摘要目的:构建基于代谢通路相关基因的乳腺癌预后预测模型并进行验证。方法:从癌症基因组图谱(TCGA)数据库下载乳腺癌患者的基因表达数据和临床信息，然后从基因集富集分析(GSEA)网站提取所有代谢通路相关基因进行差异分析，获得肿瘤和正常组织中的差异表达基因，再利用单因素Cox和最小绝对收缩和选择算子(LASSO)回归分析筛选出与预后相关的差异代谢基因用于构建预后风险评分。根据风险评分的中位数，将患者分为高危组和低危组，利用Kaplan-Meier生存分析、受试者操作特征(ROC)曲线对预后模型进行效能评价，并将此模型联合其他临床因素构建列线图，对乳腺癌患者进行生存率预测。最后通过基因表达综合数据库进行验证。结果:通过单因素Cox和LASSO回归分析最后共筛选出了6个代谢相关基因( NT5 E、 PAICS、 PFKL、 PLA2 G2 D、 QPRT和 SHMT2)用于模型构建。在训练集和验证集中，预后风险评分均是乳腺癌的独立危险因素，Kaplan-Meier生存分析结果提示，高危组患者的总生存率均显著低于低危组，差异具有统计学意义( P<0.001)。并且ROC曲线分析结果表明，该列线图模型较其他临床病理特征预测准确性更高，曲线下面积均为0.794。校准图显示预测值与实际值一致性较好。基于GSEA确定了该模型可以揭示代谢特征，同时监测肿瘤微环境的状态。 结论:本研究构建的代谢相关基因预后模型可作为乳腺癌患者有前景的独立预后标志物，并可提示肿瘤微环境的状态。

abstractsObjective:To construct and validate prognostic model for breast cancer based on metabolic pathway-related genes.Methods:Gene expression data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) website. Then all metabolic pathway-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) website for differential analysis to obtain differentially expressed genes between tumor and normal tissues, and then differential metabolic genes associated with prognosis for constructing a prognostic risk score were screened by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Patients were divided into high-risk group and low-risk group based on the median risk scores, and the efficacy of the prognostic model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. The nomogram was constructed by combining this model with other clinical factors to predict the survival rate of breast cancer patients. Finally, the model was validated using the Gene Expression Omnibus (GEO) database.Results:A total of six metabolism-related genes ( NT5 E, PAICS, PFKL, PLA2 G2 D, QPRT and SHMT2) were finally screened by univariate Cox and LASSO regression for prognosis model. The prognostic risk score was an independent risk factor for breast cancer in both the training set and validating set, and the results of the Kaplan-Meier survival analysis suggested that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group, the difference was statistically significant ( P<0.001). The results of the ROC curve indicated that the nomogram model had higher predictive accuracy than other clinicopathological features, with an area under the curve value of 0.794 for both. Calibration curve showed good agreement between predicted and actual values. Based on GSEA, it was determined that the model could reveal metabolic features while monitoring the status of the tumor microenvironment (TME). Conclusion:The metabolism-related gene prognostic model constructed in this study may serve as a promising independent prognostic marker for breast cancer patients and may indicate the status of TME.