ArticleCancer cell international2020
A methylation-based mRNA signature predicts survival in patients with gastric cancer.
Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer.International journal of molecular sciences · 2024Review
- Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.Cancers · 2023Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEvidence suggests that altered DNA methylation plays a causative role in the occurrence, progression and prognosis of gastric cancer (GC). Thus, methylated-differentially expressed genes (MDEGs) could potentially serve as biomarkers and therapeutic targets in GC.
methodsFour genomics profiling datasets were used to identify MDEGs. Gene Ontology enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis were used to explore the biological roles of MDEGs in GC. Univariate Cox and LASSO analysis were used to identify survival-related MDEGs and to construct a MDEGs-based signature. The prognostic performance was evaluated in two independent cohorts.
resultsWe identified a total of 255 MDEGs, including 192 hypermethylation-low expression and 63 Hypomethylation-high expression genes. The univariate Cox regression analysis showed that 83 MDEGs were associated with overall survival. Further we constructed an eight-MDEGs signature that was independent predictive of prognosis in the training cohort. By applying the eight-MDEGs signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significantly different overall survival (HR = 2.62, 95% CI 1.71-4.02, P < 0.0001). The prognostic value of the eight-MDEGs signature was confirmed in another independent GEO cohort (HR = 1.35, 95% CI 1.03-1.78, P = 0.0302) and TCGA-GC cohort (HR = 1.85, 95% CI 1.16-2.94, P = 0.0084). Multivariate cox regression analysis proved the eight-MDEGs signature was an independent prognostic factor for GC.
conclusionWe have thus established an innovative eight-MDEGs signature that is predictive of overall survival and could be a potentially useful guide for personalized treatment of GC patients.
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