Bioinformatics analysis of oxidative stress and immune infiltration in rheumatoid arthritis

doi:10.12122/j.issn.1673-4254.2025.04.22

Abstract

Abstract:

Objective To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). Methods RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. Results We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. Conclusion Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.

Key words: rheumatoid arthritis, oxidative stress, immune infiltration, biomarkers, bioinformatics

Zhi GAO, Ao WU, Zhongxiang HU, Peiyang SUN. Bioinformatics analysis of oxidative stress and immune infiltration in rheumatoid arthritis[J]. Journal of Southern Medical University, 2025, 45(4): 862-870.

Figures/Tables 11

Tab.1 Primer sequences of the 5 core genes

Gene	Upstream primer (5'→3')	Downstream primer (5'→3')
JUN	GCTGAGCCTACAGATGAACT	GGCAGGATACCCAAACAAAC
MYC	TACACTAACATCCCACGCTC	TATAAATCATCGCAGGCGGA
MMP9	TTCCAGTACCGAGAGAAAGC	CACTGCAGGATGTCATAGGT
STAT1	CTGTGAAGTTGAGAGATGTGA	CAGTAACGATGAGAGGACCC
CCL5	GCCCTCGCTGTCATCCTCAT	AGCACTTGCCACTGGTGTAG

Fig.1 Differentially expressed genes (DEGs) in rheumatoid arthritis (RA) and oxidative stress-associated genes. A: Volcano diagram of the DEGs in RA. B: Heat map of the DEGs (blue represents the control group, and red the disease group; on the scale bar, red represents up-regulation and green represents down-regulation. A darker color indicates a higher gene expression level). C: Wayne diagram of the DEGs and oxidative stress-related genes showing 89 intersected genes.

Fig.2 KEGG and GO enrichment analysis of DEOSGs. A: The top 10 KEGG pathway enrichment bubbles of DEOSGs. B-D: Bubble diagrams of GO enrichment results showing the top 10 biological processes, molecular functions, and cellular components.

Fig.3 GSEA enrichment analysis. A: Three KEGG pathways. B: Three GO biological processes. C: Three GO molecular functions. D: Three GO cell components.

Fig.4 Protein interaction networks of DEOSGs. A: Protein interaction network of DEOSGs. B: The top 10 key genes obtained by the Degree algorithm. C: The module with the highest MCODE score.

Fig.5 Violin map showing differential expressions of the key gene between RA and control (HC) groups. *P<0.05, **P<0.01, ***P<0.001.

Fig.6 ROC curves of the 5 core genes MYC, STAT1, JUN, CCL5, and MMP9.

Fig.7 Column chart of the proportion of 22 immune cells in 20 healthy control samples and 23 RA samples.

Fig.8 Box plot of the proportion of the 22 immune cells in healthy control and RA samples. **P<0.01, ***P<0.001, ****P<0.0001.

Fig. 9 Correlation between the core genes and infiltrating immune cells. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

Fig.10 Scatter plot of expressions of the 5 core genes in RA and control (HC) groups. *P<0.05.

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