Open AccessMultivariate gene expression‐based survival predictor model in esophageal adenocarcinoma
Author(s) -
Zhao Maoyuan,
Wang Jingsong,
Yuan Meng,
Ma Zeliang,
Bao Yongxin,
Hui Zhouguang
Publication year - 2020
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.13626
Subject(s) - competing endogenous rna , kegg , survival analysis , microrna , proportional hazards model , medicine , oncology , gene , computational biology , multivariate analysis , gene expression , multivariate statistics , biomarker discovery , bioinformatics , biology , gene ontology , rna , long non coding rna , genetics , proteomics , machine learning , computer science
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 Database (DAVID) database. We then constructed the lncRNA‐miRNA‐mRNA competing endogenous RNA (ceRNA) network of EAC and used Cox proportional hazard analysis to generate a multivariate gene expression predictor model. We finally performed survival analysis to determine the effect of differentially expressed mRNA on patients' overall survival and discover the hub gene. Results We identified a total of 488 lncRNAs, 33 miRNAs, and 1207 mRNAs with differentially expressed profiles. Cox proportional hazard analysis and survival analysis using the ceRNA network revealed four genes (IL‐11, PDGFD, NPTX1, ITPR1) as potential biomarkers of EAC prognosis in our predictor model, and IL‐11 was identified as an independent prognostic factor. Conclusions In conclusion, we identified differences in the ceRNA regulatory networks and constructed a four–gene expression‐based survival predictor model, which could be referential for future clinical research.