Identification of candidate genes for necrotizing enterocolitis based on microarray data

• Published in: Gene Vol. 661; pp. 152 - 159
• Main Authors: Chen, Guanglin, Li, Yang, Su, Yang, Zhou, Lingling, Zhang, Hua, Shen, Qiyang, Du, Chunxia, Li, Hongxing, Wen, Zechao, Xia, Yankai, Tang, Weibing
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.06.2018
• Subjects: Computational Biology - methods; Differentially-expressed genes; 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; Module analysis; Necrotizing enterocolitis; Protein Interaction Maps; Protein-protein interaction network; Sub-pathway analysis; DEGs; Protein-protein interaction network; Sub-pathway analysis; Differentially-expressed genes; GO; PPI; KEGG; DAVID; STRING; Necrotizing enterocolitis; Module analysis; NEC
• ISSN: 0378-1119; 1879-0038
• DOI: 10.1016/j.gene.2018.03.088

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. •Differentially expressed genes were determined with data from GEO database between NEC and control tissues.•Several key genes were identified from the PPI network and risk sub-pathway information.•Candidate key genes expression were confirmed in clinical samples for further validation.