Zhang Su, Hu Weitao, Tang Yuchao, Lin Hongjie, Chen Xiaoqing
Department of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Department of Gastroenterology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Front Med (Lausanne). 2025 May 14;12:1557307. doi: 10.3389/fmed.2025.1557307. eCollection 2025.
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple systems. SLE is characterized by the production of autoantibodies and inflammatory tissue damage. This study further explored the role of immune-related genes in SLE. We downloaded the expression profiles of GSE50772 using the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) in SLE. The DEGs were also analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The gene modules most closely associated with SLE were then derived by Weighted Gene Co-expression Network Analysis (WGCNA). Differentially expressed immune-related genes (DE-IRGs) in SLE were obtained by DEGs, key gene modules and IRGs. The protein-protein interaction (PPI) network was constructed through the STRING database. Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. Then, we constructed a diagnostic model. The model was validated by external cohort GSE61635 and peripheral blood mononuclear cells (PBMC) from SLE patients. Immune cell abundance assessment was achieved by CIBERSORT. The hub DE-IRGs and miRNA networks were made accessible through the NetworkAnalyst database. We screened 945 DEGs, which are closely related to the type I interferon pathway and NOD-like receptor signaling pathway. Machine learning identified a total of five hub DE-IRGs (, , , , ), and validated in GSE61635 and PBMC from SLE patients. Immune cell abundance analysis showed that the hub genes may be involved in the development of SLE by regulating immune cells (especially neutrophils). In this study, we identified five hub DE-IRGs in SLE and constructed an effective predictive model. These hub genes are closely associated with immune cell in SLE. These may provide new insights into the immune-related pathogenesis of SLE.
系统性红斑狼疮（SLE）是一种累及多个系统的慢性自身免疫性疾病。SLE的特征是自身抗体的产生和炎症性组织损伤。本研究进一步探讨了免疫相关基因在SLE中的作用。我们使用基因表达综合数据库（GEO）下载了GSE50772的表达谱，以获取SLE中差异表达基因（DEG）。还对DEG进行了基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。然后通过加权基因共表达网络分析（WGCNA）得出与SLE最密切相关的基因模块。通过DEG、关键基因模块和免疫相关基因（IRG）获得SLE中差异表达的免疫相关基因（DE-IRG）。通过STRING数据库构建蛋白质-蛋白质相互作用（PPI）网络。将三种机器学习算法应用于DE-IRG以筛选枢纽DE-IRG。然后，我们构建了一个诊断模型。该模型通过外部队列GSE61635和SLE患者的外周血单个核细胞（PBMC）进行验证。通过CIBERSORT实现免疫细胞丰度评估。枢纽DE-IRG和miRNA网络可通过NetworkAnalyst数据库获取。我们筛选出945个与I型干扰素途径和NOD样受体信号通路密切相关的DEG。机器学习共鉴定出5个枢纽DE-IRG（，，，，），并在GSE61635和SLE患者的PBMC中进行了验证。免疫细胞丰度分析表明，枢纽基因可能通过调节免疫细胞（尤其是中性粒细胞）参与SLE的发展。在本研究中，我们在SLE中鉴定出5个枢纽DE-IRG并构建了一个有效的预测模型。这些枢纽基因与SLE中的免疫细胞密切相关。这些可能为SLE的免疫相关发病机制提供新的见解。
