Dai Yunfeng, Liu Jianwen, Lai Yongxing, Gao Fei, Lin He, Zhang Li, Chen Zhihan
Fujian Medical University Shengli Clinical Medical College, Fuzhou, 350000, China.
Department of Rheumatology, Fuzhou University Affiliated Provincial Hospital, No.134 Dongjie, Fuzhou, 350000, China.
Sci Rep. 2025 Mar 17;15(1):9140. doi: 10.1038/s41598-025-93872-y.
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.
系统性红斑狼疮（SLE）是一种临床表现异质性的复杂自身免疫性疾病。了解SLE的分子机制对于制定有效的治疗策略至关重要。本研究从基因表达综合数据库（GEO）下载了微阵列数据集。对单细胞RNA测序（scRNA-seq）数据进行处理，以识别19个细胞簇并注释了五种主要细胞类型。然后我们计算了线粒体相关基因（MRGs）和铁死亡相关基因（FRGs）的评分。FRGs在巨核细胞中得分最高，而MRGs在B细胞中得分最高。通过采用伪时间分析、细胞间通讯分析和单细胞调控网络推断与聚类（SCENIC）分析，我们探索了SLE中细胞的异质性。使用高维加权相关网络分析（hdWGNCA）和机器学习算法鉴定了枢纽基因，从而开发出具有高预测准确性的预测诊断模型。免疫浸润分析揭示了诊断生物标志物与各种免疫细胞之间的显著相关性。最后，分子对接研究表明阿霉素可能通过影响这些诊断生物标志物发挥治疗作用。本研究为SLE的发病机制提供了新见解，并为未来的治疗研究提供了有价值的方向。
