摘要
目的基于生物信息学和孟德尔随机化分析研究溃疡性结肠炎(ulcerative colitis,UC)具有诊断和治疗潜力的内质网应激(endoplasmic reticulum stress,ERS)相关基因,并进行相关基因的基因集富集分析(Gene Set Enrichment Analysis,GSEA)、基因本体论(Gene Ontology,GO)和京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Gnomes,KEGG)分析,构建ceRNA调控网络。方法从高通量基因表达数据库(Gene Expression Omnibus,GEO)获取UC患者和健康对照者的结肠组织基因表达谱及临床信息,采用差异表达分析法筛选与UC相关的差异基因,从GeneCards数据库下载ERS相关基因集,差异基因和ERS相关基因取交集得到差异ERS相关基因,利用GO和KEGG对差异ERS相关基因进行分析。采用Lasso回归、支持向量机(support vector machine,SVM)、随机森林树三种方法筛选并取交集得到UC疾病特征基因,利用表达数量性状位点(expression quantitative trait locus,eQTL)暴露数据和UC结局数据进行孟德尔随机化分析,得到具有诊断和治疗潜力的目标基因。采用GSEA、基因集变异分析(Gene Set Variation Analysis,GSVA)和CIBERSORT免疫细胞浸润分析探索目标基因与免疫细胞组成之间的相关性。预测与目标基因表达相关的miRNA和lncRNA,并构建ceRNA调控网络。结果孟德尔随机化结果显示,基因ANXA5增加UC发病风险。GSEA结果显示,目标基因主要富集通路包括趋化因子信号通路、细胞因子受体相互作用通路、造血细胞调控信号通路、JAK/STAT信号通路、利什曼原虫感染等。GSVA结果显示,补体和凝血级联等通路上调,缬氨酸亮氨酸和异亮氨酸降解等通路下调。免疫细胞浸润分析结果显示,基因ANXA5正调控中性白细胞、活化肥大细胞等,负调控静止树突状细胞、巨噬细胞M2等。鉴定出3个关键miRNA和15个lncRNA,并绘制出ceRNA调控网络。结论本研究预测了ERS相关基因ANXA5增加UC发病风险,鉴定出的miRNA、lncRNA或可作为UC的生物标志物。
Objective To study the endoplasmic reticulum stress(ERS)-associated genes with diagnostic and therapeutic potential for ulcerative colitis(UC)based on bioinformatics and Mendelian randomization analysis.Gene Set Enrichment Analysis(GSEA),Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Gnomes(KEGG)analysis were performed on relevant genes,and a ceRNA regulatory network was constructed.Methods Gene expression profiles and clinical information of colon tissues from UC patients and healthy controls were obtained from Gene Expression Omnibus(GEO)database.Heteroexpression analysis was used to screen for differentially expressed genes related to UC.The ERS-associated gene set was downloaded from GeneCards database for analysis.The intersection of differentially expressed genes and ERS-associated genes was used to obtain differentially expressed ERS-associated genes.GO and KEGG were used to analyze differentially expressed ERS-associated genes.Three methods including Lasso regression,support vector machine(SVM)and random forest tree were used to screen UC disease characteristic genes,the intersection of the results obtained from the three methods was used to obtain UC disease characteristic genes.Mendelian randomization analysis was performed using expression quantitative trait locus(eQTL)exposure data and UC outcome data to obtain target genes with diagnostic and therapeutic potential.GSEA,Gene Set Variation Analysis(GSVA)and CIBERSORT immune cell infiltration analysis were used to explore the correlation between target genes and immune cell composition.MiRNAs and lncRNAs related to target genes were identified to construct a ceRNA regulatory network.Results Results of Mendelian randomization analysis showed that the gene ANXA5 increased the risk of developing UC.Results of GSEA showed that the main enriched pathways of target genes were chemokine signaling,cytokine receptor interaction,hematopoietic cell lineage,JAK/STAT signaling pathway,Leishmania infection.Results of GSVA showed the upregulation of complement and coagulation cascades pathway,etc,and the downregulation of valine leucine and isoleucine degradation pathway,etc.Results of immune cell infiltration analysis showed that neutrophils,activated mast cells,etc.were positively regulated by the gene ANXA5,while stationary dendritic cells,macrophages M2,etc.were negatively regulated by the gene ANXA5.Three key miRNAs and 15 lncRNAs were identified,and the ceRNA regulatory network was mapped.Conclusion This study predicted that the ERS-associated gene ANXA5 increased the risk of UC.The miRNA and lncRNA identified might be potential biomarkers for UC.
作者
刘鹏
赵林
李志远
白洋洋
高素亚
郭伟胜
LIU Peng;ZHAO Lin;LI Zhiyuan;BAI Yangyang;GAO Suya;GUO Weisheng(Department of General Surgery,Henan Province Hospital of TCM(The Second Affiliated Hospital of Henan University of Chinese Medicine),Zhengzhou 450002,China;Department of Gastroenterology,Xinzheng Huaxin Minsheng Hospital(formerly Xinzheng People's Hospital),Xinzheng 451100,Henan Province,China)
出处
《数理医药学杂志》
CAS
2024年第10期722-733,共12页
Journal of Mathematical Medicine
