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Modeling the natural history and detection of lung cancer based on smoking behavior.

ABSTRACT: In this study, we developed a method for modeling the progression and detection of lung cancer based on the smoking behavior at an individual level. The model allows obtaining the characteristics of lung cancer in a population at the time of diagnosis. Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were used to fit the lung cancer progression and detection model. The fitted model combined with a smoking based carcinogenesis model was used to predict the distribution of age, gender, tumor size, disease stage and smoking status at diagnosis and the results were validated against independent data from the SEER database collected from 1988 to 1999. The model accurately predicted the gender distribution and median age of LC patients of diagnosis, and reasonably predicted the joint tumor size and disease stage distribution.

SUBMITTER: Chen X

PROVIDER: S-EPMC3976286 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Modeling the natural history and detection of lung cancer based on smoking behavior.

Chen Xing X Foy Millennia M Kimmel Marek M Gorlova Olga Y OY

PloS one 20140404 4

In this study, we developed a method for modeling the progression and detection of lung cancer based on the smoking behavior at an individual level. The model allows obtaining the characteristics of lung cancer in a population at the time of diagnosis. Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were used to fit the lung cancer progression and detection model. The fitted model combined with a smoking based carcinogenesis model ...[more]

PMID: 24705368

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Project description:BackgroundPathologic condition associated with metabolic syndrome traits seems to increase the risk of colorectal cancer. One mechanism underlying this relationship may involve the growth-promoting effects of the circulation hormones associated with obesity and insulin resistance, such as leptin.Methodology/principal findingsA two-stage case-control study was used to explore the role of polymorphisms of Leptin (LEP) and Leptin receptor (LEPR), either alone or in combination with environmental factors in colorectal carcinogenesis. In stage 1, 20 single nucleotide polymorphisms (SNPs) that tag common SNPs in these two genes were genotyped among 470 cases and 458 controls. In stage 2, another population with 314 cases and 355 controls were genotyped for the two most promising SNPs from stage 1. LEPR rs12037879 only presented modestly increased colorectal cancer risk, with odds ratios of 1.41 (95% confidence interval [CI] 1.13-1.76) and 1.74 (95%CI 1.08-2.81) for GA and AA genotype when compared with GG genotype in combined population. Smokers carrying LEPR rs12037879 A allele presented 1.67-fold (95%CI 1.39-fold to 2.01-fold) increased colorectal cancer risk when compared with non-smokers carrying GG genotype in combined analysis. Individuals with family history of cancer harboring LEPR rs12037879 A allele showed 1.52-fold (95%CI: 1.24-fold to 1.86-fold) increased colorectal cancer risk, compared with individuals without family history of cancer harboring GG genotype. Multifactor gene-environment interaction analysis revealed significant interactions among LEPR rs12037879, LEPR rs6690625, smoking status and family history of cancer, exhibiting a gradient of increased colorectal cancer risk along with the increasing number of risk factors (P = 9.82 × 10(-10)).Conclusions/significanceOur research supports that polymorphisms in LEPR may be associated with marginal increase in the risk for colorectal cancer. Moreover, this association could be strengthened by cigarette smoking and family history of cancer.

Dataset Information

Modeling the natural history and detection of lung cancer based on smoking behavior.

Publications

Modeling the natural history and detection of lung cancer based on smoking behavior.

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