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. 2023 Apr 6:14:1143644.
doi: 10.3389/fgene.2023.1143644. eCollection 2023.

Bioinformatics analysis based on crucial genes of endothelial cells in rheumatoid

Xing Zhou  1   2   3   4 , Lidong Wu  1   2   3   4
Affiliations

Bioinformatics analysis based on crucial genes of endothelial cells in rheumatoid

Xing Zhou et al. Front Genet. .

Retraction in

Abstract

Objectives: Synovial neovascularization is an early and remarkable event that promotes the development of rheumatoid arthritis (RA) synovial hyperplasia. This study aimed to find potential diagnostic markers and molecular therapeutic targets for RA at the mRNA molecular level. Method: We download the expression profile dataset GSE46687 from the gene expression ontology (GEO) microarray, and used R software to screen out the differentially expressed genes between the normal group and the disease group. Then we performed functional enrichment analysis, used the STRING database to construct a protein-protein interaction (PPI) network, and identify candidate crucial genes, infiltration of the immune cells and targeted molecular drug. Results: Rheumatoid arthritis datasets included 113 differentially expressed genes (DEGs) including 104 upregulated and 9 downregulated DEGs. The enrichment analysis of genes shows that the differential genes are mainly enriched in condensed chromosomes, ribosomal subunits, and oxidative phosphorylation. Through PPI network analysis, seven crucial genes were identified: RPS13, RPL34, RPS29, RPL35, SEC61G, RPL39L, and RPL37A. Finally, we find the potential compound drug for RA. Conclusion: Through this method, the pathogenesis of RA endothelial cells was further explained. It provided new therapeutic targets, but the relationship between these genes and RA needs further research to be validated in the future.

Keywords: DEGs; bioinformatics; endothelial cells; gene expression Omnibus; rheumatoid arthritis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Normal samples and RA samples (A) volcano plot of the 113 DEGs, The red dots represent upregulated genes, and the blue dots represent downregulated genes. (B) Heatmap of 40 DEGs between the RA samples and the OA samples. Red rectangles represent high expression, and blue rectangles represent a low expression.
FIGURE 2
FIGURE 2
The GSEA analysis of all genes.
FIGURE 3
FIGURE 3
Gene ontology (GO) and kyoto encyclopedia of Genes (KEGG) enrichment analyses of DEGs. (A) GO enrichment analysis. Zsore is positive, indicating that the corresponding item may be a positive adjustment. Zsore is negative, indicating that the corresponding item may be a negative adjustment. (B) The graph corresponding to figure (A,C) Zsore is positive, indicating that the corresponding item may be a positive adjustment. Zsore is negative, indicating that the corresponding item may be a negative adjustment. (D) The graph corresponding to figure (C).
FIGURE 4
FIGURE 4
PPI network of DEGs and four cluster modules extracted by MCODE. (A) The interaction network between proteins coded by DEGs was comprised of 63 nodes and 250 edges. The larger and darker the circle, the more important the gene. (B) Interaction of top 10 genes calculated by MCODE.
FIGURE 5
FIGURE 5
The ROC curve of ten important genes screened by MCODE.
FIGURE 6
FIGURE 6
The network of mRNA-miRNA.
FIGURE 7
FIGURE 7
Analysis of immune infiltration between NC and RA sample. (A) Figure A represents the proportion of different immune cells in different samples. (B,C) represents the correlation between key genes RPS29 and SEC61G and immune cells, respectively.
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References

    1. Aletaha D., Smolen J. S. (2018). Diagnosis and management of rheumatoid arthritis: A review. JAMA 320 (13), 1360–1372. 10.1001/jama.2018.13103 - DOI - PubMed
    1. Alivernini S., MacDonald L., Elmesmari A., Finlay S., Tolusso B., Gigante M. R., et al. (2020). Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat. Med. 26 (8), 1295–1306. 10.1038/s41591-020-0939-8 - DOI - PubMed
    1. Araujo V., Arnal C., Boronat M., Ruiz E., Dominguez C. (1998). Oxidant-antioxidant imbalance in blood of children with juvenile rheumatoid arthritis. Biofactors 8 (1-2), 155–159. 10.1002/biof.5520080126 - DOI - PubMed
    1. Bartok B., Firestein G. S. (2010). Fibroblast-like synoviocytes: Key effector cells in rheumatoid arthritis. Immunol. Rev. 233 (1), 233–255. 10.1111/j.0105-2896.2009.00859.x - DOI - PMC - PubMed
    1. Bordy R., Totoson P., Prati C., Marie C., Wendling D., Demougeot C. (2018). Microvascular endothelial dysfunction in rheumatoid arthritis. Nat. Rev. Rheumatol. 14 (7), 404–420. 10.1038/s41584-018-0022-8 - DOI - PubMed

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