DNA methylation signatures associated with prognosis of gastric cancer

Abstract

Background Few 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. Methods Based 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. Results We 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. Conclusions We 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.

Association of FLG mutation with tumor mutation load and clinical outcomes in patients with gastric cancer

“…KEGG pathway enrichment analysis reveals that DEGs were mainly involved in several pathways such as neuroactive ligand–receptor interaction, protein digestion and absorption, and chemical carcinogenesis. The neuroactive ligand–receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis ( Dai et al, 2021 ). The protein digestion and absorption pathway was found to be significantly enriched in GC tissues ( Liu et al, 2020 ).…”

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

“…One example of using filter methods for feature selection is that Dai et al ( 2021 ) conducted an epigenome-wide association study (EWAS) to screen DNA methylation markers associated with OS, DSS, PFI, and DFI. To choose optimal CpG sites for cancer prognosis prediction, Li et al ( 2021 ) applied sure independence screening (SIS) (Fan and Lv, 2008 ), which selects predictors based on their correlations with the response.…”

“…The Cox proportional hazards regression model is the most popular method for estimating the coefficients in a risk prediction model. Incorporating the screened prognosis-associated CpGs, a risk score model was used to calculate a weighted sum of methylation levels for each patient based on the regression coefficients of the Cox model (Hu and Zhou, 2017 ; Xu et al, 2017 ; Guo et al, 2019 , 2021; Yang et al, 2019 ; Luo et al, 2020 ; Peng et al, 2020 ; Zhang et al, 2020 ; Dai et al, 2021 ; Li et al, 2021 ).…”

“…Quantitatively, formal statistical tests are required to test whether the difference is statistically significant (Clark et al, 2003a ). The logrank test (Peto et al, 1977 ) is the most popular method for comparing the survival probabilities among different risk score groups classified by cutoff points (Hao et al, 2017 ; Hu and Zhou, 2017 ; Xu et al, 2017 ; Guo et al, 2019 , 2021; Jiang et al, 2020 ; Luo et al, 2020 ; Zhang et al, 2020 ; Dai et al, 2021 ; Feng et al, 2021 ; Li et al, 2021 ; Yin et al, 2021 ; Zuccato et al, 2021 ). The evaluation metric is the p -value of the test.…”

“…It is, therefore, reasonable to combine methylation signatures with clinicopathological information to improve the prognostic estimation (Luo et al, 2020 ). Adding clinical factors to the model can also adjust for potential confounders such as patient age (Dai et al, 2021 ; Zuccato et al, 2021 ).…”