Identification of Prognostic Risk Factors for Esophageal Adenocarcinoma Using Bioinformatics Analysis

Overview

Journal Onco Targets Ther

Publisher Dove Medical Press

Specialty Oncology

Date 2018 Aug 14

PMID 30100738

Citations 17

Authors

Zhiyu Dong

Junwen Wang

Tingting Zhan

Shuchang Xu

Affiliations

Soon will be listed here.

Abstract

Purpose: Esophageal adenocarcinoma (EAC) is the most common type of esophageal cancer in Western countries. It is usually detected at an advanced stage and has a poor prognosis. The aim of this study was to identify key genes and miRNAs in EAC.

Methods: The mRNA microarray data sets GSE1420, GSE26886, and GSE92396 and miRNA data set GSE16456 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were obtained using R software. Functional enrichment analysis was performed using the DAVID database. A protein-protein interaction (PPI) network and functional modules were established using the STRING database and visualized by Cytoscape. The targets of the DEMs were predicted using the miRecords database, and overlapping genes between DEGs and targets were identified. The prognosis-related overlapping genes were identified using Kaplan-Meier analysis and Cox proportional hazard analysis based on The Cancer Genome Atlas (TCGA) database. The differential expression of these prognosis-related genes was validated using the expression matrix in the TCGA database.

Results: Seven hundred and fifteen DEGs were obtained, consisting of 313 upregulated and 402 downregulated genes. The PPI network consisted of 281 nodes; 683 edges were constructed and 3 functional modules were established. Forty-four overlapping genes and 56 miRNA- mRNA pairs were identified. Five genes, , , , , and , were associated with overall survival or relapse-free survival. and were found to be independent prognostic indicators for overall survival, and , , and were considered independent prognostic indicators for relapse-free survival. Among them, the overexpression of and and lower expression of were also identified in EAC tissues based on the expression matrix in the TCGA database.

Conclusion: These prognosis-related genes and differentially expressed miRNA have provided potential biomarkers for EAC diagnosis and treatment.

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