Bioinformatics analysis of oxidative stress and immune infiltration in rheumatoid arthritis.
10.12122/j.issn.1673-4254.2025.04.22
- Author:
Zhi GAO
1
;
Ao WU
2
;
Zhongxiang HU
3
;
Peiyang SUN
4
Author Information
1. Anhui University of Traditional Chinese Medicine, Hefei 230000, China.
2. First Clinical Medical College of Shandong University of Chinese Medicine, Jinan 250000, China.
3. Department of Bone and Soft Tissue Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei 230000, China.
4. Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei 230000, China.
- Publication Type:Journal Article
- Keywords:
bioinformatics;
biomarkers;
immune infiltration;
oxidative stress;
rheumatoid arthritis
- MeSH:
Arthritis, Rheumatoid/genetics*;
Oxidative Stress/genetics*;
Humans;
Computational Biology;
Protein Interaction Maps;
Gene Expression Profiling;
Gene Regulatory Networks
- From:
Journal of Southern Medical University
2025;45(4):862-870
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES: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.
CONCLUSIONS: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.
