State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Carcinogenesis. 2013 Aug;34(8):1782-6. doi: 10.1093/carcin/bgt106. Epub 2013 Mar 27.
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.
全基因组关联研究已经确定了多个与中国人食管鳞状细胞癌（ESCC）风险相关的遗传变异。我们研究了这些遗传因素以及非遗传因素是否有助于 ESCC 风险预测。我们在 9805 例病例和 10493 例对照中研究了与 ESCC 风险相关的 25 个单核苷酸多态性（SNP）和 4 个非遗传因素（性别、年龄、吸烟和饮酒）。计算加权遗传风险评分（wGRS），并使用逻辑回归分析 wGRS 与 ESCC 风险之间的关联。我们使用受试者工作特征曲线分析计算曲线下面积（AUC），以衡量在仅具有非遗传因素的模型中添加遗传变异后的区分能力。净重新分类改善（NRI）用于量化使用不同模型进行正确重新分类的程度。具有显著边缘效应（G SNPs）的 17 个联合 SNP 的 wGRS 增加了约 4 倍 ESCC 风险（P = 1.49 × 10(-) (164)），并且在饮酒者和非饮酒者中均有显著关联。然而，具有基因与饮酒相互作用中显著效应的 8 个 SNP 的 wGRS 仅在饮酒者中增加了约 4 倍 ESCC 风险（P 交互= 8.76 × 10(-) (41)）。包含 4 个非遗传因素、17 个 G SNPs、8 个 GE SNPs 及其与饮酒的相互作用的风险模型的 AUC 为 70.1%，与仅具有非遗传因素的模型相比，显著提高了 7.0%（P < 0.0001）。我们的研究结果表明，将遗传变异、生活方式因素及其与 ESCC 风险模型的相互作用结合起来，可以用于识别 ESCC 患者。
