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

作者：Su Chen, Zeyang Lin, Zhijian Ye, Jing Liang, Rong Yu, Zheng Wan, Jingjing Hou · 发表于：Frontiers in Molecular Biosciences · 年份：2024 · DOI：10.3389/fmolb.2024.1455890 · 被引用次数：5 · 研究领域：Ferroptosis and cancer prognosis、Gastric Cancer Management and Outcomes、Epigenetics and DNA Methylation

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 surviv...