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Abstract：
OBJECTIVE: To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms.
METHODS: The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).
RESULTS: We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.
CONCLUSIONS: CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.
METHODS: The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).
RESULTS: We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.
CONCLUSIONS: CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.
摘要：
目的：鉴定类风湿关节炎(RA)早期诊断的生物标志物并探讨其可能的免疫调节机制。
方法：对RA的差异表达基因进行筛选，并使用limma进行功能注释，RRA,批量校正，和clusterProfiler。从STRING数据库检索蛋白质-蛋白质相互作用网络，利用Cytoscape3.8.0和GeneMANIA选择关键基因并预测其相互作用机制。ROC曲线用于验证基于关键基因的诊断模型的准确性。通过机器学习选择疾病特异性免疫细胞，并使用Corplot软件包分析其与关键基因的相关性。使用GSEA方法探索关键基因的生物学功能。观察STAT1在胶原诱导性关节炎(CIA)大鼠滑膜组织中的表达。
结果：我们在RA中确定了9个核心关键基因（CD3G，CD8A,SYK,LCK,IL2RG,STAT1、CCR5、ITGB2和ITGAL)，主要通过细胞因子相关途径调节滑膜炎症。ROC曲线分析显示9个核心基因的预测准确率较高,其中STAT1的AUC最高(0.909)。相关分析显示CD3G具有很强的相关性，ITGAL,LCK,CD8A,和STAT1与疾病特异性免疫细胞，STAT1与M1型巨噬细胞的相关性最强(R=0.68,P=2.9e-08)。CIA大鼠踝关节滑膜组织显示STAT1和p-STAT1的高表达，滑膜成纤维细胞的细胞核和细胞质之间STAT1的表达显着差异。治疗后细胞核中p-STAT1和STAT1的蛋白表达显著降低。
结论：CD3G，CD8A,SYK,LCK,IL2RG,STAT1、CCR5、ITGB2和ITGAL可作为RA早期诊断的生物标志物。基因免疫细胞通路，如CD3G/CD8A/LCK-γδT细胞，ITGAL-Tfh细胞,STAT1-M1型巨噬细胞可能与RA的发生发展密切相关。
方法：对RA的差异表达基因进行筛选，并使用limma进行功能注释，RRA,批量校正，和clusterProfiler。从STRING数据库检索蛋白质-蛋白质相互作用网络，利用Cytoscape3.8.0和GeneMANIA选择关键基因并预测其相互作用机制。ROC曲线用于验证基于关键基因的诊断模型的准确性。通过机器学习选择疾病特异性免疫细胞，并使用Corplot软件包分析其与关键基因的相关性。使用GSEA方法探索关键基因的生物学功能。观察STAT1在胶原诱导性关节炎(CIA)大鼠滑膜组织中的表达。
结果：我们在RA中确定了9个核心关键基因（CD3G，CD8A,SYK,LCK,IL2RG,STAT1、CCR5、ITGB2和ITGAL)，主要通过细胞因子相关途径调节滑膜炎症。ROC曲线分析显示9个核心基因的预测准确率较高,其中STAT1的AUC最高(0.909)。相关分析显示CD3G具有很强的相关性，ITGAL,LCK,CD8A,和STAT1与疾病特异性免疫细胞，STAT1与M1型巨噬细胞的相关性最强(R=0.68,P=2.9e-08)。CIA大鼠踝关节滑膜组织显示STAT1和p-STAT1的高表达，滑膜成纤维细胞的细胞核和细胞质之间STAT1的表达显着差异。治疗后细胞核中p-STAT1和STAT1的蛋白表达显著降低。
结论：CD3G，CD8A,SYK,LCK,IL2RG,STAT1、CCR5、ITGB2和ITGAL可作为RA早期诊断的生物标志物。基因免疫细胞通路，如CD3G/CD8A/LCK-γδT细胞，ITGAL-Tfh细胞,STAT1-M1型巨噬细胞可能与RA的发生发展密切相关。
