Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

Autor: Huiwen Zhou, Mengya Zhong, Zhengxin Wu, Zhijian Ye, Xuehui Hong, Jiabao Zhao, Yifan Zhuang, Yubo Xiong, Yan Yang, Haijie Lu, Yuekun Zhu, Jinshui Tan, Jingsong Ma, Zhi Gao
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Cancer Cell International, Vol 21, Iss 1, Pp 1-13 (2021)
Cancer Cell International
ISSN: 1475-2867
Popis: Background 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. Methods 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. Results 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. Conclusion 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.
Databáze: OpenAIRE
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