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Bioinformatics analysis to screen for critical genes between survived and non-survived patients with sepsis

机译:生物信息学分析筛选脓毒症患者患者患者的临界基因筛选

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Sepsis is a systemic inflammatory response syndrome, which is mostly induced by infection in the lungs, the abdomen and the urinary tract. The present study is aimed to investigate the mechanisms of sepsis. Expression profile of E-MTAB-4421 (including leukocytes isolated from 207 survived and 58 non-survived patients with sepsis) and E-MTAB-4451 (including leukocytes isolated from 56 survived and 50 non-survived patients with sepsis) were downloaded from the European Bioinformatics Institute database. Based on the E-MTAB-4421 expression profile, several differentially expressed genes (DEGs) were identified and performed with hierarchical clustering analysis by the limma and pheatmap packages in R. Using the BioGRID database and Cytoscape software, a protein-protein interaction (PPI) network was constructed for the DEGs. Furthermore, module division and module annotation separately were conducted by the Mcode and BiNGO plugins in Cytoscape software. Additionally, the support vector machine (SVM) classifier was constructed by the SVM function of e1071 package in R, and then verified using the dataset of E-MTAB-4451. A total of 384 DEGs were screened in the survival group. The PPI network was divided into 4 modules (modules A, B, C and D) involving 11 DEGs including microtubule-associated protein 1 light chain 3 alpha (MAP1LC3A), protein kinase C-alpha (PRKCA), metastasis associated 1 family member 3 (MTA3), and scribbled planar cell polarity protein (SCRIB). SCRIB and PRKCA in module B, as well as MAP1LC3A and MTA3 in module D, might function in sepsis through PPIs. Functional enrichment demonstrated that MAP1LC3A in module D was enriched in autophagy vacuole assembly. Finally, the SVM classifier could correctly and effectively identify the samples in E-MTAB-4451. In conclusion, DEGs such as MAP1LC3A, PRKCA, MTA3 and SCRIB may be implicated in the progression of sepsis, and need further and more thorough confirmation.
机译:败血症是一种全身炎症反应综合征,主要由肺部,腹部和泌尿道感染诱导。本研究旨在调查败血症的机制。 E-MTAB-4421的表达谱(包括从207分离的白细胞生存和58例未存活的败血症患者)和E-MTAB-4451(包括从56个中分离的白细胞和50例未存活的败血症患者)下载了欧洲生物信息学研究所数据库。基于E-MTAB-4421表达谱,鉴定了几种差异表达的基因(DEGS)并通过R.在R中的水马和Pheatmap封装进行了分层聚类分析。使用BioGrid Database和Cytoscape软件,蛋白质 - 蛋白质相互作用(PPI )网络被构造为Degs。此外,模块划分和模块注释分别由Cytoscape软件中的MCODE和Bingo插件进行。另外,支持向量机(SVM)分类器由R中的E1071封装的SVM功能构成,然后使用E-MTAB-4451的数据集来验证。在存活组中筛选了总共384次。 PPI网络分为4个模块(模块A,B,C和D),涉及11次,包括微管相关蛋白1轻链3α(MAP1LC3A),蛋白激酶C-α(PRKCA),转移相关的1个家庭成员3 (MTA3)和涂抹平面细胞极性蛋白(SCRIB)。模块B中的SCRIB和PRKCA,以及模块D中的MAP1LC3A和MTA3,可能通过PPI在SEPSIS中起作用。功能性富集证明了模块D中的MAP1LC3A富含自噬芳瓦组件。最后,SVM分类器可以正确地识别E-MTAB-4451中的样本。总之,诸如MAP1LC3A,PRKCA,MTA3和SCRIB等参数可以涉及败血症的进展,并且需要进一步彻底的确认。

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