Identification of candidate genes for necrotizing enterocolitis based on microarray data

Guanglin Chen; Yang Li; Yang Su; Lingling Zhou; Hua Zhang; Qiyang Shen; Chunxia Du; Hongxing Li; Zechao Wen; Yankai Xia; Weibing Tang

doi:10.1016/j.gene.2018.03.088

Identification of candidate genes for necrotizing enterocolitis based on microarray data

Gene. 2018 Jun 30:661:152-159. doi: 10.1016/j.gene.2018.03.088. Epub 2018 Mar 29.

Authors

Guanglin Chen¹, Yang Li¹, Yang Su¹, Lingling Zhou¹, Hua Zhang¹, Qiyang Shen¹, Chunxia Du¹, Hongxing Li¹, Zechao Wen¹, Yankai Xia², Weibing Tang³

Affiliations

¹ State Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Department of Pediatric Surgery, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
² State Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology (Nanjing Medical University), Ministry of Education, China.
³ State Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Department of Pediatric Surgery, Children's Hospital of Nanjing Medical University, Nanjing 210008, China. Electronic address: twbcn@njmu.edu.cn.

PMID: 29605607
DOI: 10.1016/j.gene.2018.03.088

Abstract

Necrotizing enterocolitis (NEC) is one of the most serious diseases that could threaten the life of neonates. However the current opinions about the pathogenesis or how to prevent or treat the disease are still ambiguous. The purpose of the present study was to identify the key genes of this disease and provide new insights into the mechanism of NEC. The gene expression data of GSE46619, including 5 specimens from NEC patients and 4 samples from surgical-control infants, were collected from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with regard to NEC versus surgical-control group using Limma package in R software and Gene Ontology (GO) enrichment analysis and pathway enrichment analysis were conducted by means of Database for Annotation, Visualization and Integrated Discovery (DAVID) website subsequently. Furthermore the protein-protein interaction (PPI) network for DEGs was constructed using Cytoscape software and the most highly connected module was extracted using MCODE plugin from the PPI network. Moreover, the significantly enriched sub-pathways were identified using iSubpathwayMiner package in R software. A total of 2629 DEGs were screened out between NEC and control samples, including 367 up-regulated genes and 2262 down-regulated genes and they involved in different GO terms and pathways which may be associated with NEC onset and progression. PPI network and module analysis revealed that several genes were defined as hub genes including AGT, IL8 and KNG1. The sub-pathway analysis screened out 189 significantly enriched sub-pathways, including Tryptophan metabolism, Fatty acid metabolism, and Arachidonic acid metabolism. Genes in the corresponding sub-pathway, such as ACACB and CAT were regarded as critical genes in NEC. QRT-PCR was also conducted to identify the expression of the five key genes (AGT, IL8, KNG1, ACACB and CAT) in NEC samples. These findings have identified several hub genes (e.g., AGT, IL8, KNG1, ACACB and CAT) which were presumed to serve critical roles in NEC.

Keywords: Differentially-expressed genes; Module analysis; Necrotizing enterocolitis; Protein-protein interaction network; Sub-pathway analysis.

MeSH terms

Computational Biology / methods*
Enterocolitis, Necrotizing / genetics*
Enterocolitis, Necrotizing / pathology
Gene Expression Profiling / statistics & numerical data
Gene Regulatory Networks
Genetic Association Studies / methods*
Humans
Infant, Newborn
Intestine, Small / metabolism
Intestine, Small / pathology
Metabolic Networks and Pathways / genetics
Microarray Analysis / statistics & numerical data
Protein Interaction Maps