Identification of differentially expressed genes, transcription factors, microRNAs and pathways in neutrophils of sepsis patients through bioinformatics analysis.
Yukai Zheng, Lu Peng, Zhijie He, Zijun Zou, Fangyi Li, Canxia Huang, Weichao Li
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引用次数: 1
Abstract
Sepsis has been recognized to be a life-threatening organ dysfunction caused by the dysregulation of the host response to infections. Our work aims to screen key biomarkers related to neutrophils in sepsis using bioinformatics analysis. For this purpose, the microarray datasets related to neutrophils in sepsis patients were downloaded from the Gene Expression Omnibus (GEO) database. According to the Bayesian test, the Limma package in R was used to screen differentially expressed genes (DEGs). Then, DEGs were uploaded to the DAVID online diagnostic tool for subsequent Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment on the selected DEGs. Next, protein-protein interaction (PPI) network was established based on the selected DEGs using the STRING website and the Cytoscape software. Furthermore, according to the function of the iRegulon plug-in in Cytoscape, our study further predicts and established regulatory networks related to transcription factors and regulatory genes. In addition, the miRWalk2.0 database was used to search for miRNA-DEG pairs, associated with the conduction of intersections of miRNAs predicted by TargetScan, Miranda, miRDB and RNA22 databases. Then, these miRNA-DEG pairs were also displayed in the form of a regulatory network through Cytoscape. Finally, two datasets were selected to verify the screened genes, regulatory factors, and miRNAs, to plot receiver operating characteristics (ROC) curves and compute the area under the curve (AUC) values. The results showed that AKT1, MMP9, ARG1, ETS1 targeting AKT1, and has-miR-124-3p targeting RPS6KA5 may have diagnostic value for patients with sepsis and septic shock. While further experimental studies are required to confirm their role in septic neutrophils.
脓毒症被认为是一种危及生命的器官功能障碍,由宿主对感染的反应失调引起。我们的工作旨在利用生物信息学分析筛选脓毒症中与中性粒细胞相关的关键生物标志物。为此,我们从Gene Expression Omnibus (GEO)数据库下载了脓毒症患者中性粒细胞相关的微阵列数据集。根据贝叶斯检验,使用R中的Limma包筛选差异表达基因(DEGs)。然后,将基因片段上传到DAVID在线诊断工具,进行后续的基因本体(GO)和京都基因与基因组百科全书(KEGG)途径富集。接下来,利用STRING网站和Cytoscape软件,基于所选择的deg建立蛋白-蛋白相互作用(PPI)网络。此外,根据iRegulon插件在Cytoscape中的功能,我们的研究进一步预测并建立了与转录因子和调控基因相关的调控网络。此外,使用miRWalk2.0数据库搜索与TargetScan、Miranda、miRDB和RNA22数据库预测的mirna交叉传导相关的miRNA-DEG对。然后,这些miRNA-DEG对也通过Cytoscape以调控网络的形式显示出来。最后,选择两个数据集来验证筛选的基因、调控因子和mirna,绘制受试者工作特征(ROC)曲线并计算曲线下面积(AUC)值。结果显示,AKT1、MMP9、ARG1、靶向AKT1的ETS1以及靶向RPS6KA5的has-miR-124-3p可能对脓毒症和感染性休克患者具有诊断价值。但需要进一步的实验研究来证实它们在感染性中性粒细胞中的作用。