Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus. Issue 6 (19th March 2018)

• Record Type: Journal Article
• Authors: Lv, Jing; Liu, Jijun; Guo, Lei; Zhang, Jun; Cheng, Yan; Chen, Chu; Zhao, Heping; Wang, Jihan
• Is Part Of: Cell cycle. Volume 17:Issue 6(2018)
• Page Start: 792
• Page End: 800
• Publication Date: 2018-03-19
• Subjects: Barrett's esophagus -- bioinformatic analyses -- differentially expressed genes -- microarray -- microRNA; Cell cycle -- Periodicals; 571.84377
• DOI: 10.1080/15384101.2018.1431597
• Languages: English
• ISSNs: 1538-4101

Abstract

ABSTRACT: Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.