基于整合生物信息学和网络药理学策略探索CD14+免疫细胞调控白塞病血管炎症免疫的生物分子网络的机制  

Exploring the mechanism of CD14+immune cells regulating the biomolecular network of Behcet’s disease in the aspect of vascular inflammation and immunity based on integrated bioinformatics and network pharmacology

作　　者：杨凯麟 许尚栋[1] 陈彧[1] 董松波[1] 阳晟[1] 金秀峰[1] 郑军[1] 孙立忠[1] YANG Kailin;XU Shangdong;CHEN Yu;DONG Songbo;YANG Sheng;JIN Xiufeng;ZHENG Jun;SUN Lizhong(Department of Cardiovascular Surgery,Beijing Anzhen Hospital,Capital Medical University,Beijing Institute of Heart,Lung and Blood Vessel Diseases,Beijing 100029,China)

机构地区：[1]首都医科大学附属北京安贞医院-北京市心肺血管疾病研究所心血管外科,100029

出　　处：《心肺血管病杂志》2021年第12期1250-1258,共9页Journal of Cardiovascular and Pulmonary Diseases

摘　　要：目的:基于整合生物信息学和网络药理学策略探索白塞病CD14+免疫细胞的全身生物分子网络。方法:使用GEO数据库检索白塞病相关的转录组学数据集,筛选后选择GSE61399转录组学数据集,其特征包括8个白塞病患者(实验组)CD14+免疫细胞样本和9个健康人(对照组)CD14+免疫细胞样本。首先对测序中的每个样品进行质量控制,然后使用R软件中的limma包筛选差异表达基因;然后使用DAVID对差异表达基因进行富集分析;CIBERSORTx用于对白塞病CD14+免疫细胞进行分类;最后,通过加权基因共表达网络分析(WGCNA)对数据集进行分析,得到与白塞病最相关的基因模块。结果:差异基因表达分析共获得397个差异基因,富集分析结果显示,差异表达基因主要参与对肿瘤坏死因子的细胞反应、对氧化应激的反应、细胞-细胞信号传导、血管生成的正调节、趋化性、细胞趋化性、内在凋亡信号通路的负调节、凋亡过程、对内质的反应网状应激等生物过程;和干细胞多能性的信号通路、粘附连接、TGF-β信号通路、癌症中的胆碱代谢、赖氨酸降解、AMPK信号通路等信号通路。CIBERSORTx的结果显示,细胞主要是单核细胞、中性粒细胞等。WGCNA显示前20位基因为RARA、OSBPL8、SAMD9L、DDX17、NIPBL、MIR21、VMP1、CCDC88 A、TOP1、CDC27、MALAT1、EHBP1L1、RAB12、TRAF3IP3、WASF2、MIS15RGLUBP1、MIR15、GLUMM4。结论:白塞病患者CD14免疫细胞差异表达基因主要与炎症、免疫等生物学过程有关。在差异表达基因中,BMPR2、LUZP1、C5AR1、THBS1可能与白塞病的心血管受累高度相关。Objective:To explore the mechanism of CD14+immune cells regulating the biomolecular network of Behcet’s disease in the aspect of vascular inflammation and immunity based on integrated bioinformatics and network pharmacology.Methods:The GEO database was used to retrieve data sets related to Behcet’s disease.The GSE61399 was selected,including 8 CD14 immune cell samples from Behcet’s disease patients(experimental group)and 9 CD14 immune cell samples from healthy people(control group).First,quality control was performed on each sample in the sequencing,and then limma R software was used to screen for differentially expressed genes.Then DAVID was used for enrichment analysis of differentially expressed genes.CIBERSORTx was used to classify Behcet’s disease CD14 immune cells.Finally,the data set was analyzed by Weighted gene co-expression network analysis(WGCNA)to obtain the gene modules most related to Behcet’s disease.Results:A total of 397 differentially expressed mRNAs were obtained.The result of enrichment analysis showed that differentially expressed genes are mainly involved in biological processes such as tumor necrosis factor cell response,response to oxidative stress,cell-cell signaling,positive regulation of angiogenesis,chemotaxis,cell chemotaxis,negative regulation of intrinsic apoptosis signaling pathway,apoptosis process,response to endoplasmic reticulum stress;signaling pathways such as stem cell pluripotency signaling pathway,adhesion junction,TGF-βsignaling pathway,choline metabolism in cancer,lysine degradation,AMPK signaling pathway.The result of CIBERSORTx showed that the cells are mainly monocytes,neutrophils and so on.The WGCNA showed that the top 20 genes were RARA,OSBPL8,SAMD9 L,DDX17,NIPBL,MIR21,VMP1,CCDC88 A,TOP1,CDC27,MALAT1,EHBP1 L1,RAB12,TRAF3 IP3,WASF2,MIS18 BP1,EVI5,GOLIM4,ZMYM5,ARGLU1.Conclusions:The differentially expressed genes of CD14 immune cells in patients with Behcet’s disease are mainly related to biological processes such as proteasome catabolism,RNA splicing,and

关 键 词：白塞病 CD14+免疫细胞 生物信息学 加权基因共表达网络分析 

分 类 号：R54[医药卫生—心血管疾病]