Development of a prognostic model for early-stage gastric cancer-related DNA methylation-driven genes and analysis of immune landscape

Abstract

Background and aims: This study aimed to develop a prognostic model based on DNA methylation-driven genes for patients with early-stage gastric cancer and to examine immune infiltration and function across varying risk levels.

Methods: We analyzed data from stage I/II gastric cancer patients in The Cancer Genome Atlas which included clinical details, mRNA expression profiles, and level 3 DNA methylation array data. Using the empirical Bayes method of the limma package, we identified differentially expressed genes (DEGs), and the MethylMix package facilitated the identification of DNA methylation-driven genes (DMGs). Univariate Cox regression and LASSO (least absolute shrinkage and selector operation) analyses were utilized to pinpoint critical genes. A risk score prediction model was formulated using two genes that demonstrated the most significant hazard ratios (HRs). Model performance was evaluated within the initial cohort and verified in the GSE84437 cohort; a nomogram was also constructed based on these genes. We further examined 50 methylation sites associated with three CpG islands in C1orf35 and 14 methylation sites linked to one CpG island in FAAH. The CIBERSORT package was employed to identify immune cell clusters in the prediction model.

Results: A total of 176 DNA methylation-driven genes were refined down to a four-gene signature (ZC3H12A was hypermethylated; GATA3, C1orf35, and FAAH were hypomethylated), which exhibited a significant correlation with overall survival (OS), as evidenced by p-values below 0.05 following univariate Cox regression and LASSO analysis. Specifically, for the risk score prediction model, C1orf35, which had the highest hazard ratio (HR = 2.035, p = 0.028), and FAAH, with the lowest hazard ratio (HR = 0.656, p = 0.012), were selected. The Kaplan-Meier analysis demonstrated distinct survival outcomes between the high-risk and low-risk score groups. The model's predictive accuracy was confirmed with an area under the curve (AUC) of 0.611 for 3-year survival and 0.564 for 5-year survival. Notably, the hypomethylation of the three CpG islands in C1orf35 and the single CpG island in FAAH was significantly different in stage I/II gastric cancer patients compared to normal tissues. Additionally, the high-risk score group showed a notable association with resting CD4 memory T cells.

Conclusion: Promoter hypomethylation of C1orf35 and FAAH in early-stage gastric cancer underscores their potential as biomarkers for accurate diagnosis and treatment. The developed predictive model employing genes affected by DNA methylation serves as a crucial independent prognostic factor in early-stage gastric cancer.

Keywords: C1orf35; DNA methylation-driven gene; FAAH; gastric cancer; prognosis.

FIGURE 1

Methodological flowchart of the investigation.

FIGURE 2

Texture feature selection and two-gene risk score model construction in the TCGA cohort. (A) Results of univariate analyses of DNA methylation-driven genes. (B) Identification of hallmark genes using LASSO regression. (C) mRNA expression and DNA methylation levels of the four DNA methylation-driven genes. (D) Regression analysis of the relationship between mRNA and DNA methylation levels of FAAH and C1orf35. (E) Differential methylation statuses of FAAH and C1orf35, depicted through histograms highlighting the distribution of methylation in gastric cancer (GC) samples. Beta values indicate the methylation level, ranging from 0 to 1, with the horizontal black bar representing the distribution of methylation values in non-tumorous gastric samples. (F) Heatmap of DNA methylation-driven genes.

FIGURE 3

(A) Kaplan–Meier survival curve of DNA methylation-driven gene FAAH and C1orf35. (C) Heatmap and distribution of the two gene expression profiles in the high-risk and low-risk subgroups in the TCGA database. (D) Heatmap and distribution of the two gene expression profiles in the high-risk and low-risk subgroups in the GEO database. (E) Kaplan–Meier survival curve of DNA methylation-driven gene-based risk score prediction model and time-dependent ROC in the TCGA database. (F) Kaplan–Meier survival curve of DNA methylation-driven gene-based risk score prediction model and time-dependent ROC in the GEO database. (G) Nomogram for predicting the probability of 1-, 3-, and 5-year survival times for patients with stage I/II GC. (H) Calibration curve for the risk score model in the validation cohort. The dotted line represents the ideal predictive model, and the solid line represents the observed model.

FIGURE 4

Heatmap target T vs. N. no cluster. (A) (B) (C) The heatmap shows the differential methylation sites on the three CPG islands of the C1orf35 gene and (D) methylation sites on the 1 FAAH CpG islands in stage I/II GC patients (T: tumor; N: Normal). (E) C1orf35 and FAAH island methylation level in stage I/II GC patients.

FIGURE 5

(A) Differential expression of immune cell sets by ssGSEA between tumor and normal samples in the high-risk score group. Rose represents tumor, and cyan represents normal. (B) Differential expression of immune cell sets by ssGSEA between tumor and normal samples in the low-risk score group. Rose represents tumor, and cyan represents normal. (C) Differential expression of immune cell sets by ssGSEA between high-risk score and low-risk score groups. Red represents the high-risk score group, and green represents the low-risk score group. (D) Positive correlation between ssGSEA scores of immune cells and risk score in the high-risk score group. (E) Positive correlation between ssGSEA scores of immune cells and risk score in the low-risk score group. (F) Venn diagram illustrating the overlap of immune cells between high-risk score and low-risk score groups. (G) Cnetplot depicting the network of marker genes from these pathways in the high-risk score group. Colored points indicate corresponding pathways. (H) Enrichment plots of the top five KEGG pathways in the high-risk score and low-risk score groups for stage I/II GC.