Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Metabolomics analysis identifies lysine and taurine as candidate prognostic biomarkers for AML-M2 patients


There is an ongoing search for potential biomarkers for acute myeloid leukemia (AML) patients using metabolic analysis. However, only few studies to date have focused on bone marrow samples or a specific subtype of AML. In the present study, we used gas chromatography time-of-flight mass spectrometry of plasma and bone marrow supernatants to compare the metabolic characteristics of patients with AML with maturation (AML-M2). This approach identified significantly altered metabolites. We next performed pathway analysis and determined relative mRNA expression by qRT-PCR. Our results show that lysine, methionine and serine were significantly decreased in AML-M2 patients compared with healthy control. Moreover, plasma abundance of lysine was negatively associated with patients’ risk stratification. Taurine had higher plasma abundance in AML-M2 patients and plasma level of taurine was positively related with AML-M2 risk status, while the expression level of taurine transporter showed a negative correlation. Receiver operating characteristic curve analysis showed these four metabolites had high diagnostic value with lysine showing the highest sensitivity and specificity. These results suggest that plasma abundances of lysine and taurine may serve as potential metabolic biomarkers for the prognosis of patients with AML-M2.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Foran JM. New prognostic markers in acute myeloid leukemia: perspective from the clinic. Hematol Am Soc Hematol Educ Program. 2010;2010:47–55. https://doi.org/10.1182/asheducation-2010.1.47.

  2. 2.

    Prada-Arismendy J, Arroyave JC, Rothlisberger S. Molecular biomarkers in acute myeloid leukemia. Blood Rev. 2017;31(1):63–766. https://doi.org/10.1016/j.blre.2016.08.005.

  3. 3.

    Gallipoli P, Giotopoulos G, Tzelepis K, Costa ASH, Vohra S, Medina-Perez P, et al. Glutaminolysis is a metabolic dependency in FLT3(ITD) acute myeloid leukemia unmasked by FLT3 tyrosine kinase inhibition. Blood. 2018;131(15):1639–53. https://doi.org/10.1182/blood-2017-12-820035.

  4. 4.

    Liu X, Locasale JW. Metabolomics: a primer. Trends Biochem Sci. 2017;42(4):274–84. https://doi.org/10.1016/j.tibs.2017.01.004.

  5. 5.

    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. https://doi.org/10.1016/j.cell.2011.02.013.

  6. 6.

    Pasikanti KK, Esuvaranathan K, Hong Y, Ho PC, Mahendran R, Raman Nee Mani L, et al. Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J Proteome Res. 2013;12(9):3865–73. https://doi.org/10.1021/pr4000448.

  7. 7.

    Mondal S, Roy D, Camacho-Pereira J, Khurana A, Chini E, Yang L, et al. HSulf-1 deficiency dictates a metabolic reprograming of glycolysis and TCA cycle in ovarian cancer. Oncotarget. 2015;6(32):33705–19. https://doi.org/10.18632/oncotarget.5605.

  8. 8.

    Bro R, Kamstrup-Nielsen MH, Engelsen SB, Savorani F, Rasmussen MA, Hansen L, et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics. 2015;11(5):1376–80. https://doi.org/10.1007/s11306-015-0793-8.

  9. 9.

    Kumar N, Shahjaman M, Mollah MNH, Islam SMS, Hoque MA. Serum and plasma metabolomic biomarkers for lung cancer. Bioinformation. 2017;13(6):202–8. https://doi.org/10.6026/97320630013202.

  10. 10.

    Yin P, Xu G. Metabolomics toward biomarker discovery. Methods Mol Biol. 2017;1619:467–75. https://doi.org/10.1007/978-1-4939-7057-5_32.

  11. 11.

    Du H, Wang L, Liu B, Wang J, Su H, Zhang T, et al. Analysis of the metabolic characteristics of serum samples in patients with multiple myeloma. Front Pharmacol. 2018;9:884. https://doi.org/10.3389/fphar.2018.00884.

  12. 12.

    Musharraf SG, Siddiqui AJ, Shamsi T, Naz A. SERUM metabolomics of acute lymphoblastic leukaemia and acute myeloid leukaemia for probing biomarker molecules. Hematol Oncol. 2017;35(4):769–77. https://doi.org/10.1002/hon.2313.

  13. 13.

    Warburg O. On the origin of cancer cells. Science. 1956;123(3191):309–14.

  14. 14.

    Herst PM, Howman RA, Neeson PJ, Berridge MV, Ritchie DS. The level of glycolytic metabolism in acute myeloid leukemia blasts at diagnosis is prognostic for clinical outcome. J Leukoc Biol. 2011;89(1):51–5. https://doi.org/10.1189/jlb.0710417.

  15. 15.

    Wang JH, Chen WL, Li JM, Wu SF, Chen TL, Zhu YM, et al. Prognostic significance of 2-hydroxyglutarate levels in acute myeloid leukemia in China. Proc Natl Acad Sci USA. 2013;110(42):17017–22. https://doi.org/10.1073/pnas.1315558110.

  16. 16.

    Wang Y, Zhang L, Chen WL, Wang JH, Li N, Li JM, et al. Rapid diagnosis and prognosis of de novo acute myeloid leukemia by serum metabonomic analysis. J Proteome Res. 2013;12(10):4393–401. https://doi.org/10.1021/pr400403p.

  17. 17.

    Shafat MS, Gnaneswaran B, Bowles KM, Rushworth SA. The bone marrow microenvironment—home of the leukemic blasts. Blood Rev. 2017;31(5):277–86. https://doi.org/10.1016/j.blre.2017.03.004.

  18. 18.

    Aa J, Yu L, Sun M, Liu L, Li M, Cao B, et al. Metabolic features of the tumor microenvironment of gastric cancer and the link to the systemic macroenvironment. Metabolomics. 2012;8(1):164–73. https://doi.org/10.1007/s11306-011-0297-0.

  19. 19.

    Bjerrum JT. Metabonomics: analytical techniques and associated chemometrics at a glance. Methods Mol Biol. 2015;1277:1–14. https://doi.org/10.1007/978-1-4939-2377-9_1.

  20. 20.

    Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J Proteome Res. 2007;6(2):469–79. https://doi.org/10.1021/pr060594q.

  21. 21.

    Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46(W1):W486–W494494. https://doi.org/10.1093/nar/gky310.

  22. 22.

    Son J, Lyssiotis CA, Ying H, Wang X, Hua S, Ligorio M, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature. 2013;496(7443):101–5. https://doi.org/10.1038/nature12040.

  23. 23.

    Gao P, Tchernyshyov I, Chang TC, Lee YS, Kita K, Ochi T, et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 2009;458(7239):762–5. https://doi.org/10.1038/nature07823.

  24. 24.

    Wise DR, Thompson CB. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem Sci. 2010;35(8):427–33. https://doi.org/10.1016/j.tibs.2010.05.003.

  25. 25.

    Liu XX, Li XJ, Zhang B, Liang YJ, Zhou CX, Cao DX, et al. MicroRNA-26b is underexpressed in human breast cancer and induces cell apoptosis by targeting SLC7A11. FEBS Lett. 2011;585(9):1363–7. https://doi.org/10.1016/j.febslet.2011.04.018.

  26. 26.

    Zhao X, Li Y, Wu H. A novel scoring system for acute myeloid leukemia risk assessment based on the expression levels of six genes. Int J Mol Med. 2018;42(3):1495–507. https://doi.org/10.3892/ijmm.2018.3739.

  27. 27.

    Frezza C. Cancer metabolism: addicted to serine. Nat Chem Biol. 2016;12(6):389–90. https://doi.org/10.1038/nchembio.2086.

  28. 28.

    Mattaini KR, Sullivan MR, Vander Heiden MG. The importance of serine metabolism in cancer. J Cell Biol. 2016;214(3):249–57. https://doi.org/10.1083/jcb.201604085.

  29. 29.

    Newman AC, Maddocks ODK. Serine and functional metabolites in cancer. Trends Cell Biol. 2017;27(9):645–57. https://doi.org/10.1016/j.tcb.2017.05.001.

  30. 30.

    Maddocks ODK, Athineos D, Cheung EC, Lee P, Zhang T, van den Broek NJF, et al. Modulating the therapeutic response of tumours to dietary serine and glycine starvation. Nature. 2017;544(7650):372–6. https://doi.org/10.1038/nature22056.

  31. 31.

    Wojtowicz W, Chachaj A, Olczak A, Zabek A, Piatkowska E, Rybka J, et al. Serum NMR metabolomics to differentiate haematologic malignancies. Oncotarget. 2018;9(36):24414–27. https://doi.org/10.18632/oncotarget.25311.

  32. 32.

    Chen WL, Wang JH, Zhao AH, Xu X, Wang YH, Chen TL, et al. A distinct glucose metabolism signature of acute myeloid leukemia with prognostic value. Blood. 2014;124(10):1645–54. https://doi.org/10.1182/blood-2014-02-554204.

  33. 33.

    Wang X, Zhao X, Chou J, Yu J, Yang T, Liu L, et al. Taurine, glutamic acid and ethylmalonic acid as important metabolites for detecting human breast cancer based on the targeted metabolomics. Cancer Biomark. 2018;23(2):255–68. https://doi.org/10.3233/cbm-181500.

  34. 34.

    Dazhi W, Jing D, Chunling R, Mi Z, Zhixuan X. Elevated SLC6A6 expression drives tumorigenesis and affects clinical outcomes in gastric cancer. Biomark Med. 2019;13(2):95–104. https://doi.org/10.2217/bmm-2018-0256.

  35. 35.

    Stuani L, Riols F, Millard P. Stable isotope labeling highlights enhanced fatty acid and lipid metabolism in human acute myeloid leukemia. Int J Mol Sci. 2018;19(11):3325. https://doi.org/10.3390/ijms19113325.

  36. 36.

    Hao GW, Chen YS, He DM, Wang HY, Wu GH, Zhang B. Growth of human colon cancer cells in nude mice is delayed by ketogenic diet with or without omega-3 fatty acids and medium-chain triglycerides. Asian Pac J Cancer Prev. 2015;16(5):2061–8.

  37. 37.

    Li L, Pilo GM, Li X, Cigliano A, Latte G, Che L, et al. Inactivation of fatty acid synthase impairs hepatocarcinogenesis driven by AKT in mice and humans. J Hepatol. 2016;64(2):333–41. https://doi.org/10.1016/j.jhep.2015.10.004.

Download references


This work was supported by the National Natural Science Foundation of China (31371399).

Author information

Correspondence to Jiye Aa or Bei Cao or Juan Li.

Ethics declarations

Conflict of interest

All authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1: Typical GC/TOFMS total ion current (TIC) chromatograms of samples in AML-M2 patients and healthy donors. The red line represents control group while the black line represents patients group. (TIF 56 kb)

Supplementary Figure 2: Heat maps of the global metabolome for samples from AML patients and controls. (A) Plasma. (B) Bone marrow. (TIF 5104 kb)

Supplementary Figure 3: Kaplan‐Meier survival analysis of AML-M2 patients based on the abundance of (A) lysine, (B) methionine and (C) serine. (TIF 330 kb)

Supplementary Figure 4: Pathway analysis of significantly differential metabolites between patients and controls. (A) Plasma. (B) Bone marrow (TIF 2508 kb)

Supplementary Figure 5: Oncomine data-mining analysis of SLC6A6 in cancer. (A) Down-regulation of SLC6A6 was found in leukemia and lymphoma. (B) In Haferlach Leukemia’s dataset, the level of SLC6A6 mRNA was decreased in acute myeloid leukemia with all four probes (205920_at, 205921_s_at, 211030_s_at, 228754_at). The p-values were 0.002, 0.057, 0.007, and 1.99E-5, separately (TIF 2367 kb)

Supplementary Table 1: Primer sequences used for qRT-PCR (DOCX 14 kb)

Supplementary Table 2: Metabolites with significantly differential abundance in plasma samples (DOCX 17 kb)

Supplementary Table 3: Metabolites with significantly differential abundance in bone marrow samples (DOCX 17 kb)

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhou, X., Zheng, M., Wang, Q. et al. Metabolomics analysis identifies lysine and taurine as candidate prognostic biomarkers for AML-M2 patients. Int J Hematol (2020). https://doi.org/10.1007/s12185-020-02836-7

Download citation


  • Subtype 2 acute myeloid leukemia
  • Metabolomics
  • Biomarker
  • Lysine
  • Taurine