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基于孟德尔随机化、单细胞RNA测序和多种机器学习方法探索系统性红斑狼疮中谷胱甘肽代谢的关键基因

Exploring Key Genes of Glutathione Metabolism in Systemic Lupus Erythematosus Based on Mendelian Randomisation, Single-Cell RNA Sequencing and Multiple Machine Learning Approaches.

作者信息

Wang Kejiang, Li Xiaoqiong, Tang Ying, Zhao Lizhou

机构信息

The Department of Experimental Medicine, Meishan City People's Hospital, Meishan, China.

Department of Clinical Laboratory, Meishan Traditional Chinese Medicine Hospital, Meishan, China.

出版信息

IET Syst Biol. 2025 Jan-Dec;19(1):e70021. doi: 10.1049/syb2.70021.

Abstract

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterised by immune dysregulation leading to inflammation and organ damage. Despite the rising global incidence of SLE, its aetiology remains unclear. We applied Mendelian randomisation (MR), multi-omics integration, machine learning (ML), and SHAP to identify key metabolites and genes associated with SLE, revealing the crucial role of the glutathione pathway. MR analysis was performed on 1400 serum metabolites, revealing significant enrichment in the glutathione metabolic pathway. Single-cell RNA sequencing (scRNA-seq) data classified monocytes into Metabolism_high and Metabolism_low groups based on glutathione metabolism scores. Differentially expressed genes were analysed using GSEA, metabolic pathway activity assessment, transcription factor prediction, cellular communication analysis, and Pseudotime analysis. LASSO regression identified hub genes and machine learning models (CatBoost, XGBoost, NGBoost) were developed. The SHAP method was used to interpret these models. Expression of key genes was validated across multiple datasets. MR analysis confirmed that metabolites were enriched in the glutathione pathway, identifying nine hub genes. Machine learning models achieved AUCs of 0.85, 0.80, and 0.83 in the validation set. SHAP analysis highlighted LAP3 as the top contributing gene across all models. scRNA-seq data showed that LAP3 plays a significant role in the immune microenvironment of SLE. Validation across multiple datasets (training, validation, and GSE112087) revealed elevated LAP3 expression in PBMCs of SLE patients, with AUCs of 0.935, 0.795, and 0.817, respectively, suggesting strong diagnostic potential. Glutathione metabolism is closely associated with SLE development and LAP3 may play a key role in its progression. Both glutathione metabolism and LAP3 could serve as potential targets for SLE diagnosis and treatment.

摘要

系统性红斑狼疮(SLE)是一种复杂的自身免疫性疾病,其特征是免疫失调导致炎症和器官损伤。尽管全球SLE发病率不断上升,但其病因仍不清楚。我们应用孟德尔随机化(MR)、多组学整合、机器学习(ML)和SHAP来识别与SLE相关的关键代谢物和基因,揭示了谷胱甘肽途径的关键作用。对1400种血清代谢物进行了MR分析,结果显示谷胱甘肽代谢途径有显著富集。单细胞RNA测序(scRNA-seq)数据根据谷胱甘肽代谢评分将单核细胞分为代谢高组和代谢低组。使用基因集富集分析(GSEA)、代谢途径活性评估、转录因子预测、细胞通讯分析和伪时间分析对差异表达基因进行了分析。套索回归确定了枢纽基因,并开发了机器学习模型(CatBoost、XGBoost、NGBoost)。使用SHAP方法对这些模型进行了解释。在多个数据集中对关键基因的表达进行了验证。MR分析证实代谢物在谷胱甘肽途径中富集,确定了9个枢纽基因。机器学习模型在验证集中的曲线下面积(AUC)分别为0.85、0.80和0.83。SHAP分析突出显示LAP3是所有模型中贡献最大的基因。scRNA-seq数据表明LAP3在SLE的免疫微环境中起重要作用。在多个数据集(训练集、验证集和GSE112087)中的验证显示,SLE患者外周血单核细胞(PBMC)中LAP3表达升高,AUC分别为0.935、0.795和0.817,表明其具有很强的诊断潜力。谷胱甘肽代谢与SLE的发展密切相关,LAP3可能在其进展中起关键作用。谷胱甘肽代谢和LAP3都可作为SLE诊断和治疗的潜在靶点。

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