Research Papers:

Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations

Yang Cheng, Tao Jiang, Meng Zhu, Zhihua Li, Jiahui Zhang, Yuzhuo Wang, Liguo Geng, Jia Liu, Wei Shen, Cheng Wang, Zhibin Hu, Guangfu Jin, Hongxia Ma, Hongbing Shen and Juncheng Dai _

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Oncotarget. 2017; 8:53959-53967. https://doi.org/10.18632/oncotarget.10403

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Yang Cheng1,*, Tao Jiang1,*, Meng Zhu1, Zhihua Li1, Jiahui Zhang1, Yuzhuo Wang1, Liguo Geng1, Jia Liu1, Wei Shen1, Cheng Wang1, Zhibin Hu1,2, Guangfu Jin1,2, Hongxia Ma1,2, Hongbing Shen1,2 and Juncheng Dai1,2

1Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China

2Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China

*These authors contributed equally to this work

Correspondence to:

Juncheng Dai, email: [email protected]

Keywords: lung cancer, polymorphism, genetic risk score, risk prediction, ethnic populations

Received: April 21, 2016     Accepted: June 04, 2016     Published: July 05, 2016


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.

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