ArticleInternational journal of oncology2016
Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model.
Article in International journal of oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Spotlight on amino acid changing mutations in the JAK-STAT pathway: from disease-specific mutation to general mutation databases.Scientific reports · 2025Article
- The Association Between IL-8 Gene Polymorphisms and the Risk of Several Types of Cancer, Especially in Gastric Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models.Journal of cancer research and clinical oncology · 2024Article
- SNPs-Panel Polymorphism Variations inBiomedicines · 2023Article
- [Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023Article
- The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis.Cancer epidemiology · 2023Article
- Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study.JMIR public health and surveillance · 2023Article
- Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting.Cancer medicine · 2022Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors.Journal of medical genetics · 2021Article
- Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China.Frontiers in oncology · 2021Article
- Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population.Cancer medicine · 2020Article
- Implementation planning for lung cancer screening in China.Precision clinical medicine · 2019Review
- A meta-analytic review of the association between two common SNPs in miRNAs and lung cancer susceptibility.OncoTargets and therapy · 2018Review
- Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.Current epidemiology reports · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73-0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79-0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-16); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.
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