Abstract
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.
Highlights
Lung cancer is one of the most commonly diagnosed malignancies and the leading cause of cancer-related deaths in the world, with almost 1.6 million deaths per year (19.4% of total cancer mortality) [1]
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
NJMU genome-wide association studies (GWAS) contains 2 331 lung cancer cases and 3 077 healthy controls, which was used as the training set to construct the model, while Environment and Genetics in Lung Cancer Etiology (EAGLE) study containing 1 937 cases and 1 984 controls was used to validate the model
Summary
Lung cancer is one of the most commonly diagnosed malignancies and the leading cause of cancer-related deaths in the world, with almost 1.6 million deaths per year (19.4% of total cancer mortality) [1]. As well as known that the major environmental cause is tobacco smoking accounting for over 80% of all lung cancer cases. Over the past ten years, both candidate-gene studies and genome-wide association studies (GWAS) have successfully identified dozens of loci associated with lung cancer risk. Despite significant advances in medical therapy, prognosis of lung cancer remains poor with a five-year survival rate of 16.6% [13], as most cases are diagnosed at advanced stage. When lung cancer is detected before metastasis, the five-year survival rates should be 60–80% [14]. Early detection and diagnosis for lung cancer was the focus of our future research. In this respect, screening high risk population of lung cancer is an important element
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