Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model

Abstract

BackgroundLung cancer is a complex polygenic disease. Although recent genome-wide association (GWA) studies have identified multiple susceptibility loci for lung cancer, most of these variants have not been validated in a Chinese population. In this study, we investigated whether a genetic risk score combining multiple.MethodsFive single-nucleotide polymorphisms (SNPs) identified in previous GWA or large cohort studies were genotyped in 5068 Chinese case–control subjects. The genetic risk score (GRS) based on these SNPs was estimated by two approaches: a simple risk alleles count (cGRS) and a weighted (wGRS) method. The area under the receiver operating characteristic (ROC) curve (AUC) in combination with the bootstrap resampling method was used to assess the predictive performance of the genetic risk score for lung cancer.ResultsFour independent SNPs (rs2736100, rs402710, rs4488809 and rs4083914), were found to be associated with a risk of lung cancer. The wGRS based on these four SNPs was a better predictor than cGRS. Using a liability threshold model, we estimated that these four SNPs accounted for only 4.02% of genetic variance in lung cancer. Smoking history contributed significantly to lung cancer (P < 0.001) risk [AUC = 0.619 (0.603-0.634)], and incorporated with wGRS gave an AUC value of 0.639 (0.621-0.652) after adjustment for over-fitting. This model shows promise for assessing lung cancer risk in a Chinese population.ConclusionOur results indicate that although genetic variants related to lung cancer only added moderate discriminatory accuracy, it still improved the predictive ability of the assessment model in Chinese population.

Polymorphisms in human telomerase reverse transcriptase (h TERT ) gene and susceptibility to gastric cancer in a Turkish population: Hospital-based case–control study

“…Similar observations for G allele and/or GG genotype of hTERT 2736100 T>G polymorphism was reported for ALL (Sheng et al, 2013), ovarian cancer (Terry et al, 2012), cervical cancer (Wang et al, 2012) and bladder cancer (Gago-Dominguez et al, 2011). Our results are in line with previous findings displaying that there was an association between the G allele and/or GG genotype of hTERT 2736100 T>G polymorphism and increased risk for various cancers (Sheng et al, 2013;Li et al, 2013;Lan et al, 2013;Myneni et al, 2013;Li et al, 2012;Wang et al, 2010;Chen et al, 2011;Peng et al, 2015;Terry et al, 2012;Wang et al, 2012;Gago-Dominguez et al, 2011). On the contrary to these results, Shadrina et al (2015), Ding et al (2011), andJannuzzi et al (2015) reported that there was no correlation between hTERT 2736100 T>G polymorphism and susceptibility to BC, hepatocellular carcinoma and CRC, respectively.…”

“…Regarding to hTERT rs2736100 T>G polymorphism, results of case-control studies and metaanalysis of these studies were also reported with controversial (Sheng et al, 2013;Shadrina et al, 2015;Jannuzzi et al, 2015;Li et al, 2013;Lan et al, 2013;Myneni et al, 2013;Li et al, 2012;Wang et al, 2010;Chen et al, 2011;Peng et al, 2015;Terry et al, 2012;Wang et al, 2012;Gago-Dominguez et al, 2011;Ding et al, 2011). For instance, there are numerous investigations focused on the relation between hTERT rs2736100 T>G polymorphism and LC risk (Lan et al, 2013;Myneni et al, 2013;Li et al, 2012;Wang et al, 2010).…”

“…For instance, there are numerous investigations focused on the relation between hTERT rs2736100 T>G polymorphism and LC risk (Lan et al, 2013;Myneni et al, 2013;Li et al, 2012;Wang et al, 2010). It is suggested that G allele and/or GG genotype of hTERT 2736100 T>G polymorphism was significantly concerned with an increased risk of LC susceptibility (Lan et al, 2013;Myneni et al, 2013;Li et al, 2012;Wang et al, 2010). Furthermore, studies on the susceptibility of hTERT rs2736100 T>G polymorphism on glioma also showed that G allele and/or GG genotype of hTERT rs2736100 T>G polymorphism have significantly increased risk of glioma (Chen et al, 2011;Peng et al, 2015).…”

Screening for lung cancer using low dose computed tomography

“…The Etzel model is for African-Americans, and the Li and Park models apply to Chinese and Korean men, respectively. [77][78][79] Maisonneuve presented two models. The first is a version of the Bach model recalibrated in an Italian population for identifying those at high risk who would be suitable for screening; the second model uses initial screening results so is not applicable for pre-screening use.…”

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

“…When externally validated using CARET data, however, discrimination was modest (AUC, 0.68). Similarly, others have extended existing models or developed new models to incorporate candidate single nucleotide polymorphisms (SNPs) associated with lung cancer [25, 36–41]. Improvements in risk prediction were consistently modest, although at most 20 SNPs were incorporated in a single model.…”