【摘要】 目的：运用人工神经网络确定类风湿关节炎（RA）的特征基因，并分析免疫细胞在RA相关微环境中的作用。方法：GSE1919和GSE77298芯片均来自GEO数据库。运用R语言将两芯片进行合并与批次矫正，得到一个新数据集，并进行差异分析。对差异表达基因（DEGs）进行Metascape富集分析和GO、KEGG富集分析。运用R软件“randomForest”包在随机森林（RF）算法下筛选RA特征基因，并依据基因评分构建人工神经网络模型。提取排名前4的基因（HubGene）进行后续分析。应用单样本基因集富集分析（ssGSEA）计算样品中免疫细胞丰度并进行相关性分析。结果：人工神经网络模型筛选出排名前15的基因作为RA特征基因：STAT1、RUNX3、AR、CDH11、LMO4、TIMP1、PLXNC1、CAP2、PRKAA2、VDR、SPP1、HCK、EPHB2、KCNAB1、ITGB7，其中STAT1、RUNX3、CDH11在RA滑膜组织中均上调，AR在RA滑膜组织中下调。免疫细胞浸润结果显示，RA与活化CD4 T细胞相关性最显著。与RUNX3存在显著相关性的免疫细胞最多。RUNX3与活化B细胞、活化CD4 T细胞、活化CD8 T细胞、中央记忆CD4+T细胞、效应性记忆CD8 T细胞、调节性T细胞、γδT细胞和巨噬细胞呈显著正相关，但与NK细胞呈显著负相关。结论：通过人工神经网络确定了与RA相关的15个特征基因，其中排名前4的基因为STAT1、RUNX3、AR、CDH11。强调了活化CD4 T细胞、调节性T细胞、γδT细胞、巨噬细胞、NK细胞、活化B细胞等免疫细胞在RA发病机制中的重要性，为RA诊断和免疫细胞分子机制研究提供了新见解。更多还原

【Abstract】 Objective:To identify characteristic genes of rheumatoid arthritis(RA) by artificial neural network and to analyze role of immune cells in RA related microenvironment. Methods:GSE1919 and GSE77298 chips were from GEO database. Two chips were combined and batch corrected using R language to obtain a new data set, and difference was analyzed. Metascape enrichment analysis and GO and KEGG enrichment analysis were conducted for differential expressed genes(DEGs). "randomForest" package in R software was used to screen characteristic genes of RA under random forest(RF) algorithm, and artificial neural network model was constructed according to gene score. Top 4 genes(HubGene) were extracted for subsequent analysis. Single sample gene set enrichment analysis(ssGSEA) was used to calculate abundance of immune cells in samples and carry out a series of correlation analysis.Results:Top 15 genes were selected as characteristic genes of RA through artificial neural network model: STAT1, RUNX3, AR, CDH11, LMO4, TIMP1, PLXNC1, CAP2, PRKAA2, VDR, SPP1, HCK, EPHB2, KCNAB1, ITGB7. STAT1, RUNX3 and CDH11 were up-regulated in RA synovial tissue, while AR was down-regulated in RA synovial tissue. Immune cell infiltration results showed that RA had the most significant correlation with activated CD4 T cells. Number of immune cells significantly related to RUNX3 was the largest. RUNX3 was significant positive correlated with activated B cells, activated CD4 T cells, activated CD8 T cells, central memory CD4+T cells, effector memory CD8 T cells, regulatory T cells, γδT cells and macrophages, while significant negative correlated with NK cells. Conclusion:Fifteen characteristic genes related to RA are identified through artificial neural network, among which STAT1, RUNX3, AR and CDH11 are top 4 genes. It emphasizes activation of CD4 T cells, regulatory T cells, γδT cells, macrophages, NK cells, activated B cells and other immune cells are important in pathogenesis of RA, providing new insights for diagnosis of RA and study of molecular mechanism of immune cells.更多还原