Basic Study
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 21, 2018; 24(11): 1206-1215
Published online Mar 21, 2018. doi: 10.3748/wjg.v24.i11.1206
Three-microRNA signature identified by bioinformatics analysis predicts prognosis of gastric cancer patients
Cheng Zhang, Chun-Dong Zhang, Ming-Hui Ma, Dong-Qiu Dai
Cheng Zhang, Chun-Dong Zhang, Ming-Hui Ma, Dong-Qiu Dai, Department of Gastroenterological Surgery, the Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
Author contributions: Dai DQ designed this study; Zhang C, Zhang CD, and Ma MH conducted the data analysis; Zhang C wrote the article.
Supported by National Natural Science Foundation of China, No. 30572162; and Natural Science Foundation of Liaoning Province, No. 201602817.
Conflict-of-interest statement: The authors declare that there is no conflict of interest related to this study.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dong-Qiu Dai, PhD, Chief Doctor, Professor, Surgical Oncologist, Department of Gastroenterological Surgery, the Fourth Affiliated Hospital of China Medical University, 4 Chongshan Road, Shenyang 110032, Liaoning Province, China. daidq63@163.com
Telephone: +86-24-62043110 Fax: +86-24-62043110
Received: December 9, 2017
Peer-review started: December 10, 2017
First decision: December 21, 2017
Revised: December 25, 2017
Accepted: January 16, 2018
Article in press: January 16, 2018
Published online: March 21, 2018
Abstract
AIM

To identify multiple microRNAs (miRNAs) for predicting the prognosis of gastric cancer (GC) patients by bioinformatics analysis.

METHODS

The original microarray dataset GSE93415, which included 20 GC and 20 tumor adjacent normal gastric mucosal tissues, was downloaded from the Gene Expression Omnibus database and used for screening differentially expressed miRNAs (DEMs). The cut-off criteria were P < 0.05 and fold change > 2.0. In addition, we acquired the miRNA expression profiles and clinical information of 361 GC patients from The Cancer Genome Atlas database to assess the prognostic role of the DEMs. The target genes of miRNAs were predicted using TargetScan, miRDB, miRWalk, and DIANA, and then the common target genes were selected for functional enrichment analysis.

RESULTS

A total of 110 DEMs including 19 up-regulated and 91 down-regulated miRNAs were identified between 20 pairs of GC and tumor adjacent normal tissues, and the Kaplan-Meier survival analysis found that a three-miRNA signature (miR-145-3p, miR-125b-5p, and miR-99a-5p) had an obvious correlation with the survival of GC patients. Furthermore, univariate and multivariate Cox regression analyses indicated that the three-miRNA signature could be a significant prognostic marker in GC patients. The common target genes of the three miRNAs are added up to 108 and used for Gene Functional Enrichment analysis. Biological Process and Molecular Function analyses showed that the target genes are involved in cell recognition, gene silencing and nucleic acid binding, transcription factor activity, and transmembrane receptor activity. Cellular Component analysis revealed that the genes are portion of nucleus, chromatin silencing complex, and TORC1/2 complex. Biological Pathway analysis indicated that the genes participate in several cancer-related pathways, such as the focal adhesion, PI3K, and mTOR signaling pathways.

CONCLUSION

This study justified that a three-miRNA signature could play a role in predicting the survival of GC patients.

Keywords: Gene functional enrichment, Prognosis, Bioinformatic analysis, Differentially expressed miRNAs, Gastric cancer

Core tip: We identified 110 differentially expressed miRNAs through mining the datasets of Gene Expression Omnibus database and acquired the miRNA expression profiles and clinical information of 361 gastric cancer (GC) patients from The Cancer Genome Atlas database. Multiple miRNAs together acting as biomarkers may have a stronger reliability in survival prediction. Our study found that a novel three-miRNA signature could be used for predicting the prognosis of GC patients.