The impact of radiation on the development of lung cancer. (7th September 2017)

• Record Type: Journal Article
• Authors: Li, Lingling; Tian, Tianhai; Zhang, Xinan
• Is Part Of: Journal of theoretical biology. Volume 428(2017)
• Page Start: 147
• Page End: 152
• Publication Date: 2017-09-07
• Subjects: Lung cancer -- Gene mutation -- Clonal expansion -- Three-stage model -- Chi-square test; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2017.06.020
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: The 3-stage model is used to analyze the impact of radiation on lung cancer development. The Chi-square test is utilized to study the mechanism that radiation induces lung cancer development. Radiation has more significant impact on cell mutations than clonal expansion of cells in lung cancer development. There is a gender difference in the progression of lung cancer. Abstract: Environment factors such as radiation play an important role in the incidence of lung cancer. In spite of substantial efforts in experimental study and mathematical modeling, it is still a significant challenge to estimate lung cancer risk from radiation. To address this issue, we propose a stochastic model to investigate the impact of radiation on the development of lung cancer. The proposed three-stage model with clonal expansion is used to match the data of the male and female patients in the Osaka Cancer Registry (OCR) and Life Span Study (LSS) cohort of atomic bomb survivors in Hiroshima and Nagasaki. Our results indicate that the major effect of radiation on the development of lung cancer is to induce gene mutations for both male and female patients. In particular, for male patients, radiation affects the mutation in normal cells and the transformation from premalignant cells to malignant ones. However, radiation for female patients increases the mutation rates of the first two mutations in the stochastic model. The established relationship between parameters and radiation will provide insightful prediction for the lung cancer incidence in the radiation exposure.