Ye Zhuo-Miao, Hu Qing-Yu, Zheng Jing-Hui, Zhang Chi, Zhu Xiang-Dong, Tang You-Ming
Ruikang School of Clinical Medicine, Guangxi University of Chinese Medicine.
Department of Cardiology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine.
Medicine (Baltimore). 2020 Jun 19;99(25):e20448. doi: 10.1097/MD.0000000000020448.
Single nucleotide polymorphisms (SNPs) have been inconsistently associated with gastric cancer (GC) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with GC.
Databases were searched to identify association studies of SNPs and GC published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis.
This study will provide a high-quality evidence to find the SNP most associated with GC susceptibility and the best genetic model.
This study will explore which SNP is most associated with GC susceptibility.
INPLASY202040132.
单核苷酸多态性（SNP）与胃癌（GC）风险的关联并不一致。本荟萃分析旨在综合与GC相关的SNP的相关数据。
检索数据库，以识别截至2020年1月在PubMed、Web of Science、Embase、Cochrane图书馆、中国知网、中国科技期刊数据库和万方数据库中发表的SNP与GC的关联研究。采用网络荟萃分析和Thakkinstian算法选择最合适的遗传模型，以及针对显著关联的假阳性报告概率。基于加强遗传关联研究报告声明评估数据的方法学质量。将使用Stata 14.0进行系统评价和荟萃分析。
本研究将为找出与GC易感性最相关的SNP和最佳遗传模型提供高质量证据。
本研究将探索哪种SNP与GC易感性最相关。
INPLASY202040132。
