ORIGINAL RESEARCH article
Front. Immunol.
Sec. T Cell Biology
Dual-omics analysis of key biomarkers in T cell ubiquitination of rheumatoid arthritis blood and synovial tissue, validated by two-sample Mendelian randomization and qPCR
Provisionally accepted
- Third Hospital of Hebei Medical University, Shijiazhuang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Abnormal T-cell ubiquitination has been implicated in RA pathogenesis, yet its molecular mechanisms remain unclear. Methods: Transcriptomic data from RA blood and synovial tissue were analyzed to identify differentially expressed genes (DEGs). Ubiquitination-related module genes were obtained using weighted gene co-expression network analysis (WGCNA), and their overlap with DEGs yielded blood-synovial ubiquitination-related genes (BS-UGs). Single-cell datasets were used to extract T-cell marker genes, and intersection analysis identified T-cell-specific ubiquitination genes (BS-TUGs). Machine learning algorithms (SVM-RFE and Boruta) screened key BS-TUGs. Immune infiltration, transcription factor (TF) regulation, and master regulators were explored. Finally, two-sample Mendelian randomization (MR) was performed to assess causal relationships between key genes and RA. Results: A total of 521 BS-UGs and 21 candidate BS-TUGs were identified, from which six key genes (DOCK10, DGKA, NOP58, JAK3, GCC2, ANO9) were selected. These genes exhibited significant immune-cell correlations and were regulated by multiple TFs. MR analysis demonstrated a positive causal association between NOP58 (OR = 1.074, p = 0.001) and RA, and a negative association between GCC2 (OR = 0.928, p < 0.001) and RA, without heterogeneity or pleiotropy. Conclusion: Integrative dual-omics and MR analyses identified key ubiquitination-related T-cell genes driving RA pathogenesis. NOP58 and GCC2 represent potential causal biomarkers and therapeutic targets, offering novel insights into immune regulation and precision intervention in RA.
Keywords: machine learning, Rheumatoid arthritis, single-cell, T cell ubiquitination, Two-sample mendelian randomization
Received: 10 Dec 2025; Accepted: 12 Feb 2026.
Copyright: © 2026 He, ZHAO and MENG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jinghong MENG
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.