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Schizophrenia, bipolar disorder and major depression have been reported to be associated with some cancers. However, the magnitude of the causal relationship between them remains unclear. This study aims to explore the potential association between three major mental diseases and the risk of some cancers. We performed the two-sample Mendelian randomization(MR) analysis using publicly available genome-wide association studies (GWAS) statistics to investigate the causal relationship between these three mental diseases and some common types of cancers, including breast cancer, ovarian cancer, lung cancer, colorectal cancer, bladder cancer, prostate cancer, thyroid cancer, pancreatic cancer, malignant melanoma and glioma. We obtained genetic association estimates for schizophrenia, bipolar disorder and depression from the Psychiatric Genomics Consortium.The genetic association estimates for cancers were obtained from the UK Biobank, the MRC-IEU consortium and the GliomaScan consortium. After correction for heterogeneity and horizontal pleiotropy, we detected suggestive evidence for the association between thyroid cancer and genetically predicted schizophrenia (OR = 1.543, 95% CI: 1.023–2.328, P = 0.039), and thyroid cancer and major depression (OR = 3.573, 95% CI: 1.068–11.953, P = 0.039). No evidence of causal effects of schizophrenia, major depression and bipolar disorder on other types of cancers. Our findings suggest the association of schizophrenia and major depression and the development of thyroid cancer. • Mental illness is closely related to various types of cancer. • Mendelian randomization is a novel and persuasive causal reasoning method. • Association between schizophrenia, bipolar disorder, major depression and cancer • Genetic link between schizophrenia, major depression and thyroid cancer [ABSTRACT FROM AUTHOR]

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.