Abstract: Objective To investigate the differentiating genes of Septic cardiomyopathy （SCM） and to screen potential targets related to SCM, thus providing a new approach for its diagnosis and treatment. Methods GSE79962 gene chip was downloaded from GEO database, and the gene expression difference between SCM patients and control group （Nonfailing, NF） was screened by NetworkAnalyst online analysis tool. Differentially Expressed Genes （DEGs） were analyzed. Metascape online database and String website were used for enrichment analysis and Protein Interaction to construct PPI network map, and key genes and functional modules were screened out. Results A total of 390 DEGs were screened, 228 up-regulated genes and 162 down-regulated genes were included. Gene analysis indicated that DEGs mainly enriched in positive regulation of inflammatory response, positive regulation of smooth muscle cell proliferation and platelet threshing during the Biological Process （BP）. In Cellular Component （CC）, CC is mainly concentrated in platelet α granule lumen, extracellular region, protein extracellular matrix, etc. In Molecular Function, MF mainly concentrates in heme binding, interleukin-1 receptor activity, serine endopeptidase inhibitor activity, etc. Pathway analysis indicated that DEGs were mainly enriched in HIF-1 signaling pathway, JAK-STAT signaling pathway and insulin resistance. Two significant interaction modules and 10 key genes were screened by Metascape online database, which were STAT3, SERPINE1, TIMP1, TP53, ICAM1, IGF1, MYC, THBS1, HMOX1 and ADIPOQ. Conclusion DEGs in SCM is related to the occurrence and development of disease. Enrichment analysis and key gene screening provide a new idea for further exploring the pathogenesis and therapeutic target of SCM.

Key words: septic Cardiomyopathy, differentially expressed genes, bioinformatics analysis, GEO database

摘要： 目的 利用脓毒症心肌病（SCM）的差异性基因,检测与SCM有关的潜在靶点,为寻找最新的诊断方法与疗法提供新途径。方法 从美国GEO数据库上下载GSE79962基因芯片,并使用NetworkAnalyst的网络大数据分析技术工具分析SCM病人和对照组患者（NF）所表达的差异基因,并将结果可视化。将不同差异基因（DEGs）经过分析,使用Metascape网络数据库系统和String网站对其经过富集分析及蛋白质互助作用构建PPI网络图,从而检测出重要基因组和功能模块。结果 一共筛选出了390个DEGs,包括上升基因288个,下降基因162个。而基因组本体分析则表明,DEGs在生物过程学中一般富集在对药物反应、对血栓形成的积极调控、对细菌繁殖的负性调控等;在细菌组成方面一般是富集于细胞外区段、细胞外间隙、浆膜的整体成分等;在分子物质作用功能则一般是富集于与蛋白质的融合、钙离子融合、受体结合等。通路分析提示,DEGs主要富集于PI3K-Akt信号通路、胰岛素抵抗等。利用Cytoscape的在线数据库,检测出了2种显著作用模块和10个重要基因,它们为STAT3、SERPINE1、TIMP1、TP53、ICAM1、IGF1、MYC、THBS1、HMOX1、ADIPOQ。结论 DEGs与SCM的发生进展密切相关,为更深入研究SCM的发病机制和早期鉴定提供了新思路。

关键词: 脓毒症心肌病, 差异基因, 生物信息分析, GEO数据库