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Metabolomic Pathway Activity with Genomic Single-Nucleotide Polymorphisms Associated with Colorectal Cancer Recurrence and 5-Year Overall Survival

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Abstract

Purpose

Metabolomic analysis in colorectal cancer (CRC) is an emerging research area with both prognostic and therapeutic targeting potential. We aimed to identify metabolomic pathway activity prognostic for CRC recurrence and overall survival and cross-reference such metabolomic data with prognostic genomic single-nucleotide polymorphisms (SNPs).

Methods

A systematic search of PubMed, Embase and Cochrane Library was performed for studies reporting prognostic metabolomic pathway activity in CRC in keeping with PRISMA guidelines. The QUADOMICS tool was used to assess study quality. MetaboAnalyst software (version4.0) was used to map metabolites that were associated with recurrence and survival in CRC to recognise metabolic pathways and identify genomic SNPs associated with CRC prognosis, referencing the following databases: Human Metabolome Database (HMDB), the Small Molecule Pathway Database (SMPDB), PubChem and Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway Database.

Results

Nine studies met the inclusion criteria, reporting on 1117 patients. Increased metabolic activity in the urea cycle (p = 0.002, FDR = 0.198), ammonia recycling (p = 0.004, FDR = 0.359) and glycine and serine metabolism (p = 0.004, FDR = 0.374) was prognostic of CRC recurrence. Increased activity in aspartate metabolism (p < 0.001, FDR = 0.079) and ammonia recycling (p = 0.004, FDR = 0.345) was prognostic of survival. Eight resulting SNPs were prognostic for CRC recurrence (rs2194980, rs1392880, rs2567397, rs715, rs169712, rs2300701, rs313408, rs7018169) and three for survival (rs2194980, rs169712, rs12106698) of which two overlapped with recurrence (rs2194980, rs169712).

Conclusions

With a caveat on study heterogeneity, specific metabolites and metabolic pathway activity appear evident in the setting of poor prognostic colorectal cancers and such metabolic signatures are associated with specific genomic SNPs.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev. 2011;40(1):387–426.

    Article  CAS  PubMed  Google Scholar 

  2. Romero-Garcia S, Lopez-Gonzalez JS, B´ez-Viveros JL, Aguilar-Cazares D, Prado-Garcia H. Tumor cell metabolism. Cancer Biol Ther. 2014;12(11):939–948.

  3. Janke R, Dodson A, Rine J. Metabolism and epigenetics. Annu Rev Cell Dev Biol. 2015;31:473e496.

  4. Santhanama S, Alvaradoa DM, Ciorba MA. Therapeutic targeting of inflammation and tryptophan metabolism in colon and gastrointestinal cancer. Transl Res. 2016;167(1):67–9.

    Article  Google Scholar 

  5. Bettencourt IA, Powell JD. Targeting metabolism as a novel therapeutic approach to autoimmunity, inflammation, and transplantation. J Immunol. 2017;198(3):999–1005.

    Article  CAS  PubMed  Google Scholar 

  6. Phua LC, et al. Non-invasive fecal metabonomic detection of colorectal cancer. Cancer Biol Ther. 2014;15(4):389–397.

  7. Bertini I, et al. Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Res. 2012;72(37):356–65.

    Article  CAS  PubMed  Google Scholar 

  8. Long Z, et al. Metabolomic markers of colorectal tumor with different clinicopathological features. Frony Oncol. 2020;17(10):981.

    Article  Google Scholar 

  9. Wang Z, et al. Development of a correlative strategy to discover colorectal tumor tissue derived metabolite biomarkers in plasma using untargeted metabolomics. Anal Chem. 2019;91(3):2401–8.

    Article  CAS  PubMed  Google Scholar 

  10. Zhang A, Sun H, Yan G, Wang P, Han Y, Wang X. Metabolomics in diagnosis and biomarker discovery of colorectal cancer. Cancer Lett. 2014;345(1):17–20.

    Article  CAS  PubMed  Google Scholar 

  11. Fidler IJ. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat Rev Cancer. 2002;3(6):453–8.

    Article  Google Scholar 

  12. O’Leary DP, O’Leary E, Foley N, Cotter TG, Wang JH, Redmond HP. Effects of surgery on the cancer stem cell niche. Eur J Surg Oncol. 2016;42(3):319–25.

    Article  PubMed  Google Scholar 

  13. Zhang F, et al. Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review. Oncotarget. 2017;8(21):35460–72.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yoo BC, Kim KH, Woo SM, Myung JK. Clinical multi-omics strategies for the effective cancer management. J Proteomics. 2017;0–1.

  15. Vucic EA, et al. Translating cancer ‘omics’ to improved outcomes paradigm Translating cancer ‘omics’ to improved outcomes. Genome Research. 2012;188–195.

  16. Liao Y. Akt and glucose metabolism in cancer (invited review). J Metab Nutr Cancer. 2014;1(2):80e88.

  17. Dang L, White DW, Gross S, et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature. 2009;462(7274):739e744.

  18. Page MJ, Matthew J, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed.) 2021;372:71.

  19. Reitsma JB, Leeflang MMG, Sterne JAC, Bossuyt PMM. Research and reporting methods accuracy studies. 2011;4.

  20. Lumbreras B, Porta M, Márquez S, Pollán M, Parker LA, Hernández-Aguado I. QUADOMICS: an adaptation of the quality assessment of diagnostic accuracy assessment (QUADAS) for the evaluation of the methodological quality of studies on the diagnostic accuracy of ‘-omics’-based technologies. Clin Biochem. 2008;41(16–17):1316–25.

    Article  PubMed  Google Scholar 

  21. Chong J, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. 2018;0–9.

  22. Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–311. https://doi.org/10.1093/nar/29.1.308.

  23. Cai Y, et al. Sex differences in colon cancer metabolism reveal a novel subphenotype. Sci Rep. 2020;17(1):4905.

    Article  Google Scholar 

  24. Zaimenko I, et al. Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: the DACHS study. Int J Cancer. 2019;145(1):221–31.

    Article  CAS  PubMed  Google Scholar 

  25. De Vroome SW, et al. Serum N-glycome alterations in colorectal cancer associate with survival. Oncotarget. 2018;9(55):30610–23.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pacholczyk-sienicka B, Fabia A. Prediction of survival for patients with advanced colorectal cancer using 1 H high-resolution magic angle spinning nuclear MR spectroscopy. J Magn Reson Imaging. 2015;1674:1669–74.

    Article  Google Scholar 

  27. Qiu Y, et al. A distinct metabolic signature of human colorectal cancer with prognostic potential. Clin Cancer Res. 2014;20(8):2136–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jimenez B, et al. H HR-MAS NMR spectroscopy of tumor-induced local metabolic ‘field-effects’ enables colorectal cancer staging and prognostication. J Proteome Res. 2013;12(2):959–68.

    Article  CAS  PubMed  Google Scholar 

  29. Farshidfar F, et al. Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med. 2012;4(5):42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Beger RD, et al. Metabolomics enables precision medicine: a white paper, community perspective. Metabolomics. 2016;12:10.

  31. Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res. 2009;15(2):431–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang F, et al. Applications of metabolomics in cancer studies. Oncotarget. 2017;345(1):552–74.

    Google Scholar 

  33. Morris E, et al. Surgical management and outcomes of colorectal cancer liver metastases. Br J Surg. 2010;97(7):1110.

  34. Väyrynen V, et al. Incidence and management of patients with colorectal cancer and synchronous and metachronous colorectal metastases: a population-based study. BJS Open. 2020;4(4):685–92.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Shinkins B, et al. Serum carcinoembryonic antigen trends for diagnosing colorectal cancer recurrence in the FACS randomized clinical trial. Br J Surg. 2018;105(6):658–62.

    Article  CAS  PubMed  Google Scholar 

  36. Leong K, Hartley J, Karandikar S. Association of Coloproctology of Great Britain & Ireland (ACPGBI): guidelines for the management of cancer of the colon, rectum and anus (2017) – follow up, lifestyle and survivorship. Color Dis. 2017;19:67–70.

    Article  Google Scholar 

  37. Ryuk JP, et al. Predictive factors and the prognosis of recurrence of colorectal cancer within 2 years after curative resection. Ann Surg Treat Res. 2014;86(3):143–51.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Van der Geest LGM, Lam-Boer J, Koopman M, Verhoef C, Elferink MAG, de Wilt JHW. Nationwide trends in incidence, treatment and survival of colorectal cancer patients with synchronous metastases. Clin Exp Metastasis. 2015;32(5):457–65.

    Article  PubMed  Google Scholar 

  39. Keshet R, Szlosarek P, Carracedo A, Erez A. Rewiring urea cycle metabolism in cancer to support anabolism. Nat Rev Cancer. 2018;18.

  40. Adeva M, Souto G, Blanco N, Donapetry C. Ammonia metabolism in humans. Metabolism. 2012;61:1495–511.

    Article  CAS  PubMed  Google Scholar 

  41. DeBerardinis R, Cheng T. The diverse functions of glutamine in metabolism, cell biology and cancer. Oncogene. 2010;29:313–324.

  42. Coloff J, et al. Differential glutamate metabolism in proliferating and quiescent mammary epithelial cells. Cell Metab. 2016;23:867–880.

  43. Haigis MC. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science. 2018;358(6365):941–6.

    Google Scholar 

  44. Gu Y, et al. Perioperative dynamics and significance of amino acid profiles in patients with cancer. J Transl Med. 2015;13(1):1–14.

    Article  CAS  Google Scholar 

  45. Antonov A, Amelio I, Cutruzzola F, Agostini M, Melino G. Serine and glycine metabolism in cancer. Trends Biochem Sci. 2014;39(4):191–8.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Potter M, Newport E, Morten K. The Warburg effect: 80 years on. Biochem Soc Trans. 2016;44(5)1499e1505.

  47. Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927;8:519–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shyh-Chang N, et al. Influence of threonine metabolism on S-adenosylmethionine and histone methylation. Science. 2013;339:222–22638.

  49. Maddocks OD, et al. Serine starvation induces stress and p53-dependent metabolic remodelling in cancer cells. Nature. 2013;493:542–546.

  50. Han T, et al. How does cancer cell metabolism affect tumor migration and invasion? Cell Adhes Migr. 2013;7(5):395–403.

    Article  Google Scholar 

  51. Mayers J, Vander Heiden M. Famine versus feast: understanding the metabolism of tumors in vivo. Trends Biochem Sci. 2015;40:130–140.

  52. Batool T, Makky E, Jalal M, Yusoff M. A comprehensive review on l-asparaginase and its applications. Appl Biochem Biotechnol. 2016;178:900–23.

    Article  CAS  PubMed  Google Scholar 

  53. Wu X, Zhao J, Ruan Y, Sun L, Xu C, Jiang H. Sialyltransferase ST3GAL1 promotes cell migration, invasion, and TGF-β1-induced EMT and confers paclitaxel resistance in ovarian cancer. Cell Death Dis. 2018;9(11).

  54. Zhang L, Al E. Effects of Kras activation and Pten deletion alone or in combination on MUC1 biology and epithelial-to-mesenchymal transition in ovarian cancer. Oncogene. 2016;35:5010–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Canel M, Serrels A, Frame M, Brunton V. E-cadherin-integrin crosstalk in cancer invasion and metastasis. J Cell Sci. 2013;126:393–401.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

All studies from which data was extracted are accurately referenced.

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Authors and Affiliations

  1. Department of Colorectal Surgery, Cork University Hospital, Cork, Ireland

    Christina A. Fleming

  2. Department of Academic Surgery, Cork University Hospital, Cork, Ireland

    Christina A. Fleming, Mark Corrigan & H. Paul Redmond

  3. Department of Colorectal Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia

    Helen M. Mohan

  4. Department of Surgical Oncology, Bon Secours Cork Cancer Centre, Cork, Ireland

    Donal P. O’Leary

  5. Department of Surgical Oncology, Cork University Hospital, Cork, Ireland

    H. Paul Redmond

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  1. Christina A. Fleming
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Correspondence to Christina A. Fleming.

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Previous communication: Awarded the British Association of Surgical Oncology (BASO)-Association of Cancer Surgeons (ACS) Proffered Paper Prize, November 2020

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Fleming, C.A., Mohan, H.M., O’Leary, D.P. et al. Metabolomic Pathway Activity with Genomic Single-Nucleotide Polymorphisms Associated with Colorectal Cancer Recurrence and 5-Year Overall Survival. J Gastrointest Canc 54, 247–258 (2023). https://doi.org/10.1007/s12029-022-00813-3

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