ArticleBioMed research international2020
Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- MicroRNA Enrichment and Docking-Based Evaluation of Ilomastat Targeting of MMP-2 in Esophageal Squamous Cell Carcinoma: Insights from a South African Cohort.Computational and structural biotechnology journal · 2026Article
- Somatic Copy Number Alterations in Colorectal Cancer Lead to a Differentially Expressed ceRNA Network (ceRNet).Current issues in molecular biology · 2023Article
- Identification of the miRNA-mRNA regulatory network associated with radiosensitivity in esophageal cancer based on integrative analysis of the TCGA and GEO data.BMC medical genomics · 2022Article
- Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers.Frontiers in oncology · 2022Review
- MIR100HG Regulates CALD1 Gene Expression by Targeting miR-142-5p to Affect the Progression of Bladder Cancer CellsFrontiers in molecular biosciences · 2021Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundEsophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer.
methodsThe Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis.
resultsWe have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis.
conclusionsOur research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.
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