Causal effects of genetically determined metabolites on cancers included lung, breast, ovarian cancer, and glioma: a Mendelian randomization study

BACKGROUND: Previous studies have shown that metabolites play important roles in phenotypic regulation, but the causal link between metabolites and tumors has not been examined adequately. Herein, we investigate the causality between metabolites and various cancers through a Mendelian randomization (MR) study. METHODS: We carried out a two-sample MR analysis based on genetic instrumental variables as proxies for 486 selected human serum metabolites to evaluate the causal effects of genetically determined metabotypes (GDMs) on cancers. Summary data from various cancer types obtained from large consortia. Inverse variance weighted (IVW), MR-Egger and weighted-median methods were implemented to infer the causal effects, moreover, we particularly explored the presentence of horizontal pleiotropy through MR-Egger regression and MR-PRESSO Global test. Metabolic pathways analysis and subgroup analyses were further explored using available data. Statistical analyses were all performed in R. RESULTS: In MR analysis, 202 significant causative relationship features were identified. 7-alpha-hydroxy-3-oxo-4-cholestenoate (OR(IVW )=1.45; 95% CI: 1.06–1.97; P(IVW )=0.018), gamma-glutamylisoleucine (OR(IVW )=1.40; 95% CI: 1.16–1.69; P(IVW )=0.0004), 1-oleoylglycerophosphocholine (OR(IVW )=1.22; 95% CI: 1.1–1.35; P(IVW )=0.0001), gamma-glutamylleucine (OR(IVW )=4.74; 95% CI: 1.18–18.93; P(IVW )=0.027) were the most dangerous metabolites for lung cancer, ovarian cancer, breast cancer, and glioma, respectively; while pseudouridine (OR(IVW )=0.50; 95% CI: 0.30–0.83; P(IVW )=0.007), 2-methylbutyroylcarnitine (OR(IVW )=0.77; 95% CI: 0.68–0.86; P(IVW )=2.9×10(−6)), 2-methylbutyroylcarnitine (OR(IVW )=0.77; 95% CI: 0.70–0.85; P(IVW )=3.4×10(−7)), glycylvaline (OR(IVW )=0.13; 95% CI: 0.02–0.75; P(IVW )=0.021) were associated with lower risk of lung cancer, ovarian cancer, breast cancer, and glioma, respectively. Interestingly, 2-methylbutyroylcarnitine was also associated with decreased risk of lung cancer (OR(IVW )=0.59; 0.50–0.70; P(IVW) =1.98×10(−9)) expect ovarian cancer and breast cancer. In subgroup analysis, 2-methylbutyroylcarnitine was associated with decreased risk of estrogen receptor (ER) positive breast cancer (OR(IVW) =0.72; 0.64–0.80; P(IVW )=3.55×10(−9)), lung adenocarcinoma (LAC) (OR(IVW) =0.60; 0.48–0.70; P(IVW )=1.14×10(-5)). Metabolic pathways analysis identified 4 significant pathways. CONCLUSIONS: Our study integrated metabolomics and genomics to explore the risk factors involved in the development of cancers. It is worth exploring whether metabolites with causality can be used as biomarkers to distinguish patients at high risk of cancer in clinical practice. More detailed studies are needed to clarify the mechanistic pathways.