基于 DNA 甲基化驱动的差异表达基因构建胃癌预后列线图模型的建立与验证。

: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. : DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. : Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (, , , and were hypermethylated, and was hypomethylated), which was associated with OS ( < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. : The altered status of the DNAm-driven gene signature (, , , , and ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.

胃癌（GC）的发病率在常见肿瘤中排名第五，是全球癌症相关死亡的第三大原因。本研究旨在开发和验证预测 GC 患者总生存期（OS）的列线图。

通过整合来自癌症基因组图谱（TCGA）GC 队列的 DNA 甲基化（DNAm）和基因表达谱分析，鉴定出 DNAm 驱动基因。然后，基于 Kaplan-Meier（K-M）、最小绝对值收缩和选择操作（LASSO）和多变量 Cox 回归分析构建风险评分模型。分析临床参数后，构建并评估了列线图。另一个队列（GSE62254）用于外部验证。

从 TCGA 队列中筛选出 13 个差异表达的 DNAm 驱动基因，构建了一个由 6 个基因组成的特征基因集（、、、和呈高甲基化，呈低甲基化），该基因集与生存和 LASSO 回归分析后的 OS 相关（ < 0.05）。这些 DNAm 状态改变的差异表达基因（DEGs）被纳入预后风险评分模型。单因素 Cox 回归分析表明，风险评分、年龄和阳性淋巴结数量与 GC 患者的生存时间显著相关。多因素 Cox 回归分析也表明这些变量是 GC 的显著预后因素。构建了一个包含这些变量的列线图，并通过时间依赖性接收器操作特征（ROC）曲线评估其预测 GC 患者 1、3 和 5 年生存结局的性能。此外，通过决策曲线分析（DCA）揭示了该模型的临床获益。通路富集分析表明，这些 DNAm 驱动基因的改变状态可能通过影响“ECM 受体相互作用”和“DNA 复制”等信号通路影响肿瘤进展。

DNAm 驱动基因特征（、、、、和）的改变状态与 GC 患者的 OS 显著相关。包含风险评分、年龄和阳性淋巴结数量的列线图可以方便地用于预测 GC 患者的 OS，实现个体化预测。