Skip to main content

Advertisement

SpringerLink
Log in

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin’s lymphoma urinary metabolite markers

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Non-Hodgkin’s lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease.

Graphical Abstract

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are under curation process in the Metabolights repository, https://www.ebi.ac.uk/metabolights/MTBLS1816

References

  1. Pearce L. Non-Hodgkin’s lymphoma. Nurs Stand. 2016;31:15.

    PubMed  Google Scholar 

  2. Ansell SM. Non-Hodgkin lymphoma: diagnosis and treatment. Mayo Clin Proc. 2015;90:1152–63.

    PubMed  Google Scholar 

  3. Shankland KR, Armitage JO, Hancock BW. Non-Hodgkin lymphoma. Lancet. 2012;380:848–57.

    PubMed  Google Scholar 

  4. Non-Hodgkin lymphoma In: Global Cancer Observatory https://gco.iarc.fr/today/data/factsheets/cancers/34-Non-hodgkin-lymphoma-fact-sheet.pdf. Accessed 11 Jan 2020.

  5. Key Statistics for Non-Hodgkin Lymphoma In: American Cancer Society https://www.cancer.org/cancer/non-hodgkin-lymphoma/about/key-statistics.html. Accessed 10 Dec 2019.

  6. Ansell SM, Armitage J. Non-Hodgkin lymphoma: diagnosis and treatment. Mayo Clin Proc. 2005;80:1087–97.

    PubMed  CAS  Google Scholar 

  7. Marchand CR, Farshidfar F, Rattner J, Bathe OF. A framework for development of useful metabolomic biomarkers and their effective knowledge translation. Metabolites. 2018;8:59.

    PubMed Central  Google Scholar 

  8. Barichello S, Deng L, Ismond KP, Loomes DE, Kirwin EM, Wang H, et al. Comparative effectiveness and cost-effectiveness analysis of a urine metabolomics test vs. alternative colorectal cancer screening strategies. Int J Color Dis. 2019;34:1953–62.

    Google Scholar 

  9. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13:263–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  10. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–33.

    PubMed  PubMed Central  CAS  Google Scholar 

  11. Kumar A, Misra BB. Challenges and opportunities in cancer metabolomics. Proteomics. 2019;19:e1900042.

    PubMed  Google Scholar 

  12. Barberini L, Restivo A, Noto A, Deidda S, Fattuoni C, Fanos V, et al. A gas chromatography-mass spectrometry (GC-MS) metabolomic approach in human colorectal cancer (CRC): the emerging role of monosaccharides and amino acids. Ann Transl Med. 2019;7:727.

    PubMed  PubMed Central  CAS  Google Scholar 

  13. Chan EC, Koh PK, Mal M, Cheah PY, Eu KW, Backshall A, et al. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res. 2009;8:352–61.

    PubMed  CAS  Google Scholar 

  14. Spivack S, Shi MK, Patel D, Desai A, Dobkin J, Shah C, et al. P1.11-11 initial discovery of exhaled small polar energetics-related metabolites by GC-MS for lung cancer risk assessment. J Thorac Oncol. 2019;14:S519.

    Google Scholar 

  15. Filipiak W, Sponring A, Filipiak A, Ager C, Schubert J, Miekisch W, et al. TD-GC-MS analysis of volatile metabolites of human lung cancer and normal cells in vitro. Cancer Epidemiol Biomark Prev. 2010;19:182–95.

    CAS  Google Scholar 

  16. Poli D, Goldoni M, Corradi M, Acampa O, Carbognani P, Internullo E, et al. Determination of aldehydes in exhaled breath of patients with lung cancer by means of on-fiber-derivatisation SPME-GC/MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2010;878:2643–51.

    PubMed  CAS  Google Scholar 

  17. Phillips M, Altorki N, Austin JH, Cameron RB, Cataneo RN, Greenberg J, et al. Prediction of lung cancer using volatile biomarkers in breath. Cancer Biomark. 2007;3:95–109.

    PubMed  CAS  Google Scholar 

  18. Rodrigues D, Pinto J, Araujo AM, Jeronimo C, Henrique R, Bastos ML, et al. GC-MS metabolomics reveals distinct profiles of low- and high-grade bladder cancer cultured cells. Metabolites. 2019;9.

  19. Zhou Y, Song R, Ma C, Zhou L, Liu X, Yin P, et al. Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method. Oncotarget. 2017;8:20719–28.

    PubMed  PubMed Central  Google Scholar 

  20. Zhou Y, Song R, Zhang Z, Lu X, Zeng Z, Hu C, et al. The development of plasma pseudotargeted GC-MS metabolic profiling and its application in bladder cancer. Anal Bioanal Chem. 2016;408:6741–9.

    PubMed  CAS  Google Scholar 

  21. Hadi NI, Jamal Q, Iqbal A, Shaikh F, Somroo S, Musharraf SG. Serum metabolomic profiles for breast cancer diagnosis, grading and staging by gas chromatography-mass spectrometry. Sci Rep. 2017;7:1715.

    PubMed  PubMed Central  Google Scholar 

  22. Lima AR, Araujo AM, Pinto J, Jeronimo C, Henrique R, Bastos ML, et al. Discrimination between the human prostate normal and cancer cell exometabolome by GC-MS. Sci Rep. 2018;8:5539.

    PubMed  PubMed Central  Google Scholar 

  23. Lima AR, Araujo AM, Pinto J, Jeronimo C, Henrique R, Bastos ML, et al. GC-MS-based endometabolome analysis differentiates prostate cancer from normal prostate cells. Metabolites. 2018;8.

  24. Wu H, Liu T, Ma C, Xue R, Deng C, Zeng H, et al. GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization. Anal Bioanal Chem. 2011;401:635–46.

    PubMed  CAS  Google Scholar 

  25. Ibanez C, Simo C, Palazoglu M, Cifuentes A. GC-MS based metabolomics of colon cancer cells using different extraction solvents. Anal Chim Acta. 2017;986:48–56.

    PubMed  CAS  Google Scholar 

  26. Chen Y, Zhang J, Guo L, Liu L, Wen J, Xu L, et al. A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis. Oncotarget. 2016;7:87496–510.

    PubMed  PubMed Central  Google Scholar 

  27. Hu JD, Tang HQ, Zhang Q, Fan J, Hong J, Gu JZ, et al. Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS. World J Gastroenterol. 2011;17:727–34.

    PubMed  PubMed Central  CAS  Google Scholar 

  28. Jiang G, Shen X, Kang H, Li K, Zheng J, Yu Y. Serum metabolite profiling of cutaneous T-cell lymphoma based on a multiplatform approach. J Chromatogr B Analyt Technol Biomed Life Sci. 2018;1077–1078:71–6.

    PubMed  Google Scholar 

  29. Zhou J, Yu S, Wang Y, Gu X, Wu Q, Xue Y, et al. Serum metabolite profiling of B-cell non-Hodgkin’s lymphoma using UPLC-QTOFMS and GC-TOFMS. Metabolomics. 2013;10:677–87.

    Google Scholar 

  30. Hua Q, Wang L, Liu C, Han L, Zhang Y, Liu H. Volatile metabonomic profiling in urine to detect novel biomarkers for B-cell non-Hodgkin’s lymphoma. Oncol Lett. 2018;15:7806–16.

    PubMed  PubMed Central  Google Scholar 

  31. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523–6.

    PubMed  PubMed Central  CAS  Google Scholar 

  32. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007;3:211–21.

    PubMed  PubMed Central  CAS  Google Scholar 

  33. 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:W486–W94.

    PubMed  PubMed Central  CAS  Google Scholar 

  34. Eriksson L, Byrne T, Johansson E, Trygg J, Vikström C. Multi- and megavariate data analysis basic principles and applications. 3rd ed. Umeå: Umetrics Academy; 2013.

    Google Scholar 

  35. Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, et al. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol BioSyst. 2015;11:13–9.

    PubMed  CAS  Google Scholar 

  36. Moritz T, Johansson A. Plant metabolomics. In: Griffiths W, editor. Metabolomics, metabonomics and metabolite profiling. Cambridge: Royal Society of Chemistry; 2007. p. 259.

    Google Scholar 

  37. Jousse C, Pujos-Guillot E. Exploring metabolome with GC/MS. In: Rolin D, editor. Metabolomics coming of age with its technological diversity. 1st ed. London: Elsevier Science; 2012. p. 316.

    Google Scholar 

  38. Lamichhane S, Sen P, Dickens A, Hyötyläinen T, Orešič M. An overview of metabolomics data analysis: current tools and future perspectives. In: Jaumot J, Bedia C, Tauler R, editors. Data analysis for omic sciences: methods and applications. 1st ed. Amsterdam: Elsevier Science; 2018. p. 392.

    Google Scholar 

  39. Michael AF, Drummond KN, Doeden D, Anderson JA, Good RA. Tryptophan metabolism in man. J Clin Invest. 1964;43:1730–46.

    PubMed  PubMed Central  CAS  Google Scholar 

  40. 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 B Biomed Appl. 1990;526:367–74.

    CAS  Google Scholar 

  41. Young SN, Gauthier S. Effect of tryptophan administration on tryptophan, 5-hydroxyindoleacetic acid and indoleacetic acid in human lumbar and cisternal cerebrospinal fluid. J Neurol Neurosurg Psychiatry. 1981;44:323–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  42. Brown DG, Rao S, Weir TL, O'Malia J, Bazan M, Brown RJ, et al. Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool. Cancer Metab. 2016;4:11.

    PubMed  PubMed Central  Google Scholar 

  43. Goedert JJ, Sampson JN, Moore SC, Xiao Q, Xiong X, Hayes RB, et al. Fecal metabolomics: assay performance and association with colorectal cancer. Carcinogenesis. 2014;35:2089–96.

    PubMed  PubMed Central  CAS  Google Scholar 

  44. Ninomiya S, Tsurumi H, Hara T, Hoshi M, Goto N, Kanemura N, et al. Tryptophan catabolism is associated with clinical outcome of patients with malignant lymphoma. Blood. 2010;116:4146.

    Google Scholar 

  45. Nakamura N, Hara T, Shimizu M, Mabuchi R, Nagano J, Ohno T, et al. Effects of indoleamine 2,3-dioxygenase inhibitor in non-Hodgkin lymphoma model mice. Int J Hematol. 2015;102:327–34.

    PubMed  CAS  Google Scholar 

  46. Litwack G. Chapter 8 - glycolysis and gluconeogenesis. In: Litwack G, editor. Human biochemistry. Boston: Academic Press; 2018. p. 183–98.

    Google Scholar 

  47. Bahado-Singh RO, Akolekar R, Mandal R, Dong E, Xia J, Kruger M, et al. Metabolomics and first-trimester prediction of early-onset preeclampsia. J Matern Fetal Neonatal Med. 2012;25:1840–7.

    PubMed  CAS  Google Scholar 

  48. Scholl-Burgi S, Haberlandt E, Gotwald T, Albrecht U, Baumgartner Sigl S, Rauchenzauner M, et al. Stroke-like episodes in propionic acidemia caused by central focal metabolic decompensation. Neuropediatrics. 2009;40:76–81.

    PubMed  CAS  Google Scholar 

  49. D’Aniello A, Vetere A, Fisher GH, Cusano G, Chavez M, Petrucelli L. Presence of d-alanine in proteins of normal and Alzheimer human brain. Brain Res. 1992;592:44–8.

    PubMed  Google Scholar 

  50. Fukushima T, Santa T, Homma H, Nagatomo R, Imai K. Determination of D-amino acids in serum from patients with renal dysfunction. Biol Pharm Bull. 1995;18:1130–2.

    PubMed  CAS  Google Scholar 

  51. Braverman ER, Pfeiffer CC, Blum K, Smayda R. The healing nutrients within: facts, findings, and new research on amino acids. 3rd ed. Laguna Beach: Basic Health Publications; 2003.

    Google Scholar 

  52. Chen KY, Liu X, Bu P, Lin CS, Rakhilin N, Locasale JW, et al. A metabolic signature of colon cancer initiating cells. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:4759–62.

    PubMed Central  Google Scholar 

  53. Strassman M, Ceci LN. Enzymatic formation of alpha-isopropylmalic acid, an intermediate in leucine biosynthesis. J Biol Chem. 1963;238:2445–52.

    PubMed  CAS  Google Scholar 

  54. Ananieva EA, Wilkinson AC. Branched-chain amino acid metabolism in cancer. Curr Opin Clin Nutr Metab Care. 2018;21:64–70.

    PubMed  CAS  Google Scholar 

  55. de Koning TJ, Snell K, Duran M, Berger R, Poll-The BT, Surtees R. L-serine in disease and development. Biochem J. 2003;371:653–61.

    PubMed  PubMed Central  Google Scholar 

  56. Hausen A, Wachter H. Pteridines in the assessment of neoplasia. J Clin Chem Clin Biochem. 1982;20:593–602.

    PubMed  CAS  Google Scholar 

  57. Rokos H, Rokos K, Frisius H, Kirstaedter HJ. Altered urinary excretion of pteridines in neoplastic disease. Determination of biopterin, neopterin, xanthopterin, and pterin. Clin Chim Acta. 1980;105:275–86.

    PubMed  CAS  Google Scholar 

  58. Dalla Pozza E, Dando I, Pacchiana R, Liboi E, Scupoli MT, Donadelli M, et al. Regulation of succinate dehydrogenase and role of succinate in cancer. Semin Cell Dev Biol. 2020;98:4–14.

    PubMed  CAS  Google Scholar 

  59. Zhao T, Mu X, You Q. Succinate: an initiator in tumorigenesis and progression. Oncotarget. 2017;8:53819–28.

    PubMed  PubMed Central  Google Scholar 

  60. Battelli MG, Polito L, Bortolotti M, Bolognesi A. Xanthine oxidoreductase in cancer: more than a differentiation marker. Cancer Med. 2016;5:546–57.

    PubMed  CAS  Google Scholar 

  61. Battelli MG, Bortolotti M, Polito L, Bolognesi A. Metabolic syndrome and cancer risk: the role of xanthine oxidoreductase. Redox Biol. 2019;21:101070.

    PubMed  CAS  Google Scholar 

  62. Xu H, Li C, Mozziconacci O, Zhu R, Xu Y, Tang Y, et al. Xanthine oxidase-mediated oxidative stress promotes cancer cell-specific apoptosis. Free Radic Biol Med. 2019;139:70–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  63. Yang HJ, Bogomolnaya L, McClelland M, Andrews-Polymenis H. De novo pyrimidine synthesis is necessary for intestinal colonization of Salmonella Typhimurium in chicks. PLoS One. 2017;12:e0183751.

    PubMed  PubMed Central  Google Scholar 

  64. Lee B, Singh RH, Rhead WJ, Sniderman King L, Smith W, Summar ML. Considerations in the difficult-to-manage urea cycle disorder patient. Crit Care Clin. 2005;21:S19–25.

    PubMed  Google Scholar 

  65. Summar ML, Barr F, Dawling S, Smith W, Lee B, Singh RH, et al. Unmasked adult-onset urea cycle disorders in the critical care setting. Crit Care Clin. 2005;21:S1–8.

    PubMed  Google Scholar 

  66. Laconi E, Denda A, Rao PM, Rajalakshmi S, Pani P, Sarma DS. Studies on liver tumor promotion in the rat by orotic acid: dose and minimum exposure time required for dietary orotic acid to promote hepatocarcinogenesis. Carcinogenesis. 1993;14:1771–5.

    PubMed  CAS  Google Scholar 

  67. Ohshima M, Sugahara K, Kasahara K, Katakura A. Metabolomic analysis of the saliva of Japanese patients with oral squamous cell carcinoma. Oncol Rep. 2017;37:2727–34.

    PubMed  CAS  Google Scholar 

  68. Nicolaides C, Fountzilas G, Zoumbos N, Skarlos D, Kosmidis P, Pectasides D, et al. Diffuse large cell lymphomas: identification of prognostic factors and validation of the international non-Hodgkin’s lymphoma prognostic index. A Hellenic Cooperative Oncology Group Study. Oncology. 1998;55:405–15.

    PubMed  CAS  Google Scholar 

  69. Pellock SJ, Redinbo MR. Glucuronides in the gut: sugar-driven symbioses between microbe and host. J Biol Chem. 2017;292:8569–76.

    PubMed  PubMed Central  CAS  Google Scholar 

  70. Afratis N, Gialeli C, Nikitovic D, Tsegenidis T, Karousou E, Theocharis AD, et al. Glycosaminoglycans: key players in cancer cell biology and treatment. FEBS J. 2012;279:1177–97.

    PubMed  CAS  Google Scholar 

  71. Bezabeh T, Ijare OB, Albiin N, Arnelo U, Lindberg B, Smith IC. Detection and quantification of D-glucuronic acid in human bile using 1H NMR spectroscopy: relevance to the diagnosis of pancreatic cancer. MAGMA. 2009;22:267–75.

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

Gustavo Henrique Bueno Duarte would like to thank National Council for Scientific and Technological Development (Project 870359/1997-5) for the scholarship, the São Paulo Research Foundation (Process 2016/07014-5) for the research financial support, and Agilent Technologies for technical support. Hans R. Zamora Obando would like to thank the University of Costa Rica for the scholarship (OAICE-159-2019).

Funding

This study was funded by the National Council for Scientific and Technological Development (grant number 870359/1997-5) and the São Paulo Research Foundation (grant number 2016/07014-5).

Author information

Authors and Affiliations

  1. ThoMSon Mass Spectrometry Laboratory, University of Campinas–UNICAMP, Campinas, SP, 13083-970, Brazil

    Gustavo Henrique Bueno Duarte

  2. Laboratory of Analysis of Biomolecules Tiselius, University of Campinas–UNICAMP, Campinas, SP, 13083-970, Brazil

    Gustavo Henrique Bueno Duarte, Hans R. Zamora-Obando, Alan Gonçalves Amaral, Alessandra de Sousa Mesquita & Ana Valéria Colnaghi Simionato

  3. Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista, SP, 12916-900, Brazil

    Anna Maria Alves de Piloto Fernandes, Alex Aparecido Rosini Silva & Andreia M. Porcari

  4. Department of Hematology, AC Camargo Cancer Center, São Paulo, SP, 01525-001, Brazil

    Jayr Schmidt-Filho

  5. Department of Medical Oncology, AC Camargo Cancer Center, São Paulo, SP, 01525-001, Brazil

    Vladmir C. Cordeiro de Lima

  6. Department of Pathology, AC Camargo Cancer Center, São Paulo, SP, 01509-010, Brazil

    Felipe D’Almeida Costa & Victor Piana Andrade

  7. MackMass Laboratory for Mass Spectrometry school of engineering - PPGEMN, Mackenzie Presbyterian University, São Paulo, SP, 01302-907, Brazil

    Marcos Nogueira Eberlin

  8. National Institute of Science and Technology in Bioanalytics (INCTBio), Campinas, SP, 13083-970, Brazil

    Ana Valéria Colnaghi Simionato

Authors
  1. Gustavo Henrique Bueno Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anna Maria Alves de Piloto Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex Aparecido Rosini Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hans R. Zamora-Obando
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alan Gonçalves Amaral
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alessandra de Sousa Mesquita
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jayr Schmidt-Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vladmir C. Cordeiro de Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Felipe D’Almeida Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Victor Piana Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Andreia M. Porcari
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Marcos Nogueira Eberlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ana Valéria Colnaghi Simionato
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Gustavo Henrique Bueno Duarte, Ana Valéria Colnaghi Simionato, Marcos Nogueira Eberlin, Jayr Schmidt-Filho, Vladmir C. Cordeiro de Lima, Felipe D’Almeida Costa, Victor Piana Andrade; methodology: Gustavo Henrique Bueno Duarte, Ana Valéria Colnaghi Simionato; formal analysis and investigation: Gustavo Henrique Bueno Duarte; writing—original draft preparation: Gustavo Henrique Bueno Duarte; writing—review and editing: Gustavo Henrique Bueno Duarte, Anna Maria Alves de Piloto Fernandes, Álex Aparecido Rosini Silva, Hans Rolando Zamora Obando, Alan Gonçalves Amaral, Alessandra de Sousa Mesquita, Jayr Schmidt-Filho, Vladmir C. Cordeiro de Lima, Felipe D’Almeida Costa, Victor Piana Andrade, Andréia de Melo Porcari, Marcos Nogueira Eberlin, Ana Valéria Colnaghi Simionato; funding acquisition: Ana Valéria Colnaghi Simionato; resources: Ana Valéria Colnaghi Simionato; supervision: Ana Valéria Colnaghi Simionato, Marcos Nogueira Eberlin.

Corresponding author

Correspondence to Ana Valéria Colnaghi Simionato.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Research Ethics Committee of the Medical Sciences College at University of Campinas and AC Camargo Cancer Center CAEE 35481614.7.0000.5404), with the 1964 Helsinki declaration, and its later amendments or comparable ethical standards.

Consent to participate

Informed consent to participate was obtained from all individual participants included in the study.

Consent for publication

Informed consent for publication was obtained from all individual participants included in the study.

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(PDF 1.11 mb)

ESM 2

(CSV 2090 kb)

ESM 3

(CSV 2458 kb)

ESM 4

(CSV 26 kb)

ESM 5

(CSV 14 kb)

ESM 6

(MED2 445 bytes)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bueno Duarte, G.H., de Piloto Fernandes, A.M.A., Silva, A.A.R. et al. Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin’s lymphoma urinary metabolite markers. Anal Bioanal Chem 412, 7469–7480 (2020). https://doi.org/10.1007/s00216-020-02881-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-020-02881-5

Keywords

Search

Navigation