ArticleOncoTargets and therapy2018
Identification of prognostic risk factors for esophageal adenocarcinoma using bioinformatics analysis.
Article in OncoTargets and therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 24 citations in OpenAlex.
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- A new pyroptosis-related signature for predicting the immune status and injury of liver ischemia-reperfusion.American journal of translational research · 2022Article
- High expression of SLC20A1 is less effective for endocrine therapy and predicts late recurrence in ER-positive breast cancer.PloS one · 2022Article
- Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis.Journal of orthopaedic surgery and research · 2021Article
- A Five-mRNA Expression Signature to Predict Survival in Oral Squamous Cell Carcinoma by Integrated Bioinformatic Analyses.Genetic testing and molecular biomarkers · 2021Article
- Prognostic value of microRNA-378 in esophageal cancer and its regulatory effect on tumor progression.Experimental and therapeutic medicine · 2021Article
- Identifying the hub gene and immune infiltration of osteoarthritis by bioinformatical methods.Clinical rheumatology · 2021Article
- Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma.Journal of oncology · 2021Article
- A glycolysis-related gene signature predicts prognosis of patients with esophageal adenocarcinoma.Aging · 2020Article
- Multivariate gene expression-based survival predictor model in esophageal adenocarcinoma.Thoracic cancer · 2020Article
- Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis.BioMed research international · 2020Article
- Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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,
conclusionThese prognosis-related genes and differentially expressed miRNA have provided potential biomarkers for EAC diagnosis and treatment.
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