ArticleBMC medical genomics2021
A predictive model for assessing prognostic risks in gastric cancer patients using gene expression and methylation data.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Metabolic Regulation of Immune Responses: Molecular Mechanisms, Diseases, and Therapeutic Targets.MedComm · 2026Review
- Epigenetic modulation of VEGF-A/VEGFR2 pathway genes in OC/TME axis driving genetic upregulation and tumor plasticity.Scientific reports · 2025Article
- The Identification of LA-tumor associated macrophages in immune modulation via amyloid-beta precursor protein/CD74 signal pathway in gastric cancer: a predictive module and machine learning.Frontiers in oncology · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe role(s) of epigenetic reprogramming in gastric cancer (GC) remain obscure. This study was designed to identify methylated gene markers with prognostic potential for GC.
methodsFive datasets containing gene expression and methylation profiles from GC samples were collected from the GEO database, and subjected to meta-analysis. All five datasets were subjected to quality control and then differentially expressed genes (DEGs) and differentially expressed methylation genes (DEMGs) were selected using MetaDE. Correlations between gene expression and methylation status were analysed using Pearson coefficient correlation. Then, enrichment analyses were conducted to identify signature genes that were significantly different at both the gene expression and methylation levels. Cox regression analyses were performed to identify clinical factors and these were combined with the signature genes to create a prognosis-related predictive model. This model was then evaluated for predictive accuracy and then validated using a validation dataset.
resultsThis study identified 1565 DEGs and 3754 DEMGs in total. Of these, 369 were differentially expressed at both the gene and methylation levels. We identified 12 signature genes including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5 which were combined with the clinical data to produce a novel prognostic model for GC. This model could effectively split GC patients into two groups, high- and low-risk with these observations being confirmed in the validation dataset.
conclusionThe differential methylation of the 12 signature genes, including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5, identified in this study may help to produce a functional predictive model for evaluating GC prognosis in clinical samples.
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