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Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations
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 popul...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589554/ https://www.ncbi.nlm.nih.gov/pubmed/28903315 http://dx.doi.org/10.18632/oncotarget.10403 |
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author | Cheng, Yang Jiang, Tao Zhu, Meng Li, Zhihua Zhang, Jiahui Wang, Yuzhuo Geng, Liguo Liu, Jia Shen, Wei Wang, Cheng Hu, Zhibin Jin, Guangfu Ma, Hongxia Shen, Hongbing Dai, Juncheng |
author_facet | Cheng, Yang Jiang, Tao Zhu, Meng Li, Zhihua Zhang, Jiahui Wang, Yuzhuo Geng, Liguo Liu, Jia Shen, Wei Wang, Cheng Hu, Zhibin Jin, Guangfu Ma, Hongxia Shen, Hongbing Dai, Juncheng |
author_sort | Cheng, Yang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5589554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-55895542017-09-12 Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations Cheng, Yang Jiang, Tao Zhu, Meng Li, Zhihua Zhang, Jiahui Wang, Yuzhuo Geng, Liguo Liu, Jia Shen, Wei Wang, Cheng Hu, Zhibin Jin, Guangfu Ma, Hongxia Shen, Hongbing Dai, Juncheng Oncotarget Research Paper 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. Impact Journals LLC 2016-07-05 /pmc/articles/PMC5589554/ /pubmed/28903315 http://dx.doi.org/10.18632/oncotarget.10403 Text en Copyright: © 2017 Cheng et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (http://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Cheng, Yang Jiang, Tao Zhu, Meng Li, Zhihua Zhang, Jiahui Wang, Yuzhuo Geng, Liguo Liu, Jia Shen, Wei Wang, Cheng Hu, Zhibin Jin, Guangfu Ma, Hongxia Shen, Hongbing Dai, Juncheng Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title | Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title_full | Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title_fullStr | Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title_full_unstemmed | Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title_short | Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations |
title_sort | risk assessment models for genetic risk predictors of lung cancer using two-stage replication for asian and european populations |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589554/ https://www.ncbi.nlm.nih.gov/pubmed/28903315 http://dx.doi.org/10.18632/oncotarget.10403 |
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