Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations.
Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations.
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使用两阶段复制对亚洲和欧洲人群进行肺癌遗传风险预测的风险评估模型
DOI：
10.18632/oncotarget.10403
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发表时间：
2017-08-15
期刊：
影响因子：
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通讯作者：
Dai J
中科院分区：
文献类型：
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作者：
Cheng Y;Jiang T;Zhu M;Li Z;Zhang J;Wang Y;Geng L;Liu J;Shen W;Wang C;Hu Z;Jin G;Ma H;Shen H;Dai J
在过去的十年中，候选基因研究和全基因组关联研究都取得了巨大的成功。然而，在大规模和不同种族人群中系统评估肺癌风险的遗传效应的研究有限。我们系统回顾相关文献，筛选出124篇文章中发现的241个重要遗传变异。在特定亚组中进行了一项两阶段病例对照研究来评估效果[训练集：2331例对3077例对照（中国人群）；测试集：1937例与1984例对照（欧洲人群）]。采用LASSO惩罚回归和遗传风险评分（GRS）系统进行变量选择和模型开发。采用带和不带GRS的流行病学模型进一步改变接收者操作者特征曲线（AUC）下的面积来比较预测结果。在我们的研究中保留了38个遗传变异，GRS上四分位数受试者的肺癌风险比低四分位数受试者高三倍（优势比：4.64,95% CI: 3.87-5.56）。此外，我们发现在吸烟危险因素模型中加入遗传预测因子可显著提高肺癌的预测值：AUC为0.610比0.697 （P < 0.001）。在欧洲人群和合并的两个数据集中得出了类似的表现。我们的研究结果表明，遗传预测因子可以提高肺癌风险模型的预测能力，并突出了肺癌风险评估模型在不同人群中的应用，表明肺癌风险评估模型将成为筛查和预测高危人群的一种有前景的工具。
In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.