Identification of prognostic risk factors for esophageal adenocarcinoma using bioinformatics analysis

Abstract

PurposeEsophageal 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.MethodsThe 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.ResultsSeven 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, FAM46A, RAB15, SLC20A1, IL1A, and ACSL1, were associated with overall survival or relapse-free survival. FAM46A and IL1A were found to be independent prognostic indicators for overall survival, and FAM46A, RAB15, and SLC20A1 were considered independent prognostic indicators for relapse-free survival. Among them, the overexpression of RAB15 and SLC20A1 and lower expression of ACSL1 were also identified in EAC tissues based on the expression matrix in the TCGA database.ConclusionThese prognosis-related genes and differentially expressed miRNA have provided potential biomarkers for EAC diagnosis and treatment.

Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

“…Understanding the mechanisms underpinning OA development and progression will greatly contribute to the diagnosis, treatment, and prognosis of patients with this disease. Gene chip and high-throughput sequencing technologies can detect the expression levels of tens of millions of genes in humans and have been widely used to study the molecular mechanisms of various diseases, predict potential therapeutic targets, and find biomarkers (Dong et al, 2018; Liu et al, 2017a). Despite the numerous basic studies reporting on OA in recent years, the molecular mechanisms, early diagnosis, and epigenetic mechanisms of OA have not been elucidated because most of these studies have focused on the gene expression or pure methylation from a single cohort (Ren et al, 2018).…”

Multivariate gene expression‐based survival predictor model in esophageal adenocarcinoma

“…Also, Dong et al reported five genes that have a connection with overall or relapse-free survival. 16 However, the credibility of the result which shows the identification of DEGs in mRNA expression profiling data sets GSE1420, GSE26886, and GSE92396 was not high as the three data sets come from different platforms (GSE1420 from GPL96, GSE26886 from GPL570, GSE92396 from GPL6244).…”

“…There have been previous efforts to identify biomarkers for the prognosis of EAC. Some have proposed a predictor model of EAC using miRNA, 14,15 while others have conducted prognostic risk factor analysis based on the interaction between miRNA and mRNA 16 . However, to the best of our knowledge, there has not been any analysis using the lncRNA‐miRNA‐mRNA competing endogenous RNA (ceRNA) network for EAC, which provides a more reliable analysis.…”

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

“…Gene chip and high-throughput sequencing technologies can be used for detecting the gene expression, microRNA, long noncoding RNA, and DNA methylation to explore genetic alterations in disease [ 28 ]. Microarray techniques have also been widely used to predict potential target genes for OA [ 7 , 29 ].…”