基因生物信息学的脓毒症潜在发病机制研究及生物标记物筛选

Exploring potential pathogenesis and screening biomarkers for sepsis based on bioinformatics

目的:全方位了解脓毒症发生发展机制,从而筛选相关核心基因,为临床治疗脓毒症提供新靶点。方法:从基因表达数据库(GEO)中获取GSE28750芯片数据,使用GCBI在线实验室筛选出差异表达基因,分别使用基因本体论分析(GO分析)、代谢通路分析(pathway分析)、基因信号网络分析、共表达分析对所获得基因进行分析。结果:与对照组相比,脓毒症组共获得2 457个差异表达基因,其中上调基因1 282个、下调基因1 175个;差异表达基因主要涉及免疫反应、细胞分化、血液凝固等方面;pathway分析发现核心信号通路主要涉及物质代谢、信号转导、抗感染等方面;基因信号网络分析发现核心基因为:GNAI3、PIK3CB、MAPK14、IL8;共表达网络分析推测核心基因为GYG1、SERPINB1、SAMSN1、ATP11B。结论:生物信息学有助于全面深入研究疾病发生机制,筛选可能的核心靶点,为临床治疗脓毒症提供参考。

Objective:To comprehensively understand the pathogenesis of sepsis and subsequently screen outcore genes,and to provide new therapeutic targets for sepsis in clinical practice.Methods:The data of GSE28750 chip were obtained from the Gene Expression Omnibus(GEO). Then, the differentially expressed genes werescreened out by the GCBI online laboratory and analyzed using gene ontology analysis(GO),metabolic pathwayanalysis(pathway analysis),gene network analysis,and co-expression analysis.Results:Compared with the controlgroup,the sepsis group obtained 2 457 differentially expressed genes,including 1 282 up-regulated genes and 1 175 down-regulated genes. These differentially expressed genes were mainly involved in immune response, celldifferentiation,blood coagulation,and so on. Pathway analysis suggested that the core signaling pathways weremainly involved in substance metabolism,signal transduction,anti-infection,and other aspects. Further,genenetwork analysis pointed out several core genes,including GNAI3,PIK3 CB,MAPK14,and IL8. Co-expressionnetwork analysis also speculated about the core genes,including GYG1,SERPINB1,SAMSN1,and ATP11 B.Conclusion:Bioinformatics contributes to comprehensively understanding the pathogenesis of sepsis and screen outthe potential therapeutic targets,which canprovide us a new insight into the treatment of sepsis in clinical practice.