ArticleTranslational cancer research2020
Gastric cancer risk-scoring system based on analysis of a competing endogenous RNA network.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Potential Alleviating Property Against NAFLD byFood science & nutrition · 2026Article
- Competing Endogenous RNA Networks Reveal Long Non-coding RNAs as Potential Prognostic Biomarkers in Gastric Cancer: A Systematic Review and Meta-Analysis.Advanced biomedical research · 2025Review
- The role of N6-methyladenosine modification in tumor angiogenesis.Frontiers in oncology · 2024Review
- Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer.Scientific reports · 2023Article
- Immune cell death-related lncRNA signature as a predictive factor of clinical outcomes and immune checkpoints in gastric cancer.Frontiers in pharmacology · 2023Article
- Article
- Stemness-related gene signature for predicting therapeutic response in patients with esophageal cancer.Translational cancer research · 2022Article
- Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma.Frontiers in genetics · 2022Article
- Genetic regulation ofFrontiers in endocrinology · 2022Article
- A novel risk model construction and immune landscape analysis of gastric cancer based on cuproptosis-related long noncoding RNAs.Frontiers in oncology · 2022Article
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4 authors.
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Abstract
backgroundLong noncoding RNAs (lncRNAs) can play vital roles in tumor initiation, progression, invasion, and metastasis. However, the functional role of the lncRNA-based competing endogenous RNA (ceRNA) networks in gastric cancer (GC) is still unclear. We aimed to identify novel lncRNAs and their association with GC prognosis.
methodsThe lncRNA, miRNA, and mRNA expression profiles of GC patients data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using the edge-R package. Then, the relationship among lncRNAs-miRNAs-mRNAs was integrated into a constructed ceRNA network with Cytoscape software. Using Cox regression analysis, a risk score system based on DEGs associated with patient prognosis in GC was established. Finally, a nomogram was founded to predict the prognosis of GC patients.
resultsA total of 971 differentially expressed lncRNAs (DElncRNAs), 144 differentially expressed miRNAs (DEmiRNAs) and 2,789 differentially expressed mRNAs (DEmRNAs) were identified and found to be associated with GC risk. Using the bioinformatics method, a ceRNA network involving 62 DElncRNAs, 21 DEmiRNAs and 59 DEmRNAs was constructed. Based on the results of the Cox regression analysis, a risk-scoring system involving 3 lncRNAs (i.e., ADAMTS9-AS1, C15orf54, and AL391152.1) was set up for the survival analysis of GC patients. The area under the receiver operating characteristic (ROC) curve for the risk-scoring system was 0.674, with a C-index of 0.64 [95% confidence interval (CI): 0.59-0.69, P=2.806485e-08]. Univariate and multivariate Cox regression analyses demonstrated that the risk-scoring system was an independent prognostic factor for GC. The risk-scoring system is positively associated with advanced tumor grade. The expression of these 3 lncRNAs were validated in GEPIA database. A nomogram based on these 3 lncRNAs was created to predict the prognosis of GC patients.
conclusionsOur study established a novel lncRNA-expression-based ceRNA network and an ADAMTS9-AS1-C15orf54-AL391152.1-based risk-scoring system, which can be used to predict the prognosis of GC patients.
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