慢性阻塞性肺疾病内质网应激特征基因筛选及免疫浸润表现  

Screening of endoplasmic reticulum stress characteristic genes and immune infiltration manifestations in chronic obstructive pulmonary disease

作　　者：张爽 罗晨阳 何志义[1] ZHANG Shuang;LUO Chenyang;HE Zhiyi(Department of Respiratory Medicine,the First Affiliated Hospital of Guangxi Medical University,Nanning 530021,China)

机构地区：[1]广西医科大学第一附属医院呼吸内科,南宁530021

出　　处：《广西医科大学学报》2024年第7期1042-1055,共14页Journal of Guangxi Medical University

基　　金：国家自然科学基金资助项目(No.82260012)。

摘　　要：目的:基于生物信息学、机器学习算法和实验验证揭示内质网应激(ERS)在慢性阻塞性肺疾病(COPD)中的核心基因。方法:从GEO数据库下载微阵列数据集GSE5058、GSE8545和GSE19407,以鉴定COPD吸烟者和非吸烟者气道上皮细胞之间的差异表达基因(DEGs),随后与ERS相关基因重叠后得到共有差异表达基因(ERs-DEGs)并进行富集分析。通过LASSO、SVM-RFE和RF 3种机器学习算法筛选ERS特征基因,并在GSE10006中验证和评估其诊断效能,随后进行免疫浸润分析、构建关键基因的lncRNA-miRNA-mRNA ceRNA网络和小鼠肺气肿模型肺组织mRNA表达量验证。结果:筛选出153个共有差异基因,其中74个基因表达上调,79个基因表达下调。GO和KEGG分析显示,ERs-DEGs主要富集于ERS反应、蛋白折叠及多个炎症信号通路,DO分析主要富集于肺血管闭塞性疾病、COPD等。免疫浸润分析提示,COPD样本与多种免疫细胞浸润高度相关。经机器学习算法最终共鉴定出4个特征基因(包括THBS1、BCL2、USP13和RNFT2),且在训练集和验证集中均显示出良好的诊断效能。同时,选择共表达的m RNA和miRNA构建mRNA-miRNA相互作用网络。RT-PCR结果显示,与空气组小鼠比较,香烟烟雾暴露诱导的肺气肿小鼠肺组织THBS1和RNFT2的mRNA表达水平升高,BCL2和USP13的mRNA表达水平下降(均P<0.05)。结论:THBS1、BCL2、USP13和RNFT2可能是COPD发病过程中ERS形成的核心基因,有望成为COPD免疫治疗的靶点。Objective:To identify the core genes of endoplasmic reticulum stress(ERS)in chronic obstructive pulmonary disease(COPD)using the bioinformatics,various machine learning algorithms and experimental vali-dation.Methods:The microarray data GSE5058,GSE8545,and GSE19407 were downloaded from the GEO da-tabase to identify differentially expressed genes(DEGs)between airway epithelial cells of COPD smokers and non-smokers,and then the common DEGs were obtained after overlapping with ERS-related genes and enriched for analysis.Three machine learning algorithms,LASSO,SVM-RFE,and RF,were used to screen the characteris-tic genes,and their diagnostic performance was verified and evaluated in the GSE10006.Subsequently,immuno-infiltration analysis was performed.Finally the lncRNA-miRNA-mRNA ceRNA network of key genes was con-structed and the mRNA expression levels of lung tissue in the mouse emphysema model were verified.Results:A total of 153 common DEGs were screened,of which 74 genes were up-regulated and 79 genes were down-regu-lated.GO and KEGG analysis showed that ERs-DEGs were mainly enriched in ERS response,protein folding and multiple inflammatory signaling pathways,and DO analysis was mainly enriched in pulmonary vascular oc-clusive diseases and COPD and so on.Immunoinfiltration analysis showed that COPD samples were highly corre-lated with a variety of immune cell infiltrations.A total of 4 characteristic genes(including THBS1,BCL2,USP13and RNFT2)were finally identified by machine learning algorithms,and they showed good diagnostic performance in both the training set and the validation set.At the same time,co-expressed mRNA and miRNA were selected to construct the mRNA-miRNA interaction network.Reverse transcription-quantitative polymerase chain reaction(RT-qPCR)results showed that compared with the air exposure group mice,the mRNA expression levels of THBS1and RNFT2in the lung tissues of emphysema mice induced by cigarette smoke exposure were increased,and the mRNA expression levels of BCL2and USP13were decreased(al

关 键 词：慢性阻塞性肺疾病 内质网应激 免疫浸润 生物信息学 

分 类 号：R563.3[医药卫生—呼吸系统]