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Background & Aims: Major depressive disorder and schizophrenia have been hypothesized to be closely associated with cancer. However, the associations between these psychiatric conditions and the development of lung cancer remain uncertain. This study aimed to explore the causal relationship among major depressive disorder, schizophrenia, and the risk of lung cancer. Methods: Two-sample bidirectional/multivariable and mediation Mendelian randomization (MR) analyses were conducted. Genome-wide summary data on major depressive disorder (N=500,199) and schizophrenia (N=127,906) were utilized. Data on the risk of lung cancer (overall, adenocarcinoma, and squamous cell) were collected from a cohort of individuals of European ancestry (N=27,209). Three smoking-related behaviors (smoking initiation, pack years of smoking, and cigarettes smoked per day) were included in the multivariable and mediation MR analyses. Results: Patients with schizophrenia had a significantly greater risk of developing lung cancer (odds ratio (OR) = 1.144, 95% confidence interval (95% CI): 1.048-1.248, P = 0.003). The number of cigarettes smoked per day partially mediated the relationship between schizophrenia and the overall risk of lung cancer (OR = 1.185, 95% CI: 1.112-1.264, P = 0.021, proportion of mediation effect: 61.033%). However, there is no reliable evidence indicating an association between major depressive disorder and the risk of lung cancer (overall, adenocarcinoma, and squamous cell cancer). Conclusions: The findings indicated an association between schizophrenia and an increased risk of lung cancer, with smoking served as a partial mediator. When smoking was included in the regression analysis, the explanatory power of schizophrenia diagnosis was reduced, suggesting that smoking may be an important causal contributor to lung cancer in this population. Given the high prevalence of smoking among individuals with schizophrenia, these results underscore the need for further research to explore the underlying mechanisms of smoking's impact. Consequently, greater emphasis should be placed on monitoring the respiratory health of individuals with schizophrenia and implementing early interventions to address smoking-related behaviors.
RESUMO
BACKGROUND: The aim was to identify the nutritional indexes, construct a prognostic model, and develop a nomogram for predicting individual survival probability in pan-cancers. METHODS: Nutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients were collected. The enrolled patients were randomly divided into training and validation cohorts. Least absolute shrinkage and selection operator (Lasso) regression cross-validation was used to determine the variables to include in the cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration, and clinical effectiveness of the model. RESULTS: A total of 2020 patients were included. The median OS was 56.50 months (95% CI, 50.36-62.65 months). In the training cohort of 1425 patients, through Lasso regression cross-validation, 13 characteristics were included in the model. Cox proportional hazards model was developed and visualized as a nomogram. The C-indexes of the model for predicting 1-, 3-, 5-, and 10-year OS were 0.848, 0.826, 0.814, and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723-8.397; log-rank p < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180-11.497; log-rank p < 0.001). CONCLUSION: The prognostic model based on the nutritional indexes of pan-cancer can divide patients into different survival risk groups and performed well in the validation cohort.