Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis

Abstract

Objectives: This study aims to analyze the heterogeneity among different cell types in peripheral blood mononuclear cells (PBMC) in rheumatoid arthritis (RA) patients and to analyze T cell subsets to obtain key genes that may lead to RA.

Materials and methods: The sequencing data of 10,483 cells were obtained from the GEO data platform. The data were filtered and normalized initially and, then, principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (TSNE) cluster analysis were performed using the Seurat package in R language to group the cells, thereby obtaining the T cells. The T cells were subjected to subcluster analysis. The differentially expressed genes (DEGs) in T cell subclusters were obtained, and the hub genes were determined by Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network construction. Finally, the hub genes were validated using other datasets in the GEO data platform.

Results: The PBMC of RA patients were mainly divided into T cells, natural killer (NK) cells, B cells, and monocyte cells. The number of T cells was 4,483, which were further divided into seven clusters. The pseudotime trajectory analysis showed that the differentiation of T cells developed from cluster 0 and cluster 1 to cluster 5 and cluster 6. Through GO, KEGG and PPI analysis, the hub genes were identified. After validation by external data sets, nine genes were identified as candidate genes highly associated with the occurrence of RA, including CD8A, CCL5, GZMB, NKG7, PRF1, GZMH, CCR7, GZMK, and GZMA.

Conclusion: Based on single-cell sequencing analysis, we identified nine candidate genes for diagnosing RA, and validated their diagnostic value for RA patients. Our findings may provide new sights for the diagnosis and treatment of RA.

Citation: Liu Y, Fan S, Meng S. Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis. Arch Rheumatol 2023;38(1):109-118.

Ethics approval and consent to participate: Not applicable, because GEO belongs to public databases, the patients involved in the database have obtained ethical approval, users can download relevant data for free for research and publish relevant articles, and our study is based on open-source data, and the Affiliated Hospital of Weifang Medical University do not require research using publicly available data to be submitted for review to their ethics committee. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Data Sharing Statement:
The data that support the findings of this study are available from the corresponding author upon reasonable request.

Substantial contributions to the conception and design of the work: Y.L., S.M.; The acquisition, analysis, and interpretation of data for the work: Y.L., S.F.; Drafting the work: Y.L.; Revising it critically for important intellectual content: S.M.; Final approval of the version to be published, agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: Y.L., S.F., S.M.

The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

The authors received no financial support for the research and/or authorship of this article.