胃癌是高度异质性的，胃癌患者的总体生存率较低。预测胃癌患者的预后也具有一定难度，部分原因是因为我们对该疾病中与预后相关的代谢途径知之甚少。因此，我们的目标是通过在胃癌肿瘤样本中核心代谢途径活性的变化基础上，鉴定与预后相关的胃癌亚型和基因。使用基因集变异分析（GSVA）分析胃癌患者代谢途径活性的差异，并通过非负矩阵分解（NMF）确定三种临床亚型。基于我们的分析，亚型1表现出最佳预后，而亚型3则表现出最差的预后。我们发现三个亚型之间基因表达有显著差异，从中我们确定了一个新的进化驱动基因CNBD1。此外，我们使用 LASSO和随机森林算法鉴定了11个与代谢相关的基因来构建预后模型，并使用 qRT-PCR 在5个胃癌患者的配对组织中验证了结果。该模型在 GSE84437和 GSE26253 队列中的效果和鲁棒性都得到了验证，并且多元 Cox 回归分析的结果证实了11个基因签名是一个独立的预后预测因子（p<0.0001，HR = 2.8，95% CI 2.1-3.7）。该标记与肿瘤相关免疫细胞浸润有关。总之，我们的工作鉴定了不同胃癌亚型中与预后相关的重要代谢途径，并为胃癌亚型预后评估提供了新的见解。版权所有©2023 陈，赵，陈，金，黄，朱，戴，袁，陈，唐，陈，林，艾，吴，陈和秦。

Gastric cancer (GC) is highly heterogeneous and GC patients have low overall survival rates. It is also challenging to predict the prognosis of GC patients. This is partly because little is known about the prognosis-related metabolic pathways in this disease. Hence, our objective was to identify GC subtypes and genes related to prognosis, based on changes in the activity of core metabolic pathways in GC tumor samples. Differences in the activity of metabolic pathways in GC patients were analyzed using Gene Set Variation Analysis (GSVA), leading to the identification of three clinical subtypes by non-negative matrix factorization (NMF). Based on our analysis, subtype 1 showed the best prognosis while subtype 3 exhibited the worst prognosis. Interestingly, we observed marked differences in gene expression between the three subtypes, through which we identified a new evolutionary driver gene, CNBD1. Furthermore, we used 11 metabolism-associated genes identified by LASSO and random forest algorithms to construct a prognostic model and verified our results using qRT-PCR (five matched clinical tissues of GC patients). This model was found to be both effective and robust in the GSE84437 and GSE26253 cohorts, and the results from multivariate Cox regression analyses confirmed that the 11-gene signature was an independent prognostic predictor (p < 0.0001, HR = 2.8, 95% CI 2.1-3.7). The signature was found to be relevant to the infiltration of tumor-associated immune cells. In conclusion, our work identified significant GC prognosis-related metabolic pathways in different GC subtypes and provided new insights into GC-subtype prognostic assessment.Copyright © 2023 Chen, zhao, Chen, Jin, Huang, Zhu, Dai, Yuan, Chen, Tang, Chen, Lin, Ai, Wu, Chen and Qin.