摘要
目的通过对基因表达数据库(GEO)中变应性鼻炎(allergicrhinitis,AR)相关的基因芯片进行生物信息学分析,获得AR的生物标志物。方法2018年6月至2019年12月,从可公开获得的GEO(http://www.ncbi.nlm.nih.gov/geo)中下载包括3名健康对照者和6例AR患者的数据(GSE46171)。使用GEO2R在线工具在AR和正常组织之间进行筛查。然后使用生物信息学方法,包括基因本体(geneontology,GO)富集分析,京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)信号通路分析和蛋白质-蛋白质相互作用(PPI)网络构建,以鉴定AR中的关键基因。同期,武汉大学人民医院耳鼻咽喉头颈外科在手术中收集15例AR患者和15名健康对照者的下鼻甲黏膜组织,用于进一步验证重要的基因和途径,进行实时定量聚合酶链反应。采用SPSS 9.0统计学软件对测量结果进行统计学分析。结果共选择217个差异基因(differentially expressed genes,DEG),其中112个是下调基因,105个是上调基因。其中表达差异最大的5个上调基因依次为:KLK7、TMPRSS11A、SPRR2C、TPSAB1及TXLNGY;表达差异最大的5个下调基因依次为:XIST、CTAG1A、PRB1、CXCL11及PRB2。通过在217个DEG之间构建PPI网络,获得的15个hub基因依次为IFIH1、CCR2、CD80、TLR7、EIF1AY、DDX3Y、RSAD2、RPS4Y2、RPS4Y1、XAF1、KDM5D、ZFY、NLGN4Y、IFIT5和DDX60L,这些基因位于基因网络中的枢纽上。以15例AR患者和15名健康对照者的下鼻甲黏膜组织对这15个基因进行验证,结果显示AR患者的IFIH1、CCR2、CD80、TLR7、RSAD2、XAF1、IFIT5和DDX60L表达低于健康对照者,EIF1AY、DDX3Y、RPS4Y2、RPS4Y1、KDM5D、ZFY和NLGN4Y表达高于健康对照者,差异均有统计学意义(P值均<0.05)。结论本研究共发现了217个AR密切相关基因,并通过PPI网络获得15个hub基因,这些基因可能参与了AR的发病过程,有望成为AR新的生物标志物。
Objective To obtain biomarkers of allergic rhinitis(AR)by performing bioinformatics analysis on gene chips related to allergic rhinitis in the Gene Expression Database(GEO).Methods From June 2018 to December 2019,we downloaded data(GSE46171)involving 3 control individuals and 6 AR patients from the publicallyavailable Gene Expression Omnibus database(GEO,http://www.ncbi.nlm.nih.gov/geo),and differentially expressed genes(DEGs)were screened between AR and normal tissues by using the GEO2R online tool comprehensively.Then,we used the bioinformatics methods,including Gene Ontology(GO)analysis and Kyoto Encyclopedia of Gene,Genome(KEGG)pathway analysis and protein-protein interaction(PPI)network construction to identify key genes in AR.In the same period,the inferior turbinate mucosa tissues of 15 AR patients and 15 healthy controls were collected during operationinthe Department of Otolaryngology Head and Neck Surgery of the People′s Hospital of Wuhan Universityto further verify important genes and pathways and perform real-time quantitative PCR.SPSS9.0 statistical software was used for statistical analysis.Results Two hundred and seventeen DEGs genes were selected,of which 112 were down-regulated genes and 105 were up-regulated genes.Among them,the five up-regulated genes with the most significant differences were KLK7,TMPRSS11A,SPRR2C,TPSAB1,and TXLNGY;the five down-regulated genes with the most significant differences were:XIST,CTAG1A,PRB1,CXCL11 and PRB2.By constructing a PPI network among 217 DEGs,the 15 hub genes obtained were IFIH1,CCR2,CD80,TLR7,EIF1AY,DDX3Y,RSAD2,RPS4Y2,RPS4Y1,XAF1,KDM5D,ZFY,NLGN4Y,IFIT5 and DDX60L,these Genes were at a hub in a gene network.We collected inferior turbinate mucosa tissue during surgery,and these 15 genes were verified,and the expressions of IFIH1,CCR2,CD80,TLR7,RSAD2,XAF1,IFIT5 and DDX60L were reduced,wherea the expressions of EIF1AY,DDX3Y,RPS4Y2,RPS4Y1,KDM5D,ZFY and NLGN4Y were increased,differences were statistically significant(all P<0.05).Conclusions The study finds 21
作者
常文川
许昱
Chang Wenchuan;Xu Yu(Department of Otorhinolaryngology Head and Neck Surgery,Renmin Hospital of Wuhan University,Wuhan 430060,China;Research Institute of Otorhinolaryngology Head and Neck Surgery,Renmin Hospital of Wuhan University,Wuhan 430060,China)
出处
《中华耳鼻咽喉头颈外科杂志》
CAS
CSCD
北大核心
2020年第5期458-464,共7页
Chinese Journal of Otorhinolaryngology Head and Neck Surgery
基金
国家自然科学基金(81371070)。
关键词
鼻炎
变应性
生物信息学
生物标志物
Bioinformatical analysis
Rhinitis
allergic
Biomarkers
