significantly upregulated in SLE patients. In summary, this study identified TEX-related biomarkers in SLE, which may have important clinical implications for the diagnosis and treatment of SLE.