Xu Jia, Zhang Ming-Ying, Jiao Wei, Hu Cong-Qi, Wu Dan-Bin, Yu Jia-Hui, Chen Guang-Xing
First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Department of Rheumatology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Int J Gen Med. 2021 Nov 4;14:7687-7697. doi: 10.2147/IJGM.S333512. eCollection 2021.
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.
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.
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.
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.
类风湿关节炎(RA)是全球最常见的炎症性关节炎之一。然而,与RA患者滑液中巨噬细胞相关的基因和信号通路仍不清楚。本研究旨在通过生物信息学分析筛选并验证与类风湿关节炎滑膜巨噬细胞相关的差异表达基因(DEG),以鉴定候选基因。
我们检索了基因表达综合数据库(GEO),并选择了多个RA微阵列数据集的滑膜巨噬细胞表达谱的GSE97779和GSE10500进行系统分析。GSE97779包括9个来自RA患者滑液的巨噬细胞样本和5个来自健康对照者外周血的巨噬细胞样本。GSE10500包括5个来自RA患者滑液的巨噬细胞样本和3个来自健康对照者外周血的巨噬细胞样本。对DEG进行功能注释,包括基因本体(GO)分析和京都基因与基因组百科全书(KEGG)信号通路富集分析。使用STRING数据库建立DEG的蛋白质-蛋白质相互作用(PPI)网络。使用CytoHubba鉴定枢纽基因。使用MCODE确定交互网络中的基因簇。
分别从GSE97779和GSE10500中筛选出2638个DEG(1425个上调基因和1213个下调基因)和889个DEG(438个上调基因和451个下调基因)。维恩图显示,两个数据集中均有173个基因上调,106个基因下调。使用PPI网络鉴定出前10个枢纽基因,包括纤连蛋白1(FN1)、血管内皮生长因子A(VEGFA)、肝细胞生长因子(HGF)、丝氨酸蛋白酶抑制剂A1(SERPINA1)、基质金属蛋白酶9(MMP9)、血小板碱性蛋白(PPBP)、CD44、甲酰肽受体2(FPR2)、胰岛素样生长因子1(IGF1)和整合素αM(ITGAM)。
本研究为RA患者潜在的生物标志物和相关分子机制提供了新的见解。我们的研究结果表明,这10个候选基因未来可能用于RA的诊断、预后评估和治疗。然而,需要进一步研究来证实这些基因在RA和对照样本滑膜巨噬细胞中的表达情况。