基于生物信息学分析筛选原发性干燥综合征的特征基因  

Screening signature genes for primary Sjögren′s syndrome based on bioinformatics analysis

作　　者：孔腾 程灵婧 孙翔飞 郑超越 冯爽 邰杨芳 吴胜男 贺培凤 于琦 KONG Teng;CHENG Lingjing;SUN Xiangfei;ZHENG Chaoyue;FENG Shuang;TAI Yangfang;WU Shengnan;HE Peifeng;YU Qi(Library and Information Science,School of Management,Shanxi Medical University,Taiyuan 030001,China;Humanistic Medicine,School of Humanities and Social Sciences,Shanxi Medical University,Taiyuan 030001,China;Faculty of Medical Information Retrieval,School of Management,Shanxi Medical University,Taiyuan 030001,China;Faculty of Medical Information Technology,School of Management,Shanxi Medical University,Taiyuan 030001,China;Faculty of Organization and Management of Medical Informatics,School of Management,Shanxi Medical University,Taiyuan 030001,China)

机构地区：[1]山西医科大学管理学院图书情报专业,太原030001 [2]山西医科大学人文社会科学学院人文医学专业,太原030001 [3]山西医科大学管理学院医学信息检索教研室,太原030001 [4]山西医科大学管理学院医学信息技术教研室,太原030001 [5]山西医科大学管理学院医学信息组织与管理教研室,太原030001

出　　处：《中华疾病控制杂志》2023年第10期1193-1203,共11页Chinese Journal of Disease Control & Prevention

基　　金：山西省健康医疗大数据智能平台关键技术研究(201903D311011);山西省回国留学人员科研资助项目(HGKY2019057)。

摘　　要：目的应用生物信息学方法分析确定原发性干燥综合征(primary Sjögren′s syndrome,pSS)患者和健康对照者的特征基因,在转录组学水平上为pSS的发病机制提供思路和理论依据。方法从基因表达综合(gene expression opmnibus,GEO)数据库筛选获取pSS患者和健康对照者的芯片数据,数据集GSE84844和GSE66795用于分析获取目标基因,GSE40611用于验证。采用差异分析、加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)。利用生物信息学分析方法得到关键基因。通过最小绝对值收敛和选择算子(least absolute shrinkage and selection operator,LASSO)回归获得与pSS发病密切相关的特征基因,受试者工作特征(receiver operating characteristic,ROC)曲线下的面积用来评估特征基因对pSS的诊断价值。结果与健康对照者相比,pSS患者共筛选出55个差异表达基因;基因本体(gene ontology,GO)富集分析显示差异表达基因主要参与了抗病毒反应、正调控Ⅰ型干扰素的产生、抗病毒先天免疫反应等生物学过程;京都基因与基因组百科全书(kyoto encyclopedia of genes and geno omes,KEGG)信号通路富集分析发现差异表达基因富集在甲型流感、视黄酸诱导基因蛋白(retinoic acid-inducible gene I,RIG-I)样受体信号通路、坏死性凋亡和乙型肝炎等信号通路;WGCNA联合LASSO回归筛选出4个特征基因,分别为DDX60、EPSTI1、IFI27和IFI44,4个特征基因在验证数据集GSE40611中曲面下面积分别为0.807、0.866、0.804和0.892。结论DDX60、EPSTI1、IFI27和IFI44是pSS具有诊断意义的特征基因,能够为更深入地探索原发性干燥综合征的发生发展机制提供理论依据。Objective This study employed bioinformatics methods to identify signature genes in patients with primary Sjögren′s syndrome(pSS)compared to healthy controls,offering insights and a theoretical foundation for exploring the pathogenesis of pSS at the transcriptome level.Methods The microarray data containing the information of pSS patients and healthy controls was screened and obtained from the GEO database,with data sets GSE84844 and GSE66795 used to analyze and obtain target genes and GSE40611 used for validation.Differential analysis,weighted gene co-expression network analysis(WGCNA)and other bioinformatics analyses were used to obtain hub genes.The least absolute shrinkage and selection operator(LASSO)regression was used to obtain signature genes closely related to the pathogenesis of pSS,and the area under the receiver operating characteristic(ROC)curve was used to evaluate the diagnostic value of signature genes for pSS.Results Compared with healthy controls,pSS patients had 55 differential expression genes.GO enrichment analysis showed that DEGs were mainly involved in biological processes such as defense response to virus,positive regulation of typeⅠinterferon production and antiviral innate immune response.Furthermore,KEGG signal pathway enrichment analysis found that DEGs were enriched in influenza A,RIG-I-like receptor signaling pathway,necroptosis and hepatitis B.Four signature genes,DDX60,EPSTI1,IFI27 and IFI44,were screened by WGCNA combined with LASSO regression,and their AUC values in the validation data set were 0.807,0.866,0.804 and 0.892,respectively.Conclusions The signature genes DDX60,EPSTI1,IFI27,and IFI44 hold significant diagnostic value for pSS,providing a theoretical foundation for further exploration of the pathogenesis and progression mechanisms of pSS.

关 键 词：原发性干燥综合征 生物信息学分析 差异表达基因 加权基因共表达网络分析 最小绝对值收敛和选择算子 

分 类 号：R593.2[医药卫生—内科学]