Combining ctnnb1 genetic variability with epidemiologic factors to predict lung cancer susceptibility

Article type: Research Article

Authors: Wang, Xu^{a; 1} | Zhang, Xiaochang^{a; 1} | Jin, Lina^b | Yang, Zhiguang^c | Li, Wei^{a; *} | Cui, Jiuwei^{a; *}

Affiliations: [a] Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, China | [b] School of Public Health, Jilin University, Changchun, China | [c] Division of Thoracic Surgery, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, China

Correspondence: [*] Corresponding authors: Jiuwei Cui, Cancer Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, China. Tel./ Fax: +86 10431 88786134; E-mail: [email protected],CancerCenter,FirstAffiliatedHospital,JilinUniversity,Changchun,Jilin130061,China.E-mail:[email protected].

Note: [1] These are co-first authors.

Abstract: 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.

Keywords: ctnnb1, single nucleotide polymorphism, risk prediction model, lung cancer, Chinese population

DOI: 10.3233/CBM-170563

Journal: Cancer Biomarkers, vol. 22, no. 1, pp. 7-12, 2018