Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease with heterogeneous clinical manifestations. Understanding the molecular mechanisms of SLE is crucial for developing effective therapeutic strategies. This study downloaded microarray datasets from the Gene Expression Omnibus (GEO) database. Single-cell RNA sequencing (scRNA-seq) data was processed to identify 19 clusters and annotated five major cell types. Then we calculated mitochondrial-related genes (MRGs) and ferroptosis-related genes (FRGs) scores. FRGs scored the highest in Megakaryocytes, while MRGs scored the highest in B cells. By employing pseudotime analysis, cell-cell communication analysis, and Single-Cell Regulatory Network Inference and Clustering (SCENIC) analysis, we explored the heterogeneity of cells in SLE. Hub genes were identified using high-dimensional weighted correlation network analysis (hdWGNCA) and machine learning algorithms, leading to the development of a predictive diagnostic model with high predictive accuracy. Immune infiltration analysis revealed significant correlations between diagnostic biomarkers and various immune cells. Lastly, molecular docking studies suggested Doxorubicin may exert therapeutic effects by affecting these diagnostic biomarkers. This study offers new insights into the pathogenesis of SLE and provide valuable directions for future therapeutic research.