ArticleJournal of gastrointestinal oncology2022
Identification of a five m6A-relevant mRNAs signature and risk score for the prognostication of gastric cancer.
Article in Journal of gastrointestinal oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- An m6A-programmed cell death signature predicts prognosis and identifies STK25 as a therapeutic target in colon adenocarcinoma.Oncology letters · 2026Article
- Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.Scientific reports · 2025Article
- Review
- Potential Therapeutic Action of Autophagy in Gastric Cancer Managements: Novel Treatment Strategies and Pharmacological Interventions.Frontiers in pharmacology · 2021Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: N6-methyladenosine (m6A) is the most abundant form of methylation modification in eukaryotic cell messenger RNA (mRNA). However, the role of m6A in gastric cancer (GC), which is one of the most common gastrointestinal malignancies, is unclear. In this study, m6A-relevant mRNA signatures and risk scores were determined to predict the prognosis of GC. Methods: The expression profiles and clinical information of 367 patients were downloaded from The Cancer Genome Atlas (TCGA). Cluster analysis and univariate Cox analysis were performed to identify the regulatory factors of RNA methylation associated with GC prognosis. A co-expression network was constructed using the WGCNA package in R. The correlations between module eigengenes and clinical traits were then calculated to identify the relevant modules. We used univariate Cox analysis to screen for genes that are significantly associated with prognosis in the module. We identified hub genes by least absolute shrinkage and selection operator (LASSO) and multivariate analysis and developed a Cox prognostic model. Finally, the hub gene expression values weighted by the coefficients from the LASSO regression were applied to generate a risk score for each patient, and receiver operating characteristic (ROC) and Kaplan-Meier curves were used to assess the prognostic capacity of the risk scores. The asporin ( Results: Our results indicated that Conclusions: Our findings indicated that
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