The First School of Clinical Medicine, Guangxi Traditional Chinesen Medical University, Nanning, China.
Gynecology Department, The First People's Hospital of Guangzhou, Guangzhou, China.
Front Immunol. 2024 Jun 19;15:1399856. doi: 10.3389/fimmu.2024.1399856. eCollection 2024.
Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy.
scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features.
By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients.
Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.
类风湿关节炎（RA）是一种全身性疾病，会攻击关节，给全球人类带来沉重的经济负担。T 细胞调节 RA 的进展，被认为是治疗的关键靶点。因此，我们旨在整合多个数据集，以探索 RA 的机制。此外，我们建立了一个与 T 细胞相关的诊断模型，为 RA 的免疫治疗提供了一种新方法。
从基因表达综合数据库（GEO）中获取 RA 的 scRNA-seq 和 bulk-seq 数据集。使用多种方法分析和描述 RA 的 T 细胞异质性。利用孟德尔随机化（MR）和表达数量性状基因座（eQTL）筛选 RA 中潜在的致病性 T 细胞标记基因。随后，我们通过比较九种机器学习方法在预测 RA 中的表现，选择了最佳的机器学习方法，以确定与 T 细胞相关的诊断特征，构建列线图模型。使用共识聚类方法将 RA 患者分为不同的 T 细胞相关聚类。最后，我们对与 T 细胞相关的诊断特征进行免疫细胞浸润和临床相关性分析。
通过分析 scRNA-seq 数据集，我们获得了 10211 个细胞，根据特定的标记基因将其注释为 7 种不同的亚型。通过整合血液和 RA 的 GWAS 的 eQTL，结合 XGB 机器学习，我们总共确定了 8 个与 T 细胞相关的诊断特征（MIER1、PPP1CB、ICOS、GADD45A、CD3D、SLFN5、PIP4K2A 和 IL6ST）。共识聚类分析表明，RA 可分为两种不同的 T 细胞模式（Cluster 1 和 Cluster 2），Cluster 2 的 T 细胞评分高于 Cluster 1。这两个簇涉及不同的通路，并且具有不同的免疫细胞浸润状态。两种不同的 T 细胞模式在年龄或性别上没有差异。此外，ICOS 和 IL6ST 在 RA 患者中与年龄呈负相关。
我们的研究结果阐明了 RA 中 T 细胞的异质性以及这些细胞在 RA 免疫微环境中的通讯作用。构建与 T 细胞相关的诊断模型为指导 RA 免疫治疗策略提供了资源。
