类风湿关节炎的潜在生物标志物及其免疫调控机制：基于GEO数据库

doi:10.12122/j.issn.1673-4254.2024.06.10

南方医科大学学报 ›› 2024, Vol. 44 ›› Issue (6): 1098-1108.doi: 10.12122/j.issn.1673-4254.2024.06.10

• • 上一篇

类风湿关节炎的潜在生物标志物及其免疫调控机制：基于GEO数据库

陈莉莉¹(), 吴天宇², 张铭³^,⁵, 丁子夏⁴, 张妍⁴, 杨依清⁴, 郑佳倩⁴, 张小楠³()

^1.蚌埠医科大学，卫生管理学院，安徽蚌埠，233030
^2.蚌埠医科大学，公共卫生学院，安徽蚌埠，233030
^3.蚌埠医科大学，心脑血管疾病基础与临床重点实验室，安徽蚌埠，233030
^4.蚌埠医科大学，临床医学院，安徽蚌埠，233030
^5.山东第一医科大学运动医学与康复学院，山东泰安 271016

收稿日期:2023-10-30 出版日期:2024-06-20 发布日期:2024-07-01
通讯作者: 张小楠 E-mail:clily666666@163.com;zhangxn@bbmc.edu.cn
作者简介:陈莉莉，在读本科生，E-mail: clily666666@163.com
基金资助:
国家级大学生创新训练项目(202310367024)

Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database

Lili CHEN¹(), Tianyu WU², Ming ZHANG³^,⁵, Zixia DING⁴, Yan ZHANG⁴, Yiqing YANG⁴, Jiaqian ZHENG⁴, Xiaonan ZHANG³()

^1.School of Health Management,
^2.School of Public Health,
^3.Key Laboratory of Cardiovascular and Cerebrovascular Diseases,
^4.College of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China
^5.School of Sports Medicine and Rehabilitation, Shandong First Medical University, Taian 271016, China

Received:2023-10-30 Online:2024-06-20 Published:2024-07-01
Contact: Xiaonan ZHANG E-mail:clily666666@163.com;zhangxn@bbmc.edu.cn

摘要/Abstract

摘要：

目的探讨类风湿关节炎（RA）的潜在发病机制，挖掘可以作为RA早期诊断的生物标志物，并探究其可能的免疫调控机制。方法依托GEO数据库多个基因组数据，利用limma包、RRA方法、批次矫正方法与clusterProfiler包筛选差异表达基因并预测其作用功能；通过STRING在线数据库得到蛋白质互作网络，利用Cytoscape3.8.0软件与GeneMANIA数据库筛选差异表达关键基因并预测其互作机制；构建ROC工作曲线验证关键基因诊断模型的信效度，并通过机器学习方法筛选疾病特征免疫细胞；使用corrplot包分析特征免疫细胞与疾病关键基因的相关性；使用GSEA富集方法探究关键基因生物学功能和相关性；通过构建胶原诱导性关节炎（CIA）大鼠模型探究具有较高诊断价值的差异基因在RA滑膜组织中的表达情况。结果得到与RA密切相关核心关键基因9个，分别为：CD3G、CD8A、SYK、LCK、IL2RG、STAT1、CCR5、ITGB2、ITGAL，富集分析结果表明差异基因可能主要通过细胞因子相关通路调控滑膜炎症；ROC工作曲线分析表明9个核心基因的预测模型信效度较高，其中STAT1预测模型的AUC值最高（0.909）；相关性分析结果显示，CD3G、ITGAL、LCK、CD8A和STAT1等5个核心基因与疾病特征免疫细胞相关性较强，其中STAT1与M1型巨噬细胞的相关性联系最强（R=0.68，P<0.001）；CIA大鼠膝关节的HE、番红-O固绿染色结果表明关节炎大鼠造模成功且药物治疗有效，免疫荧光结果表明STAT1和磷酸化的STAT1在CIA大鼠踝关节滑膜组织中表达高于正常组和治疗组；免疫印迹实验结果表明，STAT1蛋白表达量在滑膜成纤维细胞的细胞核（P=0.0002）与细胞质（P<0.0001）中存在差异，且经过治疗，细胞核内p-STAT1与STAT1的蛋白表达量显著降低（P<0.0001）。结论 CD3G、CD8A、SYK、LCK、IL2RG、STAT1、CCR5、ITGB2与ITGAL等9个基因可作为RA的早期诊断生物标志物，CD3G/CD8A/LCK-γδ T细胞、ITGAL-Tfh细胞、STAT1-M1型巨噬细胞等基因-免疫细胞途径可能与RA的发生发展密切相关。

关键词: 类风湿关节炎, 生物标志物, 免疫调控, 生物信息学, 机器学习

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. Conclusion 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.

Key words: rheumatoid arthritis, biomarkers, immune regulatory, bionformatic, machine learning

陈莉莉, 吴天宇, 张铭, 丁子夏, 张妍, 杨依清, 郑佳倩, 张小楠. 类风湿关节炎的潜在生物标志物及其免疫调控机制：基于GEO数据库[J]. 南方医科大学学报, 2024, 44(6): 1098-1108.

Lili CHEN, Tianyu WU, Ming ZHANG, Zixia DING, Yan ZHANG, Yiqing YANG, Jiaqian ZHENG, Xiaonan ZHANG. Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database[J]. Journal of Southern Medical University, 2024, 44(6): 1098-1108.

图/表 8

图1 不同数据集的差异基因表达情况

Fig.1 Differential gene expressions in different datasets of RA. A-E: RA-related differentially expressed genes in the GSE1919, GSE12021, GSE55235, GSE55457 and GSE77298 datasets.

图2 差异表达基因的GO和KEGG富集分析

Fig.2 GO and KEGG enrichment analysis of the differentially expressed genes (DEGs). A: Circle diagram for GO enrichment analysis of the DEGs. B: Sankey diagrams for KEGG enrichment analysis of the DEGs.

图3 差异表达核心基因筛选及表达情况

Fig.3 Screening of the differentially expressed core genes and their expressions. A: Results of the core gene screening. B: Complex Heatmap of the core genes. C-K: Expression of the core genes in control, early RA and advanced RA in the validation dataset (C: SYK; D: STAT1; E: LCK; F: IL2RG; G: ITGB2; H: ITGAL; I: CD3G; J: CCR5; K: CD8A).

图4 核心基因功能预测和ROC工作曲线结果

Fig.4 Core gene function prediction and receiver operating characteristic (ROC) curve results. A:Analysis of GeneMANIA results for core genes. B-J: Area under ROC for the core genes (B: CD8A; C: CD3G; D: IL2RG; E: ITGB2; F: ITGAL; G: LCK; H: SYK; I: CCR5; J: STAT1).

图5 疾病特征免疫细胞表达及筛选

Fig.5 Disease-specific immune cell expression and screening. A: Immune cell infiltration in RA. B: Differential expression of immune cells in RA. C: Correlation Heatmap of the immune cells. D: Intersections of disease-specific cells obtained by screening with Lasso regression and CIBERSORT methods.

图6 核心基因与疾病特征免疫细胞相关性分析

Fig.6 Correlation analysis of the core genes with the disease-specific immune cells. A: Heatmap of correlations between core genes and disease-specific immune cells. B-F: Correlation analysis of the core genes with disease-specific immune cells (B: CD8A with γδ T cell; C: CD3G with γδ T cell; D: ITGAL with Tfh cell; E: LCK with γδ T cell; F: STAT1 with macrophages M1).

图7 STAT1在CIA大鼠踝关节中的表达情况

Fig.7 Expression of STAT1 in ankle joints of CIA rats. A: GSEA analysis of biological processes potentially mediated by upregulated STAT1. B: HE staining of the knee joints of CIA rats. C: Safranine O-Fast Green staining of the knee joints. D: STAT1 expression in the synovial tissue of CIA rats.

图8 p-STAT1在CIA大鼠滑膜组织和滑膜成纤维细胞中的表达情况

Fig.8 Expression of p-STAT1 in the synovial tissue and fibroblast-like synoviocytes of CIA rats. A: p-STAT1 expression in the synovial tissue of CIA rats. B: Immunoblotting showing expressions of STAT1/p-STAT1 in the cytoplasm and nucleus of the synovial tissue cells in different groups. C, D: Western blotting of p-STAT and STAT1 proteins in cytoplasm and nuclei of synovial fibroblasts in the joint tissue of CIA rats. *P<0.05,**P<0.01,***P<0.001, ****P<0.0001.

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类风湿关节炎的潜在生物标志物及其免疫调控机制：基于GEO数据库

Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database

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