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Carcinogenesis. 2013 Aug;34(8):1782-6. doi: 10.1093/carcin/bgt106. Epub 2013 Mar 27.

Risk prediction of esophageal squamous-cell carcinoma with common genetic variants and lifestyle factors in Chinese population.

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1
State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Genome-wide association studies have identified multiple genetic variants associated with risk of esophageal squamous-cell carcinoma (ESCC) in Chinese populations. We examined whether these genetic factors, along with non-genetic factors, can contribute to ESCC risk prediction. We examined 25 single nucleotide polymorphisms (SNPs) and 4 non-genetic factors (sex, age, smoking and drinking) associated with ESCC risk in 9805 cases and 10 493 controls from Chinese populations. Weighted genetic risk score (wGRS) was calculated and logistic regression was used to analyze the association between wGRS and ESCC risk. We calculated the area under the curve (AUC) using receiver operating characteristic curve analysis to measure the discrimination after adding genetic variants to the model with only non-genetic factors. Net reclassification improvement (NRI) was used to quantify the degree of correct reclassification using different models. wGRS of the combined 17 SNPs with significant marginal effect (G SNPs) increased ~4-fold ESCC risk (P = 1.49 × 10(-) (164)) and the associations were significant in both drinkers and non-drinkers. However, wGRS of the eight SNPs with significant effect in gene × drinking interaction (GE SNPs) increased ~4-fold ESCC risk only in drinkers (P interaction = 8.76 × 10(-) (41)). The AUC for a risk model with 4 non-genetic factors, 17 G SNPs, 8 GE SNPs and their interactions with drinking was 70.1%, with the significant improvement of 7.0% compared with the model with only non-genetic factors (P < 0.0001). Our results indicate that incorporating genetic variants, lifestyle factors and their interactions in ESCC risk models can be useful for identifying patients with ESCC.

PMID:
23536576
DOI:
10.1093/carcin/bgt106
[Indexed for MEDLINE]
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