Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 15;19(2):e0298447.
doi: 10.1371/journal.pone.0298447. eCollection 2024.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study

Li Wu  1   2 , Qi Wang  1   3   4 , Qi-Chao Gao  1 , Gao-Xiang Shi  1   5 , Jing Li  2 , Fu-Rong Fan  6 , Jing Wu  6 , Pei-Feng He  3   4 , Qi Yu  3   4
Affiliations

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study

Li Wu et al. PLoS One. .

Abstract

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart.
Fig 2
Fig 2. WGCNA analysis of the GSE55235 and GSE84844 data series.
(A) Soft threshold analysis in RA. (B) Module correlations in RA. (C) Genes with differential expression measured in RA are clustered in the dendrogram. Color bands indicate the results obtained from automatic single-block analysis. (D) Heatmap of the module–trait relationship in RA. Each cell displays the corresponding correlation and p-value. (E) Soft threshold analysis in pSS. (F) Module correlations in pSS. (G) Genes with differential expression measured in pSS are clustered in the dendrogram. Color bands indicate the results obtained from automatic single-block analysis. (H) Heatmap of module–trait relationship in pSS. Each cell displays the corresponding correlation and p-value. RA, rheumatoid arthritis; pSS, primary Sjögren’s syndrome; HC, healthy control.
Fig 3
Fig 3. Intersection genes of RA and pSS through WGCNA.
(A) Venn diagram of the intersection genes of RA and pSS. (B) PPI network of the intersection genes. (C) GO enrichment analysis of the intersection genes. (D) KEGG enrichment analysis of the intersection genes. RA, rheumatoid arthritis; pSS, primary Sjögren’s syndrome.
Fig 4
Fig 4. Intersection DEGs between RA and pSS.
(A) Venn diagram of the intersection DEGs in GSE23117 and GSE55457. (B) PPI network of intersection DEGs. (C) GO enrichment analysis of the intersection DEGs. (D) KEGG enrichment analysis of the intersection DEGs.
Fig 5
Fig 5. Targets shared by RA and pSS from the CTD and GeneCards databases.
(A) Venn diagram of targets shared by RA and pSS from the CTD and GeneCards databases. (B) GO enrichment analysis of the shared targets. (C) KEGG enrichment analysis of the shared targets. (D) PPI network of the shared targets. (E–G) Three closely connected gene modules according to the MCODE plug-in for Cytoscape. RA, rheumatoid arthritis; pSS, primary Sjögren’s syndrome.
Fig 6
Fig 6. Identification of hub genes.
(A) Venn diagram of targets shared in the CTD and GeneCards databases with genes from WGCNA and DEG analysis. (B) The Sankey diagram revealed the relationship between targets, drugs, and diseases. RA, rheumatoid arthritis; pSS, primary Sjögren’s syndrome; WGCNA, weighted gene co-expression network analysis; DEG, differentially expressed genes.
Fig 7
Fig 7. Validation of hub genes.
(A) Hub gene expression in GSE1919. (B) Hub gene expression in GSE110169. (C) Hub gene expression in GSE40611. (D) Hub gene expression in GSE84844. Mean t-tests were used to compare the two sets of data, and statistical significance was determined by a p-value <0.05. RA, rheumatoid arthritis; pSS, primary Sjögren’s syndrome.
Fig 8
Fig 8. MiRNA–gene regulatory network.
V shape indicates miRNA; circle shape indicates gene; green indicates downregulated; pink indicates upregulated.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Lee DM, Weinblatt ME. Rheumatoid arthritis. Lancet. 2001;358(9285):903–11. doi: 10.1016/S0140-6736(01)06075-5 - DOI - PubMed
    1. Masi AT, Bijlsma JW, Chikanza IC, Pitzalis C, Cutolo M. Neuroendocrine, immunologic, and microvascular systems interactions in rheumatoid arthritis: physiopathogenetic and therapeutic perspectives. Semin Arthritis Rheum. 1999;29(2):65–81. doi: 10.1016/s0049-0172(99)80039-0 - DOI - PubMed
    1. Fox RI. Sjogren’s syndrome. Lancet. 2005;366(9482):321–31. - PubMed
    1. Ramos-Casals M, Brito-Zeron P, Siso-Almirall A, Bosch X. Primary Sjogren syndrome. BMJ. 2012;344:e3821. doi: 10.1136/bmj.e3821 - DOI - PubMed
    1. Kollert F, Fisher BA. Equal rights in autoimmunity: is Sjogren’s syndrome ever ’secondary’? Rheumatology (Oxford). 2020;59(6):1218–25. - PubMed

MeSH terms

Grants and funding

Qi Yu and Pei-Feng He were supported by grants from the Shanxi Province key research and development program (No. 201903D311011; 201803D31067). The funders had a role in study conception and design.
-