A comprehensive bioinformatics method to screen key genes for severe burn. (April 2016)

• Record Type: Journal Article
• Authors: Nong, Qingwen; Li, Shuntang; Liu, Daen
• Is Part Of: European journal of inflammation. Volume 14:Number 1(2016)
• Page Start: 18
• Page End: 26
• Publication Date: 2016-04
• Subjects: Burn injury -- differentially expressed genes -- enrichment analysis -- inflammatory responses -- protein-protein interaction network; Inflammation -- Periodicals; Anti-Inflammatory Agents -- therapeutic use -- Periodicals; Immunotherapy -- Periodicals; Inflammation -- Periodicals; Anti-inflammatory agents -- Periodicals; Immunotherapy -- Periodicals; Anti-inflammatory agents; Immunotherapy; Inflammation; Periodicals; 616.0473
• DOI: 10.1177/1721727X15622459
• Languages: English
• ISSNs: 1721-727X

Abstract

Severe burn is a kind of traumatic injury, which can lead to serious financial burden, high morbidity and mortality following inflammatory response and complications. Microarray analysis has emerged as a popular tool for generating gene expression data and revealing the complex gene expression patterns. The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information has become the leading public repository of microarray data. This study aimed to study the mechanisms of severe burn. Microarray of GSE37069 was downloaded from GEO including blood samples from 244 severe burn patients and blood samples from 35 healthy controls. The differentially expressed genes (DEGs) between blood samples from healthy controls and patients were identified by t-test in the LIMMA package of R. Their interaction relationships were searched using STRING online software and then protein-protein interaction (PPI) network was constructed by Cytoscape. Using DAVID online tool, underlying functions of the DEGs involved in the PPI network were analyzed by functional and pathway enrichment analyses. We screened 541 DEGs in blood samples from severe burns patients compared with blood samples from healthy controls. Meanwhile, DEGs including MMP9, TIMP1, GZMK, GZMA, and GZMB showed higher degrees in the PPI networks. Moreover, they may function in severe burn through interacting with others. According to functional enrichment analysis, these DEGs were mainly involved in BP categories associated with inflammatory responses. MMP9, TIMP1, HGF, GZMA, GZMB, and GZMK might play important roles in severe burns.