ArticleBMC cancer2021
DNA methylation signatures associated with prognosis of gastric cancer.
Article in BMC cancer, 2021. 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, 19 citations in OpenAlex.
- Comprehensive Analysis of Glycosyltransferase-Related Genes Reveals their Prognostic and Therapeutic Implications in Stomach Adenocarcinoma.Current protein & peptide science · 2026Article
- The role of RGS12 in tissue repair and human diseases.Genes & diseases · 2025Review
- Methylation-regulated tumor suppressor gene PDE7B promotes HCC invasion and metastasis through the PI3K/AKT signaling pathway.BMC cancer · 2024Article
- Article
- The Mechanism of DNA Methylation and miRNA in Breast Cancer.International journal of molecular sciences · 2023Review
- Analysis of a Four-Component Competing Endogenous RNA Network Reveals Potential Biomarkers in Gastric Cancer: An Integrated Systems Biology and Experimental Investigation.Advanced biomedical research · 2023Article
- Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.Journal of computational biology : a journal of computational molecular cell biology · 2022Review
- TFPI inhibits breast cancer progression by suppressing ERK/p38 MAPK signaling pathway.Genes & genomics · 2022Article
- Epithelial-mesenchymal transition-related long noncoding RNAs in gastric carcinoma.Frontiers in molecular biosciences · 2022Review
- Association ofFrontiers in genetics · 2022Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFew studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.
methodsBased on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.
resultsWe newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed.
conclusionsWe newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.
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