Combining gene expression signature with clinical features for survival stratification of gastric cancer
Combining gene expression signature with clinical features for survival stratification of gastric cancer
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结合基因表达特征与临床特征进行胃癌生存分层
DOI：
10.1016/j.ygeno.2021.06.018
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发表时间：
2021
期刊：
影响因子：
4.4
通讯作者：
Zhou Tianhua
中科院分区：
文献类型：
--
作者：
Sun Qiang;Guo Dongyang;Li Shuang;Xu Yanjun;Jiang mingchun;Li Yang;Duan Huilong;Liu Wei;Zhu Shankuan;Wang Liangjing;Zhou Tianhua
AJCC分期被认为是临床的黄金标准。然而，在评估临床病理特征相似的胃癌(GC)患者的预后方面仍存在一些缺陷。我们的目标是开发一种新的临床和遗传风险评分(CGRS)来改善GC患者的预后预测。我们基于APOD、CCDC92、CyS1、GSDME、ST8SIA5、STARD3NL、TIMEM245、TSPYL5和VAT1等9个基因签名，采用Lasso-Cox回归算法建立了基于9个基因特征的遗传风险评分(GRS)。CGRS是通过将GRS与来自监测、流行病学和最终结果(SEER)数据库的临床风险评分(CRS)相结合而建立的。GRS和CGRS在四个不同数据类型的独立队列中将GC患者分为高风险组和低风险组，预后显著不同，如微阵列、核糖核酸测序和qRT-PCR1(all HR；>1，allP<；；0.001)。GRS和CGRS都是独立于AJCC分期系统的预后信号。受试者工作特征(ROC)分析显示，在我们研究的大多数队列中，CGRS的ROC曲线下面积大于AJCC分期系统。为了方便临床医生在临床实践中评估胃癌的预后，开发了基于CGRS的诺模图和网络工具(http://39.100.117.92/CGRS/))。CGRs将遗传特征与临床特征相结合，在预测胃癌预后方面表现出较强的稳健性，并可通过Web应用程序轻松应用于临床。
The AJCC staging system is considered as the golden standard in clinical practice. However, it remains some pitfalls in assessing the prognosis of gastric cancer (GC) patients with similar clinicopathological characteristics. We aim to develop a new clinic and genetic risk score (CGRS) to improve the prognosis prediction of GC patients. We established genetic risk score (GRS) based on nine-gene signature includingAPOD,CCDC92,CYS1,GSDME,ST8SIA5,STARD3NL,TIMEM245,TSPYL5, andVAT1based on the gene expression profiles of the training set from the Asian Cancer Research Group (ACRG) cohort by LASSO-Cox regression algorithms. CGRS was established by integrating GRS with clinical risk score (CRS) derived from Surveillance, Epidemiology, and End Results (SEER) database. GRS and CGRS dichotomized GC patients into high and low risk groups with significantly different prognosis in four independent cohorts with different data types, such as microarray, RNA sequencing and qRT-PCR (all HR > 1, allP< 0.001). Both GRS and CGRS were prognostic signatures independent of the AJCC staging system. Receiver operating characteristic (ROC) analysis showed that area under ROC curve of CGRS was larger than that of the AJCC staging system in most cohorts we studied. Nomogram and web tool (http://39.100.117.92/CGRS/) based on CGRS were developed for clinicians to conveniently assess GC prognosis in clinical practice. CGRS integrating genetic signature with clinical features shows strong robustness in predicting GC prognosis, and can be easily applied in clinical practice through the web application.