基于加权基因共表达网络分析识别雄激素性脱发的枢纽基因  

Identification of key genes in androgenic alopecia based on weighted gene co-expression network analysis

作　　者：候圣祥 张敬芳 胡俊 刘璐 陈天禹 庞燊 HOU Sheng-xiang;ZHANG Jing-fang;HU Jun(Changsha Medical University,Changsha 410219,China)

机构地区：[1]长沙医学院,410219

出　　处：《实用皮肤病学杂志》2022年第1期9-13,共5页Journal of Practical Dermatology

基　　金：国家大学生创新创业训练项目(S202010823012);湖南省教学改革研究项目(HNJG-2020-1038);湖南省学位与研究生教育改革研究项目(2020JGYB271);湖南省教育科学“十三五”规划项目(XJK20CGD049)。

摘　　要：目的使用加权基因共表达网络分析(WGCNA)探究雄激素性脱发(AGA)基因的协同共表达,寻找与雄激素性脱发有关的关键基因。方法借助R语言分析基因表达数据库(GEO)中关于AGA的GSE90594数据集,得到与临床表现正负相关性最高的模块各一个,并提取模块中的基因,使用clusterprofiler包进行下游GO与KEGG富集分析,同时对模块内GS和MM的相关性进行分析,并使用Cytosacpe绘制共表达网络图,筛选枢纽基因。结果根据基因表达的相关性,发现20个共表达模块,其中brown模块和turquoise模块与AGA显著相关,共表达网络发现13个基因(PDGFRA、PMP22、ZCCHC24、COL6A1、ISLR、PRRX1、PKP1、CALN1、PNMAL2、PPP5D1、GJB6、DSC2、GJA3)在网络中处于核心的地位。并用上述基因做了疾病模型,对疾病预测准确性很高。结论PDGFRA、PMP22、ZCCHC24、COL6A1、ISLR、PRRX1的表达与AGA呈正相关,PKP1、CALN1、PNMAL2、PPP5D1、GJB6、DSC2、GJA3的表达与AGA呈负相关。Objective To explore the co-expression of androgen alopecia(AGA)genes by weighted gene co-expression network analysis(WGCNA),and to find the key genes related to androgen alopecia.Methods With the help of GSE 90594 data set about androgen alopecia in R language analysis gene expression database(GEO),the modules with the highest positive and negative correlation with clinical manifestations were obtained,and the genes in each module were extracted.The downstream GO and KEGG enrichment analysis were carried out using clusterprofiler package,and the correlation between GS and MM in the module was analyzed.Co-expression network was drawn using Cytoscape and key genes were screened.Results 20 co-expression modules were found according to the correlation of gene expression,among which brown module and turquoise module were significantly associated with androgen alopecia.Thirteen genes(PDGFRA,PMP22,ZCCHC24,COL6A1,ISLR,PRRX1,PKP1,CALN1,PNMAL2,PPP5D1,GJB6,DSC2,GJA3)were found in the core of the coexpression network.The above genes are used to build disease model,and the accuracy of disease prediction is very high.Conclusion The expression of PDGFRA,PMP22,ZCCHC24,COL6A1,ISLR,PRRX1 was positively correlated with AGA,In contrast,the expression of PKP1,CALN1,PNMAL2,PPP5D1,GJB6,DSC2 and GJA3 was negatively correlated with AGA.

关 键 词：脱发 雄激素性 权重基因共表达网络分析 生物信息学 

分 类 号：R758.71[医药卫生—皮肤病学与性病学]