Diagnóstico precoz de cáncer de pulmón en muestras de suero previas a su diagnóstico clínicoUtilidad de la metabolómica

  1. Antonio Pereira-Vega 1
  2. I. M. Díaz Olivares 2
  3. L. Padrón Fraysse 1
  4. G. Peces-Barba Romero 3
  5. L. Seijo Maceiras 4
  6. Carolina Gotera 3
  7. J.L. López-Campos Bodineau 5
  8. J.L. Gómez-Ariza 6
  9. Belén Callejón Leblic 6
  10. Tamara García Barrera 6
  1. 1 Servicio de Neumología del Hospital Juan Ramón Jiménerz (Huelva)
  2. 2 Fundación Andaluza Beturia de la Investigación en la Salud (FABIS)
  3. 3 Fundación Jiménez Díaz (Madrid)
  4. 4 Clínica Universidad de Navarra (Madrid)
  5. 5 Hospital Virgen del Rocío (Sevilla)
  6. 6 Departamento de Química Analítica de la Universidad de Huelva (UHU)
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Journal:
Revista española de patología torácica

ISSN: 1889-7347

Year of publication: 2024

Volume: 36

Issue: 2

Pages: 127-140

Type: Article

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More publications in: Revista española de patología torácica

Abstract

Introduction: New biomarkers (BM) based on omics techniques can help in the early diagnosis of lung cancer (LC). Our group has proposed 11 metabolites as possible BMs of CP. To validate these BMs, longitudinal studies are fundamental and rare. Objective: To analyze, in two retrospective longitudinal studies carried out in different cohorts, how the global metabolomic profile (GMP) varies in the years prior to the clinical diagnosis of PC. These cohorts follow up the included subjects for at least 5 years, with annual clinical visits and blood collection. Method and material: 72 samples have been selected from 38 subjects who have developed CP during follow-up. Baseline samples (time of diagnosis, n = 10) are compared with samples from 1 - 2 years (n = 25); 1 - 3 years (n = 38); and 4 - 7 years prior to the diagnosis of CP (n = 7). Advanced analytical chemistry techniques were used: mass spectrometry and liquid chromatography. For comparison between groups, Partial Least Squares Discriminant Analysis (PLS-DA) was used. Results: Significant differences were found in the comparisons of the PMG between the different groups. The causal metabolites of these differences and the metabolic pathways involved were determined, highlighting the metabolism of glycine, serine and threonine. They are compared with our 11 possible BMs. Conclusions: In the years prior to the clinical diagnosis of CP, metabolomic changes occur. There are metabolites that can be detected in the years prior to clinical diagnosis and that will provide very relevant data in the early diagnosis of PC.

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Funding information

Artículo financiado con la Beca Neumosur nº 9/2015

Bibliographic References

  • Y. Chen, Z. Ma, A. Li, H. Li, B. Wang, J. Zhong et al. Metabolic profiling of human serum in lung cancer patients using liquid chromatography/hybrid quedrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry. J Cancer Res Clin Oncol, 2015, 141 (4): 705-718.
  • Callejon-Leblic B, García-Barrera T, Grávalos-Guzmán J, Pereira-Vega A, Gómez-Ariza JL. Metabolomic profiling of potential lung cancer biomarkers using broncho alveolar lavage fluid and the integrated direct infusión/gas chromatografy mass spectrometry platform. Journal of Proteomics, 2016, 145:197-206.
  • Callejon-Leblic B, Garcia-Barrera T, Pereira-Vega A, Gomez-Ariza JL. Metabolomic study of serum, urine and broncho alveolar lavage fluid based on gas chromatography mass spectrometry to delve into the pathology of lung cancer. Journal of Pharmaceutical and Biomedical Analysis, 2019, 163: 122-129.
  • A. Pereira Vega, B. Callejón Leblic, S. García Garrido, L. Padrón Fraysse, J.L. Gómez Ariza, T. García Barrera. “ Search for Metabolic Biomarkers for the Early Detection of Lung Cancer in Population at Risk”. Rev. Esp. Patol. Torac., 2021,33 (1) 35-45.
  • Callejón-Leblic B, Pereira-Vega A, Vázquez-Gandullo1, E. Sánchez-Ramos JL, Gómez-Ariza JL, García-Barrera T. Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry. Biochimie, 2019, 157: 111-122.
  • Deja S, Porebska I, Kowal A, Zabek A, Barg W, Pawelczyk K et al. Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. Journal of Pharmaceutical and Biomedical Analysis, 2014, 100: 369-380.
  • Callejón-Leblic B, Arias-Borrego A, Pereira-Vega A, Gómez-Ariza JL, García-Barrera T. The metallome of lung cancer and its potential use as biomarker. Int. J. Mol. Sci., 2019; 20, 778-794.
  • Parker, MS. Lung Cancer Epidemiology. Lung Cancer Screening. 2018; 1-5. ISBN 978-1-62623-513-7.
  • T. Wen, L. Gao, Z. Wen, C. Wu, C. Sen Tan, W. Zhong Toh et al. Exploratory investigation of plasma metabolomics in human lung adenocarcinoma. Molecular BioSystems, 2013. 9: 2.370-2.378.
  • D. Xiang, B. Zhang, D. Doll, K. Shen, G. Kloecker, C. Freter. «Lung cancer screening: from imaging to biomarker» Biomarker Research, 2013, 1: 4 (1-9).
  • F.M. Sullivan, F. S. Mair, W. Anderson, P. Armory, A. Briggs, C. Chew et al. «Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging». European Respiratory Journal, 2021; 57 (1): 2000670.doi: 10.1183/13993003.00670-2020.
  • T. Schult, M. Lauer, Y. Berker, M. Cardoso, L. Vandergrift, P. Habbel et al. "Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics" Proceedings of the National Academy of Sciences, 2021: 118 (51).
  • P. Yin, R. Lehmann, G. Xu. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem, 2015, 407: 4.879-4.892.
  • M. Yu, R. Sun, Y. Zhao, F. Shao, W. Zhu and J. Aa. Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer. Future Oncology, 2021, 17, 32, 4.355-4.369.
  • P. Moreno Casado, M.A. Calzado Canales, A. Álvarez Kindelán. Prognosis markers in lung carcinogénesis SIAH2-DyRK2 pathway. Rev. Esp. Patol. Torac, 2018, 30, 2, 144-152.
  • N. Ahmed, B. Kidane, L. Wang, Z. Nugent, N. Moldovan, A. McElrea et al.Metabolic Changes in Early-Stage Non–Small Cell Lung Cancer Patients after Surgical Resection. Cancers, 2021, 13, 3012.
  • A. Botticelli, P. Vernocchi, F. Marini, A. Quagliariello, B. Cerbelli, S. Reddel et al. Gut metabolomics profiling of non small cell lung cancer (NSCLC) patients under immunotherapy treatment. Journal of Translational Medicine, 2020, 18, 49.
  • Las cifras de cáncer en España en 2020. Sociedad Española de Oncología Médica (SEOM). Depósito legal: M-3266-2020.
  • M.D. Stephen A.Feig. Estimation of Currently Attainable Benefit from Mammographic Screening of Women Aged 40-49 Years. New Swedish Screening Results, 1995. 2.412-2.419.
  • D. Yang, X Yang, Y Li, P Zhao, R Fu, T Ren et al. Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal. Jounal of Translational Medicine, 2020. 18: 243.
  • The National Lung Screening Trial Research Team (NLST). Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial. Journal of Thoracic Oncology. 2019; 14(10):1.732-1.742.
  • S. Martin Bote, M. Arenas de Larriva, M. Entrenas Castillo, N. Feu Collado, N. Pascual Martínez, R. Lama Martinez et al. Differences in Sweat Metabolites According to Diagnostic Status in Squamous Lung Cancer. Rev. Esp. Patol. Torac, 2019, 31, 4, 218-223.
  • Y. Adir, S. Tirman, S. Abramovitch, C. Botbol, A. Lutaty, T. Scheinmann et al. Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profiles. Cancers Immunology, Immunotherapy, 2018. https://doi.org/10.1007/s00262-018-2173-5.
  • Belen Callejon-Leblic, J.L. Gomez-Ariza, A. Pereira-Vega and T. Garcia-Barrera, Metal dyshomeostasis based biomarkers of lungcancer using human biofluids. Metalomics, 2018, 10 (10), 1.444-1.451.
  • A. Klupczynska, S. Plewa, M. Kasprzyk, W. Dyszkiewicz, Z. J. Kokot, J. Matysiak. Serum lipidome screening in patients with stage I non-small cell lung cancer. Clinical and Experimental Medicine, 2019, 19, 505-513.
  • Wen CP, Zhang F, Liang D, Wen C, Gu J, Skinner H et al. The ability of bilirubin in identifying smokers with higher risk of lung cancer: a large cohort study in conjunction with global metabolomic profiling. Clinical Cancer Research, 2014; 21(1): 193-200.
  • Chen X, Gole J, Gore A, He Q, Lu M, Min J et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nature communications, 2020; 11(1): 1-10.
  • Edelsberg J, Weycker D, Atwood M, Hamilton-Fairley G, Jett JR. Cost-effectiveness of an autoantibody test (Early CDT-Lung) as an aid to early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules. PloS one, 2018; 13(5): e0197826. DOI: https://doi.org/10.1371/journal.pone.0197826 21.
  • J. F. Haince, P. Joubert, H. Bach, R. A. Bux, P. S. Tappia, and B. Ramjiawan. Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome. Int. J. of Mol. Sci, 2022. 23, 1215.
  • J Zheng, Y Zheng, W Li, J. Zhi, X Huang, W Zhu et al. Combined metabolomics with transcriptomics reveals potential plasma biomarkers correlated with non-small-cell lung cancer proliferation through the Akt pathway. Clinica Chemica Acta 530, 2022. 66-73.
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