脓毒症患者骨骼肌细胞基因标志物的特征分析  

Characteristic bioanalysis of skeletal muscle cells gene markers in septic patients

作　　者：田阔[1] 王健健[1] 刘佩芳[1] 张荟雪[1] 卢晓宇[1] 徐晨 王丽华 Tian Kuo;Wang Jianjian;Liu Peifang;Zhang Huixue;Lu Xiaoyu;Xu Chen(Stroke Center, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China Wang Jianjian)

机构地区：[1]哈尔滨医科大学附属第二医院卒中中心,黑龙江哈尔滨150001 [2]不详

出　　处：《中华危重病急救医学》2019年第3期293-297,共5页Chinese Critical Care Medicine

基　　金：国家自然科学基金(81771361,81571166).

摘　　要：目的利用生物信息学方法分析脓毒症患者骨骼肌细胞基因标志物的特征。方法从美国国立生物技术信息中心(NCBI)的基因表达数据库(GEO)中获取脓毒症患者骨骼肌纤维组织基因芯片表达数据集(GSE13205),利用NCBI网站提供的在线R语言分析工具(GEO2R)进行基因差异表达分析,用在线生物信息队列研究工具(BART)和美国国家网络生物学资源项目软件Cytoscpe进行数据处理、分析并绘图;应用DAVID基因数据库中的京都基因与基因组百科全书(KEGG)通路富集方法和基因本体(GO)功能富集方法对差异表达基因进行在线通路富集分析和GO功能分析;并进一步应用基因与蛋白质相互作用检索数据库(STRING-DB)进行基因数据集相关蛋白质的相互作用分析。结果在GSE13205数据集中提取前250个基因(TOP250),共242个差异表达基因纳入分析,其中上调基因78个,下调基因164个。这些差异表达基因被富集到不同的生物学过程或分子功能的子集中,其中生物学过程主要富集在生长的正负向调控和矿物质的吸收等通路上。在蛋白质相互作用网络图中识别到14个最为密切相关的差异表达基因。结论脓毒症患者差异表达基因主要集中在控制细胞生长凋亡以及介导肿瘤相关免疫功能异常的自身调节等方面。Objective To analyze the characteristics of skeletal muscle cells gene markers in septic patients by using bioinformatics. Methods The differential gene expression of marker microarrays (GSE13205) in skeletal muscle tissue of patients with sepsis was obtained from gene expression omnibus (GEO) database of National Center for Biotechnology Information (NCBI). Gene differential expression analysis was carried out using online GEO2R provided by NCBI. Data processing, analysis and mapping were carried out using online bioinformatics array research tool (BART) and Cytoscpe software, the software of the national resource for network biology. Functional annotation and pathway analysis of differential expression genes were performed using Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) provided by the database for annotation, visualization and integrated discovery (DAVID), and protein interaction analysis was further performed in search tool for the retrieve of interacting genes/proteins (STRING-DB). Results The TOP250 genes were extracted from the GSE13205 dataset. A total of 242 differentially expressed genes were included in the analysis. Among them, 78 up-regulated genes and 164 down-regulated genes were identified. After extensive data analysis, these differentially expressed genes were enriched into different biological processes or subsets of molecular functions, mainly enriched in the positive and negative regulation of growth, mineral absorption and other pathways. The 14 most closely related genes among differentially expressed genes were identified from the protein interaction network. Conclusion The differential expression genes in patients with sepsis were mainly concentrated on cell growth and apoptosis and mediating tumor-related immune function regulation.

关 键 词：脓毒症 基因芯片 生物信息学 生物标志物 

分 类 号：R459.7[医药卫生—急诊医学]