摘要
目的采用加权基因共表达网络分析法,筛选影响脓毒症预后的关键基因。方法从美国生物技术信息中心的基因表达数据库中,获取脓毒症患者和健康志愿者外周血基因芯片数据GSE54514,采用R语言加权基因共表达网络分析包构建脓毒症患者与健康志愿者差异基因的共表达网络,筛选与脓毒症预后相关的模块与枢纽基因,并对与脓毒症预后相关性最高的模块中的基因进行富集分析。结果通过对脓毒症患者与健康志愿者的622个差异表达基因构建共表达网络,筛选得到与脓毒症预后相关性最高的模块。GO富集分析显示该模块中的基因与髓系细胞的激活、中性粒细胞的激活相关;而KEGG通路富集分析显示这些基因在病毒感染过程中具有重要作用。最后通过构建蛋白质互相作用网络在与脓毒症预后相关性最高的模块中筛选得到15个枢纽基因。结论通过生物信息学方法挖掘出与脓毒症预后高度相关的15个关键基因,这些基因与调节机体对感染的免疫应答有关。
Objective To identify the key genes affecting the outcome of sepsis using weighted gene co-expression network analysis.Methods The peripheral blood gene chip data GSE54514 from septic patients and healthy volunteers were obtained from the gene expression database of the American Center for Biotechnology Information.An R package for weighted gene co-expression network analysis was used to construct a co-expression network of differentially expressed genes between sepsis patients and healthy volunteers to identify key modules associated with the outcome of sepsis.Then gene functional enrichment analysis was performed to figure out the possible behavior of genes in the most significant modulerelated tooutcomes of sepsis.Hub genes were selected from the most significant module according to module membership and degree of protein-protein interaction network.Results A total of 622 differentially expressed genes identified from the microarray data of GSE36895 in septic patients and healthy volunteers were used to construct a co-expression network,and the module with the most significant correlation with the outcome of sepsis was identified.GO enrichment analysis showed that the genes in this module were related to activation of myeloid cells and neutrophils,however,the KEGG pathway enrichment analysis showed that these genes played an important role in virus infection processes.Fifteen hub genes were finally selected from the module with the most significant correlation with the outcome of sepsis by constructing a protein-protein interaction network.Conclusion Fifteen key genes related to the outcome of sepsis are identified via bioinformatics methods,and the mechanism is related to regulating the immune response to infection.
作者
丁利锋
肖淑媛
张艳
方向明
Ding Lifeng;Xiao Shuyuan;Zhang Yan;Fang Xiangming(Department of Anesthesiology,the First Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou 310003,China)
出处
《中华麻醉学杂志》
CAS
CSCD
北大核心
2020年第2期221-224,共4页
Chinese Journal of Anesthesiology
基金
国家重点研发计划(2018YFC2001904)。
关键词
脓毒症
预后
基因
计算生物学
Sepsis
Prognosis
Genes
Computational biology
