Multivariate gene expression-based survival predictor model in esophageal adenocarcinoma.

ABSTRACT:

Background

Despite the recent development of molecular-targeted treatment and immunotherapy, survival of patients with esophageal adenocarcinoma (EAC) with poor prognosis is still poor due to lack of an effective biomarker. In this study, we aimed to explore the ceRNA and construct a multivariate gene expression predictor model using data from The Cancer Genome Atlas (TCGA) to predict the prognosis of EAC patients.

Methods

We conducted differential expression analysis using mRNA, miRNA and lncRNA transciptome data from EAC and normal patients as well as corresponding clinical information from TCGA database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of those unique differentially expressed mRNAs using the Integrate Discovery Da

SUBMITTER: Zhao M 

PROVIDER: S-EPMC7529573 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature