Causal Association Between 637 Human Metabolites and Ovarian Cancer: a Mendelian Randomization Study

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2024 Jan 23

PMID 38262941

Authors

Yedong Huang

Wenyu Lin

Xiangqin Zheng

Affiliations

Soon will be listed here.

Abstract

Background: Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

Methods: In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables.

Results: The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

Conclusions: The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Citing Articles

Investigation of risk signatures associated with anoikis in thyroid cancer through integrated transcriptome and Mendelian randomization analysis.

Chen X, Lai J, Shen W, Wang D, Wei Z Front Endocrinol (Lausanne). 2024; 15:1458956.

PMID: 39568815 PMC: 11576184. DOI: 10.3389/fendo.2024.1458956.

References

Indiveri C, Iacobazzi V, Tonazzi A, Giangregorio N, Infantino V, Convertini P . The mitochondrial carnitine/acylcarnitine carrier: function, structure and physiopathology. Mol Aspects Med. 2011; 32(4-6):223-33. DOI: 10.1016/j.mam.2011.10.008. View

Aus A . Can we prevent ovarian cancer?. Ceska Gynekol. 2020; 85(1):49-58. View

Heldman D, Grever M, Trewyn R . Differential excretion of modified nucleosides in adult acute leukemia. Blood. 1983; 61(2):291-6. View

Fan L, Yin M, Ke C, Ge T, Zhang G, Zhang W . Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer. J Cancer. 2016; 7(10):1265-72. PMC: 4934035. DOI: 10.7150/jca.15074. View

Guan S, Liu Y, Guo Y, Shen X, Liu Y, Jin H . Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: Searching with metabolomics. Front Endocrinol (Lausanne). 2022; 13:982200. PMC: 9478024. DOI: 10.3389/fendo.2022.982200. View

Sun L, Guo D, Jia Y, Shi M, Yang P, Wang Y . Association between Human Blood Metabolome and the Risk of Alzheimer's Disease. Ann Neurol. 2022; 92(5):756-767. DOI: 10.1002/ana.26464. View

Siegel R, Miller K, Fuchs H, Jemal A . Cancer Statistics, 2021. CA Cancer J Clin. 2021; 71(1):7-33. DOI: 10.3322/caac.21654. View

Jacob M, Lopata A, Dasouki M, Abdel Rahman A . Metabolomics toward personalized medicine. Mass Spectrom Rev. 2017; 38(3):221-238. DOI: 10.1002/mas.21548. View

Petricoin E, Ardekani A, Hitt B, Levine P, Fusaro V, Steinberg S . Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002; 359(9306):572-7. DOI: 10.1016/S0140-6736(02)07746-2. View

10.

Long J, Zhang C, Zhu N, Du K, Yin Y, Tan X . Lipid metabolism and carcinogenesis, cancer development. Am J Cancer Res. 2018; 8(5):778-791. PMC: 5992506. View

11.

Chen J, Zhang X, Cao R, Lu X, Zhao S, Fekete A . Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J Proteome Res. 2011; 10(5):2625-32. DOI: 10.1021/pr200173q. View

12.

Guo J, Xiao Q, Gao S, Li X, Wu Q, Gong T . Review of Mendelian Randomization Studies on Ovarian Cancer. Front Oncol. 2021; 11:681396. PMC: 8385140. DOI: 10.3389/fonc.2021.681396. View

13.

Kettunen J, Demirkan A, Wurtz P, Draisma H, Haller T, Rawal R . Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016; 7:11122. PMC: 4814583. DOI: 10.1038/ncomms11122. View

14.

Hanahan D, Weinberg R . Hallmarks of cancer: the next generation. Cell. 2011; 144(5):646-74. DOI: 10.1016/j.cell.2011.02.013. View

15.

Chen L, Wang S, Zhang Y, Li Y, Zhang X, Ma J . Multi-omics reveals specific host metabolism-microbiome associations in intracerebral hemorrhage. Front Cell Infect Microbiol. 2023; 12:999627. PMC: 9813413. DOI: 10.3389/fcimb.2022.999627. View

16.

Shin S, Fauman E, Petersen A, Krumsiek J, Santos R, Huang J . An atlas of genetic influences on human blood metabolites. Nat Genet. 2014; 46(6):543-550. PMC: 4064254. DOI: 10.1038/ng.2982. View

17.

Schulze A, Harris A . How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature. 2012; 491(7424):364-73. DOI: 10.1038/nature11706. View

18.

Geng F, Guo D . Lipid droplets, potential biomarker and metabolic target in glioblastoma. Intern Med Rev (Wash D C). 2017; 3(5). PMC: 5639724. DOI: 10.18103/imr.v3i5.443. View

19.

Burgess S, Scott R, Timpson N, Davey Smith G, Thompson S . Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015; 30(7):543-52. PMC: 4516908. DOI: 10.1007/s10654-015-0011-z. View

20.

Brion M, Shakhbazov K, Visscher P . Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013; 42(5):1497-501. PMC: 3807619. DOI: 10.1093/ije/dyt179. View