Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Mendelian randomization; T cells; bulk RNA sequencing; combined biomarkers; machine learning; rheumatoid arthritis; single-cell RNA sequencing.

Figure 1

The workflow of this study.

Figure 2

Annotation of clusters and subpopulations of cells in the RA scRNA-Seq data. (A) UMAP of 18 cell clusters. (B) 10,211 cells were labeled by cell type in the UMAP analysis. (C) The proportions of different cell types. (D) Marker genes for T cells, B cells, monocytes, pDCs, NK cells, DCs, and plasmablasts are presented in UMAP results.

Figure 3

Analysis of cell-cell communication in the RA immune microenvironment. (A, B) The number and strength of interactions in cellular communication networks. (C) Bubble plots of ligand-receptor pairs mediating T cell interactions with other cells. (D) Ranking of the importance of each ligand-receptor contribution to the MIF signaling pathway. (E) The communication pattern of MIF signaling pathway in different cell clusters. (F) Expression levels of receptor-ligand pairs in the MIF signaling pathway.

Figure 4

Pseudo-time analysis of T cells in RA PBMCs. (A)UMAP of seven T cell subtypes. (B) Bubble plots show the expression of marker genes in the 7 T cells subtypes. (C) Trajectory plots showing the development of T cells. (D) Dynamic expression of ligand CD74 in T cells along pseudo time. (E) Heat map showing the expression of dynamic genes in pseudo time and BP enrichment analysis of different states.

Figure 5

Identification of T cell-related diagnostic features and construction and verification of nomogram models. (A) Heatmap showing Beta and IVW-P values of five MR methods for 309 genes. (B) Venn diagram of T cell-related genes and RA causal genes. (C) ROC curves for nine machine learning models. (D) Box plot of differential expression of T cell-related genes in healthy individuals and patients with RA.(E) A nomogram model describing T cell-related diagnostic features.(F) Validation of the diagnostic model based on eight T cell-related diagnostic features.^* P < 0.05, ^** P < 0.01, ^*** P < 0.001.

Figure 6

Identification, immune cell infiltration analysis, and GSVA of T cell patterns. (A) Consensus clustering matrix of eight T cell-related diagnostic features. (B) PCA showed a valid distinction between the two T cell patterns. (C) Differences in T cell scores between Cluster 1 and Cluster 2. (D) Differential expression of eight T cell-related diagnostic features according to T cell pattern. (E) The box plot shows two T cell patterns of immune cell infiltration. (F) Correlation heat map of immune cells with eight T cell-related diagnostic features. (G) Differential pathways in 2 T cell clusters ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001.

Figure 7

Correlation analysis of eight T cell-related diagnostic features with age of patients with RA.