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.
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机译：胃癌是世界上最常见的恶性肿瘤之一。作为新陈代谢重新编程的关键标志之一，相关研究在诊断治疗和对恶性肿瘤的预后预测中具有有希望的应用。本研究旨在鉴定一组新陈代谢相关基因，以构建胃癌预后的预测模型。来自公共数据库的大群胃癌病例（1121例）被列入我们的分析和分类患者，以比例为7：3的培训和测试队列。在鉴定具有预后价值的新陈代谢相关基因列表后，我们构建利用套索COX法基于新陈代谢相关基因的风险分数。根据风险评分，患者分为高风险群体。我们的研究结果表明，高风险患者的预后显着比低风险患者（高风险与低风险患者;五年整体生存：37.2％Vs 72.2％; P <0.001）和测试队列（高风险与低风险患者;五年整体生存：42.9％Vs 62.9％; P <0.001）。该观察结果在外部验证队列中验证（高风险与低风险患者;五年整体生存：30.2％Vs 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，佐剂化疗和TNM阶段综合到墨顶图中。根据接收器操作特征曲线和决策曲线分析的结果，我们发现载体评分比传统因素具有卓越的预测能力，表明风险得分与临床病理学特征结合可以对生存和改善个体化临床产生鲁棒预测决策患者。总之，我们确定了与生存相关的代谢基因列表，并开发了一种基于代谢的胃癌预测模型。通过一系列生物信息学和统计分析，确认了模型的预测能力。
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