ArticleThoracic cancer2020
Multivariate gene expression-based survival predictor model in esophageal adenocarcinoma.
Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Model construction and risk analysis of the lncRNA genes associated with the prognosis of esophageal adenocarcinoma with immune infiltration.Journal of gastrointestinal oncology · 2023Article
- Systems biology and OMIC data integration to understand gastrointestinal cancers.World journal of clinical oncology · 2022Review
- Prognostic score model based on six m6A-related autophagy genes for predicting survival in esophageal squamous cell carcinoma.Journal of clinical laboratory analysis · 2022Article
- Article
- Integrated analysis of competing endogenous RNA in esophageal carcinoma.Journal of gastrointestinal oncology · 2021Article
- Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma.Journal of oncology · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDespite 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.
methodsWe 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.
resultsWe 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.
conclusionsIn 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.
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