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

Yi Feng; Runchen Wang; Caichen Li; Xiuyu Cai; Zhenyu Huo; Ziyu Liu; Fan Ge; Chuiguo Huang; Yi Lu; Ran Zhong; Jianfu Li; Bo Cheng; Hengrui Liang; Shan Xiong; Xingyu Mao; Yilin Chen; Ruying Lan; Yaokai Wen; Haoxin Peng; Yu Jiang; Zixuan Su; Xiangrong Wu; Jianxing He; Wenhua Liang

doi:10.21037/tlcr-22-34

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

Transl Lung Cancer Res. 2022 Jul;11(7):1302-1314. doi: 10.21037/tlcr-22-34.

Authors

Yi Feng^{1

2

3}, Runchen Wang^{1

2

3}, Caichen Li^{1

2}, Xiuyu Cai⁴, Zhenyu Huo^{1

2

3}, Ziyu Liu^{1

2

3}, Fan Ge^{1

2

5}, Chuiguo Huang⁶, Yi Lu^{1

2

3}, Ran Zhong^{1

2}, Jianfu Li^{1

2}, Bo Cheng^{1

2}, Hengrui Liang^{1

2}, Shan Xiong^{1

2}, Xingyu Mao⁷, Yilin Chen⁸, Ruying Lan^{1

2

3}, Yaokai Wen^{1

2

3}, Haoxin Peng^{1

2

3}, Yu Jiang^{1

2

3}, Zixuan Su^{1

2

3}, Xiangrong Wu^{1

2

3}, Jianxing He^{1

2

9}, Wenhua Liang^{1

2

10}

Affiliations

¹ Department of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
² Guangzhou Institute of Respiratory Health, Guangzhou, China.
³ Nanshan School, Guangzhou Medical University, Guangzhou, China.
⁴ Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
⁵ First Clinical School, Guangzhou Medical University, Guangzhou, China.
⁶ Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
⁷ Department of Economics and Management, Shanghai Institute of Technology, Shanghai, China.
⁸ State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
⁹ Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, China.
¹⁰ Department of Oncology Medical Center, The First People's Hospital of Zhaoqing, Zhaoqing, China.

Abstract

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.

Keywords: 2-methylbutyroylcarnitine; Mendelian randomization (MR); Serum metabolite; cancer.