Integrated transcriptomic analysis of peripheral blood CD4 + T cells identifies immune-associated hub genes in rheumatoid arthritis: discovery and external validation.

Authors

Haiyang Kou, Huaiquan Liu, Lingyan Lai, Shili Yang, Xinyan Zhang, Yu Sun, Yunlin Xu, Bo Chen

Published in

Immunologic research. Volume 74. Issue 1. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by dysregulated immune responses, particularly involving CD4 + T cells. Identification of robust and reproducible molecular signatures from peripheral blood CD4 + T cells is critical for advancing biomarker discovery and understanding disease mechanisms. GSE80785 and GSE56649 (both on GPL570 platform) were integrated as the discovery cohort (n = 46; 37 RA and 9 healthy controls) after ComBat batch effect correction. GSE55235 and GSE55457 served as independent validation cohorts. Differentially expressed genes (DEGs) were identified using limma. Weighted gene co-expression network analysis (WGCNA) was performed to identify RA-associated modules. Candidate hub genes were screened by integrating PPI network topology (CytoHubba), LASSO regression, and Random Forest algorithms. External validation assessed expression consistency and diagnostic performance via ROC analysis. In the discovery cohort, 1,672 DEGs were identified (773 upregulated, 899 downregulated). WGCNA revealed 14 modules, with one module showing the strongest association with RA status. Integrative multi-algorithm analysis consistently identified four immune-associated hub genes: POLR2F, CDC42, HNRNPC, and LCN2. These genes exhibited significant correlations with immune-related pathways. In the validation cohorts, the four hub genes showed consistent expression patterns, with the combined model achieving AUC values ranging from 0.750 to 0.913. This multi-cohort study identifies POLR2F, CDC42, HNRNPC, and LCN2 as reproducible immune-associated hub genes in RA peripheral blood CD4 + T cells. The findings provide a solid foundation for further functional studies and potential biomarker development in RA.

PMID:
42496954
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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