Human blood metabolites and the risk of colorectal cancer: A Mendelian randomization study
Abstract
Background: Metabolomics can offer vital information into a cancer’s condition. Despite its potential, research on the metabolites linked to colorectal cancer (CRC) remains limited. From a cell molecular biomechanics perspective, understanding these metabolite associations can offer a deeper understanding of the disease’s underlying mechanisms. We performed Mendelian randomisation (MR) analyses to investigate causal associations between 486 blood metabolites and CRC. Methods: Data on blood metabolites were derived from a Genome-wide association study (GWAS) involving 7824 Europeans. Additionally, summary statistics for CRC were sourced from the FinnGen consortium database. To explore the causal relationship between CRC and blood metabolites, we primarily utilized the inverse variance weighted (IVW) analysis. Supplementary analyses incorporated MR-Egger and weighted median methods to ensure the robustness of our findings. The potential for pleiotropic effects was evaluated using the Cochran’s Q test and the MR-Egger intercept test. Furthermore, colocalization analyses were performed to ascertain whether the observed associations were influenced by specific genetic loci within the genomic region. Results: The results of this study indicated significant associations between eight metabolites: Indolelactate (OR = 2.62, 95% confidence interval (CI): 0.26–1.66, p = 0.007), 1-heptadecanoylglycerophosphocholine (OR = 1.37, 95% CI: 0.10–0.54, p = 0.005), 1-stearoylglycerophosphocholine (OR = 3.47, 95% CI: 0.65–1.84, p = 0.00005) , X-11792 (OR = 0.57, 95% CI: −0.94–−0.17, p = 0.005), X-12038 (OR = 0.44, 95% CI: −1.50–−0. 12, p = 0.021), X-12212 (OR = 1.96, 95% CI: 0.10–1.25, p = 0. 022), X-14056 (OR = 0.50, 95% CI: −1.28–−0.12, p = 0.018) , X-14745 (OR 0.41, 95% CI: −1.48–−0.31, p = 0.003) and CRC. These metabolites might play roles in altering the mechanical properties of cells in the colon. They could potentially affect the cytoskeletal structure, cell membrane fluidity, or the way cells interact with the extracellular matrix. Conclusion: The eight identified blood metabolites with causative influence on CRC provide valuable clues for understanding CRC from a cell molecular biomechanics angle, which can further aid in its screening, prevention, and treatment strategies.
References
1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. Jan 2022; 72(1): 7–33. doi: 10.3322/caac.21708
2. Coppedè F, Lopomo A, Spisni R, Migliore L. Genetic and epigenetic biomarkers for diagnosis, prognosis and treatment of colorectal cancer. World J Gastroenterol. Jan 28 2014; 20(4): 943–56. doi: 10.3748/wjg.v20.i4.943
3. La Vecchia S, Sebastián C. Metabolic pathways regulating colorectal cancer initiation and progression. Semin Cell Dev Biol. Feb 2020; 98: 63–70. doi: 10.1016/j.semcdb.2019.05.018
4. Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin. Jul 2021; 71(4): 333–358. doi: 10.3322/caac.21670
5. Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Science. Apr 10 2020; 368(6487). doi: 10.1126/science.aaw5473
6. Fathi AT, Sadrzadeh H, Borger DR, et al. Prospective serial evaluation of 2-hydroxyglutarate, during treatment of newly diagnosed acute myeloid leukemia, to assess disease activity and therapeutic response. Blood. Nov 29 2012; 120(23): 4649–52. doi: 10.1182/blood-2012-06-438267
7. Dang L, White DW, Gross S, et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature. Dec 10 2009; 462(7274): 739–44. doi: 10.1038/nature08617
8. Ward PS, Patel J, Wise DR, et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell. Mar 16 2010; 17(3): 225–34. doi: 10.1016/j.ccr.2010.01.020
9. Kasprzak A. Insulin-Like Growth Factor 1 (IGF-1) Signaling in Glucose Metabolism in Colorectal Cancer. Int J Mol Sci. Jun 16 2021; 22(12). doi: 10.3390/ijms22126434
10. Gao R, Wu C, Zhu Y, et al. Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites. Gastroenterology. Oct 2022; 163(4): 1024–1037.e9. doi: 10.1053/j.gastro.2022.06.069
11. Cross AJ, Moore SC, Boca S, et al. A prospective study of serum metabolites and colorectal cancer risk. Cancer. Oct 1 2014; 120(19): 3049–57. doi: 10.1002/cncr.28799
12. Chen B, Yan Y, Wang H, Xu J. Association between genetically determined telomere length and health-related outcomes: A systematic review and meta-analysis of Mendelian randomization studies. Aging Cell. Jul 2023; 22(7): e13874. doi: 10.1111/acel.13874
13. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. May 2017; 32(5): 377–389. doi: 10.1007/s10654-017-0255-x
14. Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. Jun 2014; 46(6): 543–550. doi: 10.1038/ng.2982
15. Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. Jan 2023; 613(7944): 508–518. doi: 10.1038/s41586-022-05473-8
16. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. Nov 2013; 37(7): 658–65. doi: 10.1002/gepi.21758
17. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. Oct 1 2013; 178(7): 1177–84. doi: 10.1093/aje/kwt084
18. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. May 2016; 40(4): 304–14. doi: 10.1002/gepi.21965
19. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. Sep 20 2015; 34(21): 2926–40. doi: 10.1002/sim.6522
20. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. May 2014; 10(5): e1004383. doi: 10.1371/journal.pgen.1004383
21. Bae SC, Lee YH. Causal association between body mass index and risk of rheumatoid arthritis: A Mendelian randomization study. Eur J Clin Invest. Apr 2019; 49(4): e13076. doi: 10.1111/eci.13076
22. Yun Z, Guo Z, Li X, et al. Genetically predicted 486 blood metabolites in relation to risk of colorectal cancer: A Mendelian randomization study. Cancer Med. Jun 2023; 12(12): 13784–13799. doi: 10.1002/cam4.6022
23. Morita I, Kawamoto M, Hattori M, Eguchi K, Sekiba K, Yoshida H. Determination of tryptophan and its metabolites in human plasma and serum by high-performance liquid chromatography with automated sample clean-up system. J Chromatogr. Apr 6 1990; 526(2): 367–74. doi: 10.1016/s0378-4347(00)82520-7
24. Goedert JJ, Sampson JN, Moore SC, et al. Fecal metabolomics: assay performance and association with colorectal cancer. Carcinogenesis. Sep 2014; 35(9): 2089–96. doi: 10.1093/carcin/bgu131
25. Brown DG, Rao S, Weir TL, et al. Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool. Cancer Metab. 2016; 4: 11. doi: 10.1186/s40170-016-0151-y
26. Zhang Q, Zhao Q, Li T, et al. Lactobacillus plantarum-derived indole-3-lactic acid ameliorates colorectal tumorigenesis via epigenetic regulation of CD8(+) T cell immunity. Cell Metab. Jun 6 2023; 35(6): 943–960.e9. doi: 10.1016/j.cmet.2023.04.015
27. Knuplez E, Marsche G. An Updated Review of Pro- and Anti-Inflammatory Properties of Plasma Lysophosphatidylcholines in the Vascular System. Int J Mol Sci. Jun 24 2020; 21(12). doi: 10.3390/ijms21124501
28. Tan ST, Ramesh T, Toh XR, Nguyen LN. Emerging roles of lysophospholipids in health and disease. Prog Lipid Res. Nov 2020; 80: 101068. doi: 10.1016/j.plipres.2020.101068
29. Meyer zu Heringdorf D, Jakobs KH. Lysophospholipid receptors: signalling, pharmacology and regulation by lysophospholipid metabolism. Biochim Biophys Acta. Apr 2007; 1768(4): 923–40. doi: 10.1016/j.bbamem.2006.09.026
30. Tagesson C, Franzén L, Dahl G, Weström B. Lysophosphatidylcholine increases rat ileal permeability to macromolecules. Gut. Apr 1985; 26(4): 369–77. doi: 10.1136/gut.26.4.369
31. Tang X, Wang W, Hong G, et al. Gut microbiota-mediated lysophosphatidylcholine generation promotes colitis in intestinal epithelium-specific Fut2 deficiency. J Biomed Sci. Mar 15 2021; 28(1): 20. doi: 10.1186/s12929-021-00711-z
32. Ecker J, Benedetti E, Kindt ASD, et al. The Colorectal Cancer Lipidome: Identification of a Robust Tumor-Specific Lipid Species Signature. Gastroenterology. Sep 2021; 161(3): 910–923.e19. doi: 10.1053/j.gastro.2021.05.009
33. Yang J, Wei H, Zhou Y, et al. High-Fat Diet Promotes Colorectal Tumorigenesis Through Modulating Gut Microbiota and Metabolites. Gastroenterology. Jan 2022; 162(1): 135–149.e2. doi: 10.1053/j.gastro.2021.08.041
34. Bao L, Zhang Y, Yan S, Yan D, Jiang D. Lysophosphatidylcholine (17:0) Improves HFD-Induced Hyperglycemia & Insulin Resistance: A Mechanistic Mice Model Study. Diabetes Metab Syndr Obes. 2022; 15: 3511–3517. doi: 10.2147/dmso.S371370
35. Bi J, Ichu TA, Zanca C, et al. Oncogene Amplification in Growth Factor Signaling Pathways Renders Cancers Dependent on Membrane Lipid Remodeling. Cell Metab. Sep 3 2019; 30(3): 525–538.e8. doi: 10.1016/j.cmet.2019.06.014
36. Hilvo M, Denkert C, Lehtinen L, et al. Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res. May 1 2011; 71(9): 3236–45. doi: 10.1158/0008-5472.Can-10-3894
37. Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. Feb 2003; 32(1): 1–22. doi: 10.1093/ije/dyg070
38. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj. Jul 12 2018; 362: k601. doi: 10.1136/bmj.k601
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