J Cancer 2016; 7(7):846-853. doi:10.7150/jca.13437
Combining Telomerase Reverse Transcriptase Genetic Variant rs2736100 with Epidemiologic Factors in the Prediction of Lung Cancer Susceptibility
1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China.
2. Stanford University Medical School Stanford, Palo Alto Veterans Institute for Research, Palo Alto, CA94305, USA.
3. Second Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun , Jilin 130033, P.R. China.
4. School of Public Health, Jilin University, Changchun 130021, Jilin, P. R. China.
Wang X, Ma K, Chi L, Cui J, Jin L, Hu JF, Li W. Combining Telomerase Reverse Transcriptase Genetic Variant rs2736100 with Epidemiologic Factors in the Prediction of Lung Cancer Susceptibility. J Cancer 2016; 7(7):846-853. doi:10.7150/jca.13437. Available from https://www.jcancer.org/v07p0846.htm
Genetic variants from a considerable number of susceptibility loci have been identified in association with cancer risk, but their interaction with epidemiologic factors in lung cancer remains to be defined. We sought to establish a forecasting model for identifying individuals with high-risk of lung cancer by combing gene single-nucleotide polymorphisms with epidemiologic factors. Genotyping and clinical data from 500 lung cancer cases and 500 controls were used for developing the logistic regression model. We found that lung cancer was associated with telomerase reverse transcriptase (TERT) rs2736100 single-nucleotide polymorphism. The TERT rs2736100 model was still significantly associated with lung cancer risk when combined with environmental and lifestyle factors, including lower education, lower BMI, COPD history, heavy cigarettes smoking, heavy cooking emission, and dietary factors (over-consumption of meat and deficiency in fish/shrimp, vegetables, dairy products, and soybean products). These data suggest that combining TERT SNP and epidemiologic factors may be a useful approach to discriminate high and low-risk individuals for lung cancer.
Keywords: Lung cancer, forecasting model, telomerase, TERT, WWOX, single nucleotide polymorphism, epidemiologic factors, Chinese population.