Combining ctnnb1 genetic variability with epidemiologic factors to predict lung cancer susceptibility.
Combining ctnnb1 genetic variability with epidemiologic factors to predict lung cancer susceptibility.
复制标题
将 ctnnb1 遗传变异与流行病学因素相结合来预测肺癌易感性。
DOI：
10.3233/cbm-170563
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Cui
中科院分区：
文献类型：
--
作者：
Xu Wang;Xiaochang Zhang;Lina Jin;Zhiguang Yang;Wei Li;J. Cui
目的
肺癌的早期检测和诊断仍然具有挑战性，但可以改善患者的预后。本研究的目的是开发一个模型来估计特定个体患肺癌的风险。
方法
我们进行了一项病例对照研究，以开发一种预测模型来识别肺癌高危人群。来自500名肺癌患者和500名基于人群的年龄和性别匹配对照的临床数据被用于开发和评估该模型。使用logistic回归模型分析环境变异与β-连环蛋白（ctnnb 1）单核苷酸多态性（SNP）和肺癌风险之间的关联。通过计算受试者工作特征（ROC）曲线下面积确定模型的预测准确性。
结果
既往诊断为慢性阻塞性肺疾病（COPD）、肺结核、癌症家族史和吸烟是肺癌的危险因素。曲线下面积（AUC）为0.740，敏感性、特异性和约登指数分别为0.718、0.660和0.378。
结论
我们的肺癌风险预测模型对于区分高风险个体是有用的。
OBJECTIVE
Early detection and diagnosis of lung cancer remain challenging but would improve patient prognosis. The goal of this study is to develop a model to estimate the risk of lung cancer for a given individual.
METHODS
We conducted a case-control study to develop a predictive model to identify individuals at high risk for lung cancer. Clinical data from 500 lung cancer patients and 500 population-based age- and gender-matched controls were used to develop and evaluate the model. Associations between environmental variants together with single nucleotide polymorphisms (SNPs) of beta-catenin (ctnnb1) and lung cancer risk were analyzed using a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic (ROC) curve.
RESULTS
Prior diagnosis of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis, family history of cancer, and smoking are lung cancer risk factors. The area under the curve (AUC) was 0.740, and the sensitivity, specificity, and Youden index were 0.718, 0.660, and 0.378, respectively.
CONCLUSION
Our risk prediction model for lung cancer is useful for distinguishing high-risk individuals.
影响因子：
10.3
作者：
Spitz, Margaret R.;Hong, Waun Ki;Etzel, Carol J.
通讯作者：
Etzel, Carol J.
影响因子：
19.7
作者：
Gatsonis, Constantine A.
通讯作者：
Gatsonis, Constantine A.