Gastric cancer is one of the most common malignant tumours in the world. As one of the crucial hallmarks of cancer reprogramming of metabolism and the relevant researches have a promising application in the diagnosis treatment and prognostic prediction of malignant tumours. This study aims to identify a group of metabolism-related genes to construct a prediction model for the prognosis of gastric cancer.

A large cohort of gastric cancer cases (1121 cases) from public database was included in our analysis and classified patients into training and testing cohorts at a ratio of 7: 3. After identifying a list of metabolism-related genes having prognostic value, we constructed a risk score based on metabolism-related genes using LASSO-COX method. According to the risk score, patients were divided into high- and low-risk groups. Our results revealed that high-risk patients had a significantly worse prognosis than low-risk patients in both the training (high-risk vs low-risk patients; five years overall survival: 37.2% vs 72.2%; p < 0.001) and testing cohorts (high-risk vs low-risk patients; five years overall survival: 42.9% vs 62.9%; p < 0.001). This observation was validated in the external validation cohort (high-risk vs. low-risk patients; five years overall survival: 30.2% vs 40.4%; p = 0.007).

To reinforce the predictive ability of the model, we integrated risk score, age, adjuvant chemotherapy, and TNM stage into a nomogram. According to the result of receiver operating characteristic curves and decision curves analysis, we found that the nomogram score had a superior predictive ability than conventional factors, indicating that the risk score combined with clinicopathological features can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.

In conclusion, we identified a list of metabolic genes related to survival and developed a metabolism-based predictive model for gastric cancer. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was confirmed.

胃癌是世界上最常见的恶性肿瘤之一。作为癌症新陈代谢重编程的关键标志之一，相关研究在恶性肿瘤的诊断和预后预测中具有广阔的应用前景。本研究旨在确定一组与代谢相关的基因，以构建胃癌预后的预测模型。

我们的分析中包括来自公共数据库的大量胃癌病例（1121例），并将患者按7：3的比例分为训练和测试队列。在确定了具有预后价值的代谢相关基因列表后，我们构建了使用LASSO-COX方法基于代谢相关基因的风险评分。根据风险评分，将患者分为高风险组和低风险组。我们的结果显示，在训练（高风险与低风险患者；五年总生存期：37.2％对72.2％；p <0.001）和训练队列中，高风险患者的预后均显着低于低风险患者（高危vs低危患者；五年总生存期：42.9％vs 62.9％；p<0.001）。该观察结果在外部验证队列中得到验证（高风险与低风险患者；五年总生存期：30.2％对40.4％；p = 0.007）。

为了增强模型的预测能力，我们将风险评分，年龄，辅助化疗和TNM分期纳入了诺模图。根据接收者操作特征曲线和决策曲线分析的结果，我们发现列线图评分具有比常规因素更好的预测能力，表明风险评分与临床病理特征相结合可以为生存提供可靠的预测并改善个性化临床患者的决策。

总之，我们确定了与生存有关的代谢基因列表，并开发了基于代谢的胃癌预测模型。通过一系列的生物信息学和统计分析，证实了该模型的预测能力。