Key genes affecting outcome of sepsis: identification using weighted gene co-expression network analysis
10.3760/cma.j.issn.0254-1416.2020.02.024
- VernacularTitle:影响脓毒症预后的关键基因：采用加权基因共表达网络分析法筛选
- Author:
Lifeng DING
1
;
Shuyuan XIAO
;
Yan ZHANG
;
Xiangming FANG
Author Information
1. 浙江大学医学院附属第一医院麻醉科，杭州 310003
- From:
Chinese Journal of Anesthesiology
2020;40(2):221-224
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the key genes affecting the outcome of sepsis using weighted gene co-expression network analysis.Methods:The peripheral blood gene chip data GSE54514 from septic patients and healthy volunteers were obtained from the gene expression database of the American Center for Biotechnology Information.An R package for weighted gene co-expression network analysis was used to construct a co-expression network of differentially expressed genes between sepsis patients and healthy volunteers to identify key modules associated with the outcome of sepsis.Then gene functional enrichment analysis was performed to figure out the possible behavior of genes in the most significant modulerelated tooutcomes of sepsis.Hub genes were selected from the most significant module according to module membership and degree of protein-protein interaction network.Results:A total of 622 differentially expressed genes identified from the microarray data of GSE36895 in septic patients and healthy volunteers were used to construct a co-expression network, and the module with the most significant correlation with the outcome of sepsis was identified.GO enrichment analysis showed that the genes in this module were related to activation of myeloid cells and neutrophils, however, the KEGG pathway enrichment analysis showed that these genes played an important role in virus infection processes.Fifteen hub genes were finally selected from the module with the most significant correlation with the outcome of sepsis by constructing a protein-protein interaction network.Conclusion:Fifteen key genes related to the outcome of sepsis are identified via bioinformatics methods, and the mechanism is related to regulating the immune response to infection.
