N7-甲基腺苷（m7G）相关基因在胃癌（GC）的发展和预后中的作用尚不明确。本研究旨在基于m7G甲基化调节因子探索GC的预后生物标志物，构建预后危险模型。我们分别从癌症基因组图谱（TCGA）和基因表达序列数据库（GEO）获取与胃腺癌（STAD）组织学类型相关的GC的RNA测序文件和相关临床病理信息。总共提取了来自先前研究中的29个m7G调节因子。根据m7G调节因子的表达相似性，我们将来自TCGA的GC样本进一步划分为两个具有不同整体生存率和遗传异质性的聚类，并将这两个聚类之间的差异表达基因（DEGs）定义为m7G相关基因。然后，我们使用单变量回归分析和回归分析获得了预后m7G相关基因。我们利用TCGA和基因型-组织表达（GTEx）中的样本验证了这些包含在预后模型中的m7G相关基因的差异表达和预后价值。随后，将风险评分与其他预后因素相结合，开发了一个诊断图。通过标准接受者操作特征曲线（ROC曲线）评估了诊断图的预测能力。基因集富集分析（GSEA）用于鉴定两组中的激活通路。最后，评估了预后模型与GC的免疫特性之间的关联。我们构建了一个由11个m7G相关基因组成的预后模型。高风险组GC患者的预后较差，并在每个组中进一步证明了这一结果。该风险模型可应用于具有不同临床特征的患者。GSEA的结果显示，细胞粘附、细胞连接和聚焦粘附在高风险组中富集。此外，我们发现在低风险组中程序性细胞死亡配体1（PD-L1）的表达显著升高，而在高风险组中程序性细胞死亡配体2（PD-L2）和肿瘤坏死因子受体超家族成员4（TNFRSF4）过度表达。我们成功建立并验证了一个与m7G相关的预后模型，以预测预后并为改善GC治疗提供新的思路。版权所有：2023年转化癌症研究。保留所有权利。

The role of N7-methyladenosine (m7G)-related genes in the progression and prognosis of gastric cancer (GC) remains unclear. This study aimed to explore prognostic biomarkers for GC based on m7G methylation regulators and to construct a prognostic risk model.RNA sequencing profiles with corresponding clinicopathological information associated with GC of which the histological type was stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A total of 29 m7G regulators were extracted from previous studies. According to the expression similarity of m7G regulators, the GC samples obtained from TCGA were further classified into 2 clusters demonstrating different overall survival (OS) rates and genetic heterogeneity, and the differentially expressed genes (DEGs) between these 2 clusters were defined as m7G-related genes. Univariate regression analysis and regression analysis were then used to obtain the prognostic m7G-related genes. The samples in TCGA and Genotype-Tissue Expression (GTEx) were used to verify the differential expression and prognostic value of these m7G-related genes contained in the prognostic model. Subsequently, the risk score was combined with other prognostic factors to develop a nomogram. The predictive ability of the nomogram was evaluated by the standard receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) was used to identify activation pathways in both groups. Finally, the association between the prognostic model and the immune characteristics of GC were appraised.A prognostic model consisting of 11 m7G-related genes was constructed. GC patients in the high-risk group were shown to have a poor prognosis and this result was further demonstrated in each group. The risk model can be applied for patients with different clinical features. The results of GSEA showed that cell adhesion, cell junction, and focal adhesion were highly enriched in the high-risk group. In addition, we found that the expression of programmed cell death ligand 1 (PD-L1) was significantly elevated in the low-risk group, whereas programmed cell death ligand 2 (PD-L2) and tumor necrosis factor receptor superfamily member 4 (TNFRSF4) were overexpressed in the high-risk group.We successfully built and verified a m7G relevant prognostic model for predicting prognosis and providing a new train of thought for improving the treatment of GC.2023 Translational Cancer Research. All rights reserved.