Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database

doi:10.12122/j.issn.1673-4254.2024.06.10

Abstract

Abstract:

Objective To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms. Methods The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA). Results We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment. Conclusion CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Key words: rheumatoid arthritis, biomarkers, immune regulatory, bionformatic, machine learning

Lili CHEN, Tianyu WU, Ming ZHANG, Zixia DING, Yan ZHANG, Yiqing YANG, Jiaqian ZHENG, Xiaonan ZHANG. Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database[J]. Journal of Southern Medical University, 2024, 44(6): 1098-1108.

Figures/Tables 8

Fig.1 Differential gene expressions in different datasets of RA. A-E: RA-related differentially expressed genes in the GSE1919, GSE12021, GSE55235, GSE55457 and GSE77298 datasets.

Fig.2 GO and KEGG enrichment analysis of the differentially expressed genes (DEGs). A: Circle diagram for GO enrichment analysis of the DEGs. B: Sankey diagrams for KEGG enrichment analysis of the DEGs.

Fig.3 Screening of the differentially expressed core genes and their expressions. A: Results of the core gene screening. B: Complex Heatmap of the core genes. C-K: Expression of the core genes in control, early RA and advanced RA in the validation dataset (C: SYK; D: STAT1; E: LCK; F: IL2RG; G: ITGB2; H: ITGAL; I: CD3G; J: CCR5; K: CD8A).

Fig.4 Core gene function prediction and receiver operating characteristic (ROC) curve results. A:Analysis of GeneMANIA results for core genes. B-J: Area under ROC for the core genes (B: CD8A; C: CD3G; D: IL2RG; E: ITGB2; F: ITGAL; G: LCK; H: SYK; I: CCR5; J: STAT1).

Fig.5 Disease-specific immune cell expression and screening. A: Immune cell infiltration in RA. B: Differential expression of immune cells in RA. C: Correlation Heatmap of the immune cells. D: Intersections of disease-specific cells obtained by screening with Lasso regression and CIBERSORT methods.

Fig.6 Correlation analysis of the core genes with the disease-specific immune cells. A: Heatmap of correlations between core genes and disease-specific immune cells. B-F: Correlation analysis of the core genes with disease-specific immune cells (B: CD8A with γδ T cell; C: CD3G with γδ T cell; D: ITGAL with Tfh cell; E: LCK with γδ T cell; F: STAT1 with macrophages M1).

Fig.7 Expression of STAT1 in ankle joints of CIA rats. A: GSEA analysis of biological processes potentially mediated by upregulated STAT1. B: HE staining of the knee joints of CIA rats. C: Safranine O-Fast Green staining of the knee joints. D: STAT1 expression in the synovial tissue of CIA rats.

Fig.8 Expression of p-STAT1 in the synovial tissue and fibroblast-like synoviocytes of CIA rats. A: p-STAT1 expression in the synovial tissue of CIA rats. B: Immunoblotting showing expressions of STAT1/p-STAT1 in the cytoplasm and nucleus of the synovial tissue cells in different groups. C, D: Western blotting of p-STAT and STAT1 proteins in cytoplasm and nuclei of synovial fibroblasts in the joint tissue of CIA rats. *P<0.05,**P<0.01,***P<0.001, ****P<0.0001.

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