Department of Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Cancer Med. 2020 Oct;9(19):7310-7316. doi: 10.1002/cam4.3354. Epub 2020 Aug 10.
Genome-wide association studies (GWAS) have identified some single nucleotide polymorphisms (SNPs) associated with the risk of gastric cancer (GCa). However, currently, there is no published predictive model to assess the risk of GCa. In the present study, risk-associated SNPs derived from GWAS and large meta-analyses were selected to construct a predictive model to assess the risk of GCa. A total of 1115 GCa cases and 1172 controls from the eastern Chinese population were included. Logistic regression models were used to identify SNPs that correlated with the risk of GCa. A predictive model to assess the risk of GCa was established by receiver operating characteristic curve analysis. Multifactor dimensionality reduction (MDR) and classification and regression tree (CART) were applied to calculate the effect of high-order gene-environment interactions on risk of the cancer. A total of 42 SNPs were selected for further analysis. The results revealed that ASH1L rs80142782, PKLR rs3762272, PRKAA1 rs13361707, MUC1 rs4072037, PSCA rs2294008, and PLCE1 rs2274223 polymorphisms were associated with a risk of GCa. The area under curve considering both genetic factors and BMI was 3.10% higher than that of BMI alone. MDR analysis revealed that rs13361707 and rs4072307 variants and BMI had interaction effects on susceptibility to GCa, with the highest predictive accuracy (61.23%) and cross-validation consistency (100/100). CART analysis also supported this interaction model that non-overweight status and a six SNP panel could synergistically increase the susceptibility to GCa. The six SNP panel for predicting the risk of GCa may provide new tools for prevention of the cancer based on GWAS and large meta-analyses derived genetic variants.
全基因组关联研究（GWAS）已经确定了一些与胃癌（GCa）风险相关的单核苷酸多态性（SNP）。然而，目前还没有发表的预测模型来评估 GCa 的风险。在本研究中，从 GWAS 和大型荟萃分析中选择了与风险相关的 SNP，构建了一个预测模型来评估 GCa 的风险。共纳入了来自中国东部人群的 1115 例 GCa 病例和 1172 例对照。使用逻辑回归模型确定与 GCa 风险相关的 SNP。通过接收者操作特征曲线分析建立了评估 GCa 风险的预测模型。多因素维度缩减（MDR）和分类回归树（CART）用于计算高阶基因-环境相互作用对癌症风险的影响。共选择了 42 个 SNP 进行进一步分析。结果表明，ASH1L rs80142782、PKLR rs3762272、PRKAA1 rs13361707、MUC1 rs4072037、PSCA rs2294008 和 PLCE1 rs2274223 多态性与 GCa 风险相关。同时考虑遗传因素和 BMI 的曲线下面积比仅考虑 BMI 的曲线下面积高 3.10%。MDR 分析表明，rs13361707 和 rs4072307 变体与 BMI 对 GCa 的易感性存在交互作用，预测准确率最高（61.23%），交叉验证一致性为 100/100。CART 分析也支持这种交互模型，即非超重状态和六个 SNP 面板可以协同增加 GCa 的易感性。用于预测 GCa 风险的六个 SNP 面板可能为基于 GWAS 和大型荟萃分析衍生遗传变异的癌症预防提供新的工具。
