Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis

Jia Xu; Ming-Ying Zhang; Wei Jiao; Cong-Qi Hu; Dan-Bin Wu; Jia-Hui Yu; Guang-Xing Chen

doi:10.2147/IJGM.S333512

Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis

Int J Gen Med. 2021 Nov 4:14:7687-7697. doi: 10.2147/IJGM.S333512. eCollection 2021.

Authors

Jia Xu¹, Ming-Ying Zhang², Wei Jiao¹, Cong-Qi Hu¹, Dan-Bin Wu², Jia-Hui Yu¹, Guang-Xing Chen^{2

3}

Affiliations

¹ First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
² Department of Rheumatology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
³ Baiyun Hospital of The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510470, Guangdong, People's Republic of China.

Abstract

Objective: Rheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis.

Methods: We searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network.

Results: There were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network.

Conclusion: This study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.

Keywords: bioinformatics analysis; key genes; pathways; rheumatoid arthritis; synovitis macrophages.

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China (No. 81573850) and Young scientific research talents training project of The First Affiliated Hospital of Guangzhou University of Chinese Medicine (2017QN07). The Guangzhou Science Technology and Innovation Commission Technology Research Projects (No. 201904010336).