骨关节炎患者外周血淋巴细胞基因表达谱的生物信息学分析

Bioinformatics analysis of gene expression profile of peripheral blood lymphocytes in patients with osteoarthritis

背景:目前没有监测骨关节炎发生或进展的敏感标志物,检测骨关节炎活动期外周血基因表达谱变化,有助于探寻血液中精确的诊疗靶点及阐释发病机制。目的:通过生物信息学方法分析骨关节炎患者与正常人外周血淋巴细胞基因表达谱差异,从分子层面探索血液中骨关节炎的诊疗靶点,为研究骨关节炎提供新思路。方法:从GEO和ArrayExpress数据库中查找骨关节炎血液相关的芯片数据,并下载GSE63359数据集。筛选样本包括46例骨关节炎患者血液和26例健康人血液,其中女性患者32例(健康女性19例)。用R语言limma包分别筛选出男/女性骨关节炎和男/女性健康人之间的差异表达基因,用ggplot2包绘制火山图,ComplexHeatmap包绘制热图。设定阈值为P<0.05&|log2FC|>0.5获取差异表达基因,然后制作韦恩图,得到8个差异表达基因:MAP2K7、CREBZF、CLK4、TRIM37、IL18RAP、LRRN3、BLNK和MS4A1;通过DAVID对差异表达基因进行GO和KEGG通路分析,并用R语言ggplot2包绘制气泡图。用STRING和Cytoscape软件构件PPI网络,Mcode和centiscape插件进行模块分析,Cytohubba筛选出关键基因。结果与结论:共筛选出差异表达基因115个,其中上调基因16个,下调基因99个,对所有差异表达基因进行GO富集分析主要集中在“淋巴细胞介导的免疫”“体液免疫反应”“抗原受体介导的信号通路”“B细胞受体信号通路”“免疫球蛋白介导的免疫反应”和“吞噬作用的正调控”等生物功能上;KEGG主要富集在5条与骨关节炎相关的通路上:造血细胞谱系、Th1和Th2细胞分化、Th17细胞分化、破骨细胞分化和TNF信号通路。利用PPI网络及相关插件筛选出10个与骨关节炎高度相关的关键基因,其中瘤坏死因子、CD19、转铁蛋白受体、配对框5、丝裂原活化蛋白激酶7、CD24、CD20和B细胞连接器8个核心基因与骨关节炎炎症和细胞凋亡高度相关。提示:通过生物信息学分析发现骨关节炎和健康人的外周血淋巴细胞基因表达差异集中在炎症反应和细胞凋亡,从而使血液表达谱成为监测骨关节炎靶点标记物和研究其潜在分子机制的有效突破口。

BACKGROUND:At present,there are no sensitive markers for monitoring the occurrence or progression of osteoarthritis.The detection of changes in peripheral blood gene expression profiles during the active period of osteoarthritis is helpful to investigate the accurate diagnosis and treatment targets in the blood and explain the pathogenesis.OBJECTIVE:To analyze the differences in the gene expression profiles of peripheral blood lymphocytes between osteoarthritis patients and healthy people by bioinformatics methods,and to explore the diagnosis and treatment targets of osteoarthritis from the molecular level in the blood,so as to provide new ideas for the study of osteoarthritis.METHODS:We searched osteoarthritis blood-related chip data from Gene Expression Omnibus and ArrayExpress databases,and downloaded the GSE63359 data set.The screening samples included 46 osteoarthritis patients’blood and 26 healthy people’s blood,including 32 female patients and 19 healthy women.The R language limma package was used to screen the differentially expressed genes between male/female osteoarthritis patients and male/female healthy people.The ggplot2 package was used to draw volcano plots,and the ComplexHeatmap package was used to draw heat maps.The threshold was set to P<0.05&|log2FC|>0.5 to obtain differentially expressed genes,and then a Venn diagram was made to obtain eight differentially expressed genes:MAP2K7,CREBZF,CLK4,TRIM37,IL18RAP,LRRN3,BLNK,and MS4A1.DAVID was used to analyze the gene ontology and Kyoto encyclopedia of genes and genomes pathways of differentially expressed genes,and the R language ggplot2 package was used to draw bubble plots.STRING and Cytoscape software were used to constructed protein-protein interaction network.Mcode and centiscape plug-in were used for module analysis,and the Cytohubba was used to screen out key genes.RESULTS AND CONCLUSION:A total of 115 differentially expressed genes were screened out,including 16 up-regulated genes and 99 down-regulated genes.The gene ontology enrichment analysis of all differentially expressed genes mainly focused on“lymphocyte-mediated immunity,”“humoral immune response,”“antigen receptor-mediated signaling pathway,”“B cell receptor signaling pathway,”“immunoglobulin-mediated immune response,”“positive regulation of phagocytosis,”and other biological functions.Kyoto encyclopedia of genes and genomes was mainly enriched in five pathways related to osteoarthritis:hematopoietic cell lineage,Th1 and Th2 cell differentiation,Th17 cell differentiation,osteoclast differentiation and TNF signaling pathway.Protein-protein interaction network and related plug-ins were used to screen out 10 key genes,including 8 core genes,that were highly related to osteoarthritis:tumor necrosis factor,CD19,transferrin receptor,pairing box 5,mitogen-activated protein kinase 7,CD24,CD20 and B cell connection,which were highly related to osteoarthritis inflammation and cell apoptosis.The bioinformatics analysis indicates that the differences in peripheral blood lymphocytes cells gene expression between osteoarthritis patients and healthy people are concentrated in cell apoptosis and inflammation,and thus blood expression profile becomes an effective breakthrough for monitoring osteoarthritis target markers and studying its potential molecular mechanisms.