Wu Zhengxin, Tan Jinshui, Zhuang Yifan, Zhong Mengya, Xiong Yubo, Ma Jingsong, Yang Yan, Gao Zhi, Zhao Jiabao, Ye Zhijian, Zhou Huiwen, Zhu Yuekun, Lu Haijie, Hong Xuehui
School of Medicine, Guangxi University, Nanning, 530004, China.
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China.
Cancer Cell Int. 2021 Dec 14;21(1):668. doi: 10.1186/s12935-021-02385-x.
Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear.
Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan-Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR.
Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan-Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model.
In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.
代谢重编程在多种癌症中均有报道，且与临床预后相关，但嘧啶代谢在胃癌（GC）中的预后作用仍不清楚。
在此，我们采用差异表达基因（DEG）分析来检测嘧啶代谢信号通路中的差异表达基因，并使用单因素Cox分析、Lasso惩罚Cox回归分析、Kaplan-Meier生存分析、单因素和多因素Cox回归分析来探讨它们在GC中的预后作用。通过定量实时PCR在GC细胞和临床样本中对DEG进行实验验证。
通过DEG分析，我们发现NT5E、DPYS和UPP1这三个基因在GC中高表达。这一结论也在GC细胞和临床样本中得到了验证。通过单因素Cox分析和Lasso惩罚Cox回归分析，根据这三个DEG建立了一个预后风险模型。Kaplan-Meier生存分析表明，高风险评分的患者队列总生存率低于低风险评分的患者队列。对该模型进行分层生存分析、单因素和多因素Cox回归分析证实，它是一个可靠的独立临床因素。因此，我们制作了列线图，根据包括我们的风险模型在内的一些重要临床因素直观地描绘GC患者的生存率。
总之，我们的研究发现GC中嘧啶代谢失调，并基于嘧啶代谢中差异表达的基因建立了GC的预后模型。
