Lung cancer has a significant impact on human health. Recent studies indicate that lung cancer has the highest incidence and mortality rates among all cancers. Notably, current research suggests that lung cancer is a multifactorial disease, influenced by genetic factors, lifestyle habits, social factors, comorbidities, and more. Therefore, understanding the relationship between these diverse factors and lung cancer, as well as its prognosis, is crucial for its prevention, treatment, and overall patient outcomes.
In our study, logistic regression and linear regression were used to assess the associations between various factors and lung carcinogenesis. Kaplan-Meier survival curves and Cox proportional hazards models were employed to evaluate the relationship between these factors and the prognosis of lung cancer. Propensity score matching and multivariate regressions were utilized to reduce bias. Mendelian randomization was conducted to analyze the effects of genetic variants. Additionally, machine learning algorithms were applied to construct predictive models.
The project spanned 36 months, and the results of our study are expected to make a significant contribution to the prevention, treatment, and prognosis of lung cancer by elucidating the associations between risk factors and lung cancer.
