Bioinformatics mining of ferroptosis-related biomarkers and development of a diagnostic model in primary Sjögren's syndrome
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摘要:目的
基于多种生物信息学手段识别原发性干燥综合征(pSS)中与铁死亡相关的基因并构建诊断模型, 为pSS的诊断和治疗提供潜在靶点。
方法从GEO数据库中获得pSS的基因表达矩阵，应用R软件limma包识别出差异表达基因(DEGs)。通过加权基因共表达网络分析(WGCNA)识别pSS中最相关的模块化基因，从FerrDb数据库获得铁死亡相关基因集，将pSS中最相关的模块基因与DEGs和铁死亡相关基因取交集识别出关键基因。通过2种机器学习算法去除冗余基因并构建由关键基因组成的诊断模型，在3个独立数据集中验证诊断模型的准确性，基于免疫浸润分析揭示关键基因与免疫细胞的关联，应用Seurat软件包分析单细胞数据集。
结果差异基因分析共获得265个DEGs, WGCNA获得1个与pSS最为相关的模块(r=0.44, P < 0.01), 从中识别8个铁死亡相关基因，去除冗余基因后，最终获得3个与铁死亡相关的关键基因(PARP9、PARP12、PARP14)。基于3个关键基因建立诊断模型，该模型在3个独立数据集中均表现出优秀的诊断效能(受试者工作特征曲线中，曲线下面积分别为0.848、0.853、1.000)。免疫浸润分析和单细胞数据集分析结果提示, 3个关键基因与树突状细胞、巨噬细胞和T细胞或B细胞存在显著关联。
结论本研究基于生物信息学手段识别出pSS中与铁死亡相关的3个关键基因(PARP9、PARP12、PARP14)并构建pSS诊断模型，为pSS的诊断提供了新的潜在工具。
Abstract:ObjectiveTo identify iron death-related genes in primary Sjögren's syndrome (pSS) through various bioinformatics methods, providing potential targets for the diagnosis and treatment of pSS.
MethodsGene expression matrix of pSS was obtained from the GEO database, and differentially expressed genes (DEGs) were identified using the "limma" package by R software. Weighted gene co-expression network analysis (WGCNA) was used to identify the most relevant modular genes in pSS. Iron death-related gene sets were obtained from the FerrDb database, and key genes were identified by intersecting the most relevant module genes with DEGs and iron death-related genes. Two machine learning algorithms were used to remove redundant genes and construct a diagnostic model consisting of key genes. The accuracy of the diagnostic model was validated in three independent datasets, and the association between key genes and immune cells was revealed by immune infiltration analysis and single-cell data analysis using the Seurat package.
ResultsA total of 265 DEGs were obtained by differential gene analysis, and the WGCNA algorithm identified a module that was most strongly correlated with pSS (r=0.44, P < 0.01), from which eight iron death-related genes were identified. After removing redundant genes, three key genes related to iron death (PARP9, PARP12, and PARP14) were finally obtained, and a diagnostic model was established based on the three genes. The model exhibited excellent diagnostic performance in three independent datasets (the areas under the receiver operating characteristic curve were 0.848, 0.853 and 1.000, respectively). Immune infiltration analysis and single-cell data analysis revealed significant associations of the three key genes with dendritic cells, macrophages and T/B cells.
ConclusionIn this study, three key genes related to iron death in pSS (PARP9, PARP12, PARP14) are identified based on bioinformatics methods and a diagnostic model of pSS is constructed, providing a new potential tool for the diagnosis of pSS.
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图 1 pSS相关DEGs的功能分析结果
A: 去除批次效应前UMAP图; B: 去除批次效应后UMAP图; C: DEGs的GO富集分析结果; D: DEGs的KEGG通路富集分析结果。
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图 2 基于WGCNA鉴定与pSS相关的模块
A、B: 基于比例独立性、平均连接分析选择β=4作为软阈值构建网络; C: 基因树显示不同基因共表达模块(各模块以不同的唯一颜色表示); D: 热图显示基因模块与pSS的相关性(红色模块与pSS显著相关); E: 红色模块内基因成员身份与基因重要性的相关性分析散点图; F: 红色模块基因、铁死亡相关基因、DEGs取交集的韦恩图。
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图 3 基于机器学习算法鉴定与铁死亡相关的关键基因
A: 基于LASSO模型筛选pSS诊断生物标志物(最佳基因数为4个，见曲线最低点); B: 基于SVM-RFE算法的关键基因图; C、D、E、F: 训练集中PARP9、PARP12、PARP14、TRIM21诊断pSS的ROC曲线。
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图 4 诊断模型的验证结果
A: 验证集GSE84844中3个关键基因的表达情况; B: 验证集GSE208260中3个关键基因的表达情况; C: 验证集GSE48378中3个关键基因的表达情况; D、E、F: 在GSE84844、GSE208260、GSE48378数据集中进行的诊断模型分类; G、H、I: 诊断模型在GSE84844、GSE208260 GSE48378数据集中的ROC曲线。图A、B、C中，两者比较, **P < 0.01, ***P < 0.001, ****P < 0.000 1。
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图 5 免疫细胞浸润分析
A: 每个样本中22种免疫细胞的分布直方图; B: 健康人和pSS患者之间22种免疫细胞的相关性分析结果(两者比较, **P < 0.01, ****P < 0.000 1, ns P>0.05); C: pSS组和非pSS组的免疫浸润箱线图; D: 3个关键基因与免疫细胞及免疫功能的相关性分析结果。
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