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Metabolomics

, 14:109 | Cite as

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

  • Nguyen Phuoc Long
  • Sang Jun Yoon
  • Nguyen Hoang Anh
  • Tran Diem Nghi
  • Dong Kyu Lim
  • Yu Jin Hong
  • Soon-Sun Hong
  • Sung Won Kwon
Review Article

Abstract

Introduction

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

Objectives

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Methods

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.

Results and Conclusion

The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Keywords

Pancreatic cancer Metabolomics Diagnostic biomarkers Systematic review 

Abbreviations

AUC

Area under the curve

BCAAs

Branched-chain amino acids

CP

Chronic pancreatitis

GC–MS

Gas chromatography–mass spectrometry

LC–MS

Liquid chromatography–mass spectrometry

MRI

Magnetic resonance imaging

MS/MS

Tandem mass spectrometry

NMR

Nuclear magnetic resonance

OPLS-DA

Orthogonal projections to latent structures discriminant analysis

PC

Pancreatic cancer

PCA

Principal component analysis

PDAC

Pancreatic ductal adenocarcinoma

PLS-DA

Partial least squares discriminant analysis

PLS-DF

Partial least squares discriminant function

Q2

Cross-validated coefficient of determination

R2

Coefficient of determination

ROC

Receiver operating characteristic curve

SFE-SFC/MS/MS

Supercritical fluid extraction-supercritical fluid chromatography coupled with tandem mass spectrometry

Notes

Author contributions

SWK and SSH supervised the project. SWK, SSH, and NPL contributed to the study design. NPL, SJY, NHA, TDN, DKL, and YJH searched and collected the data. NPL, SJY, NHA, TDN, DKL, and YJH performed data processing and interpretation. NPL, SJY, NHA, and TDN prepared the first draft of the manuscript. All authors have read, revised critically, and approved the final manuscript.

Funding

The Bio-Synergy Research Project of the Ministry of Science, ICT and Future Planning through the National Research Foundation of Korea (NRF-2012M3A9C4048796), the National Research Foundation of Korea (NRF-2017R1E1A2A02022658, NRF-2018R1A5A2024425), and the BK21 Plus Program in 2017.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Supplementary material

11306_2018_1404_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (PDF 1275 KB)

References

  1. Abbassi-Ghadi, N., Kumar, S., Huang, J., Goldin, R., Takats, Z., & Hanna, G. B. (2013). Metabolomic profiling of oesophago-gastric cancer: A systematic review. European Journal of Cancer, 49, 3625–3637.PubMedCrossRefGoogle Scholar
  2. Akita, H., Ritchie, S. A., Takemasa, I., Eguchi, H., Pastural, E., Jin, W., et al. (2016). Serum metabolite profiling for the detection of pancreatic cancer: Results of a large independent validation study. Pancreas, 45, 1418–1423.PubMedCrossRefGoogle Scholar
  3. Aksenov, A. A., da Silva, R., Knight, R., Lopes, N. P., & Dorrestein, P. C. (2017). Global chemical analysis of biology by mass spectrometry. Nature Reviews Chemistry, 1, 0054.CrossRefGoogle Scholar
  4. Amelio, I., Cutruzzola, F., Antonov, A., Agostini, M., & Melino, G. (2014). Serine and glycine metabolism in cancer. Trends in Biochemical Sciences, 39, 191–198.PubMedPubMedCentralCrossRefGoogle Scholar
  5. Ananieva, E. A., & Wilkinson, A. C. (2018). Branched-chain amino acid metabolism in cancer. Current Opinion in Clinical Nutrition and Metabolic Care, 21, 64–70.PubMedCrossRefGoogle Scholar
  6. Aromataris, E., & Riitano, D. (2014). Constructing a search strategy and searching for evidence. A guide to the literature search for a systematic review. AJN The American Journal of Nursing, 114, 49–56.CrossRefPubMedGoogle Scholar
  7. Azuaje, F., Devaux, Y., & Wagner, D. (2009). Challenges and standards in reporting diagnostic and prognostic biomarker studies. Clinical and Translational Science, 2, 156–161.PubMedPubMedCentralCrossRefGoogle Scholar
  8. Ballehaninna, U. K., & Chamberlain, R. S. (2012). The clinical utility of serum CA 19–9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal. Journal of Gastrointestinal Oncology, 3, 105–119.PubMedPubMedCentralGoogle Scholar
  9. Bao, Y., Giovannucci, E. L., Kraft, P., Stampfer, M. J., Ogino, S., Ma, J., et al. (2013). A prospective study of plasma adiponectin and pancreatic cancer risk in five US cohorts. Journal of the National Cancer Institute, 105, 95–103.PubMedCrossRefGoogle Scholar
  10. Baran, R. (2017). Untargeted metabolomics suffers from incomplete raw data processing. Metabolomics, 13(9), 107CrossRefGoogle Scholar
  11. Bathe, O. F., Shaykhutdinov, R., Kopciuk, K., Weljie, A. M., McKay, A., Sutherland, F. R., et al. (2011). Feasibility of identifying pancreatic cancer based on serum metabolomics. Cancer Epidemiology and Prevention Biomarkers, 20, 140–147.CrossRefGoogle Scholar
  12. Beger, R. D., Schnackenberg, L. K., Holland, R. D., Li, D., & Dragan, Y. (2006). Metabonomic models of human pancreatic cancer using 1D proton NMR spectra of lipids in plasma. Metabolomics, 2, 125–134.CrossRefGoogle Scholar
  13. Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., et al. (2015). STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. Radiology, 277, 826–832.PubMedCrossRefGoogle Scholar
  14. Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., et al. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. BMJ, 326, 41.PubMedPubMedCentralCrossRefGoogle Scholar
  15. Bowden, J. A., Heckert, A., Ulmer, C. Z., Jones, C. M., Koelmel, J. P., Abdullah, L., et al. (2017). Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-metabolites in frozen human plasma. Journal of Lipid Research, 58, 2275–2288.PubMedCrossRefGoogle Scholar
  16. Cajka, T., Smilowitz, J. T., & Fiehn, O. (2017). Validating quantitative untargeted lipidomics across nine liquid chromatography–high-resolution mass spectrometry platforms. Analytical Chemistry, 89, 12360–12368.PubMedCrossRefGoogle Scholar
  17. Cancer Genome Atlas Research Network. (2017). Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell, 32, 185–203 e13.CrossRefGoogle Scholar
  18. Canto, M. I., Harinck, F., Hruban, R. H., Offerhaus, G. J., Poley, J. W., Kamel, I., et al. (2013). International Cancer of the Pancreas Screening (CAPS) Consortium summit on the management of patients with increased risk for familial pancreatic cancer. Gut, 62, 339–347.PubMedCrossRefGoogle Scholar
  19. Cappelletti, V., Appierto, V., Tiberio, P., Fina, E., Callari, M., & Daidone, M. G. (2015). Circulating biomarkers for prediction of treatment response. Journal of the National Cancer Institute Monographs, 2015, 60–63.PubMedCrossRefGoogle Scholar
  20. Chan, A., Diamandis, E. P., & Blasutig, I. M. (2013). Strategies for discovering novel pancreatic cancer biomarkers. Journal of Proteomics, 81, 126–134.PubMedCrossRefGoogle Scholar
  21. Chen, J. J., Lu, T.-P., Chen, D.-T., & Wang, S.-J. (2014). Biomarker adaptive designs in clinical trials. Translational Cancer Research, 3(3), 279–292Google Scholar
  22. Crews, B., Wikoff, W. R., Patti, G. J., Woo, H. K., Kalisiak, E., Heideker, J., et al. (2009). Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data. Analytical Chemistry, 81, 8538–8544.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Davis, V. W., Schiller, D. E., Eurich, D., Bathe, O. F., & Sawyer, M. B. (2013). Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature. Annals of Surgical Oncology, 20(Suppl 3), S415–S423.PubMedCrossRefGoogle Scholar
  24. Di Gangi, I. M., Mazza, T., Fontana, A., Copetti, M., Fusilli, C., Ippolito, A., et al. (2016). Metabolomic profile in pancreatic cancer patients: A consensus-based approach to identify highly discriminating metabolites. Oncotarget, 7, 5815–5829.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Diamandis, E. P. (2010). Cancer biomarkers: Can we turn recent failures into success? Journal of the National Cancer Institute, 102, 1462–1467.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Domingo-Almenara, X., Montenegro-Burke, J. R., Benton, H. P., & Siuzdak, G. (2017). Annotation: A computational solution for streamlining metabolomics analysis. Analytical Chemistry, 90(1), 480–489.PubMedPubMedCentralCrossRefGoogle Scholar
  27. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083.PubMedCrossRefGoogle Scholar
  28. Dunn, W. B., Broadhurst, D. I., Edison, A., Guillou, C., Viant, M. R., Bearden, D. W., et al. (2017). Quality assurance and quality control processes: Summary of a metabolomics community questionnaire. Metabolomics, 13, 50.CrossRefGoogle Scholar
  29. Fan, L., Zhang, W., Yin, M., Zhang, T., Wu, X., Zhang, H., et al. (2012). Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform. Acta Oncology, 51, 473–479.CrossRefGoogle Scholar
  30. Findeisen, P., & Neumaier, M. (2009). Mass spectrometry based proteomics profiling as diagnostic tool in oncology: Current status and future perspective. Clinical Chemistry and Laboratory Medicine, 47, 666–684.PubMedCrossRefGoogle Scholar
  31. Foster, K. R., Koprowski, R., & Skufca, J. D. (2014). Machine learning, medical diagnosis, and biomedical engineering research—Commentary. Biomedical Engineering Online, 13, 94.PubMedPubMedCentralCrossRefGoogle Scholar
  32. Fukutake, N., Ueno, M., Hiraoka, N., Shimada, K., Shiraishi, K., Saruki, N., et al. (2015). A novel multivariate index for pancreatic cancer detection based on the plasma free amino acid profile. PLoS ONE 10, e0132223.PubMedPubMedCentralCrossRefGoogle Scholar
  33. Gangi, I. M. D., Vrhovsek, U., Pazienza, V., & Mattivi, F. (2014). Analytical metabolomics-based approaches to pancreatic cancer. TrAC Trends in Analytical Chemistry, 55, 94–116.CrossRefGoogle Scholar
  34. Grissa, D., Petera, M., Brandolini, M., Napoli, A., Comte, B., & Pujos-Guillot, E. (2016). Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data. Frontiers in Molecular Biosciences, 3, 30.PubMedPubMedCentralCrossRefGoogle Scholar
  35. Halbrook, C. J., & Lyssiotis, C. A. (2017). Employing metabolism to improve the diagnosis and treatment of pancreatic cancer. Cancer Cell, 31, 5–19.PubMedCrossRefGoogle Scholar
  36. Harsha, H. C., Kandasamy, K., Ranganathan, P., Rani, S., Ramabadran, S., Gollapudi, S., et al. (2009). A compendium of potential biomarkers of pancreatic cancer. PLoS Med 6, e1000046.PubMedPubMedCentralCrossRefGoogle Scholar
  37. He, X., Zhong, J., Wang, S., Zhou, Y., Wang, L., Zhang, Y., et al. (2017). Serum metabolomics differentiating pancreatic cancer from new-onset diabetes. Oncotarget, 8, 29116–29124.PubMedPubMedCentralGoogle Scholar
  38. Hezel, A. F., Kimmelman, A. C., Stanger, B. Z., Bardeesy, N., & Depinho, R. A. (2006). Genetics and biology of pancreatic ductal adenocarcinoma. Genes and Development, 20, 1218–1249.PubMedCrossRefGoogle Scholar
  39. Hirata, Y., Kobayashi, T., Nishiumi, S., Yamanaka, K., Nakagawa, T., Fujigaki, S., et al. (2017). Identification of highly sensitive biomarkers that can aid the early detection of pancreatic cancer using GC/MS/MS-based targeted metabolomics. Clinica Chimica Acta, 468, 98–104.CrossRefGoogle Scholar
  40. Irwig, L., Bossuyt, P., Glasziou, P., Gatsonis, C., & Lijmer, J. (2002). Designing studies to ensure that estimates of test accuracy are transferable. BMJ, 324, 669–671.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Itoi, T., Sugimoto, M., Umeda, J., Sofuni, A., Tsuchiya, T., Tsuji, S., et al. (2017). Serum Metabolomic profiles for human pancreatic cancer discrimination. International Journal of Molecular Sciences, 18(4), 767.PubMedCentralCrossRefGoogle Scholar
  42. Kamisawa, T., Wood, L. D., Itoi, T., & Takaori, K. (2016). Pancreatic cancer. Lancet, 388, 73–85.PubMedCrossRefGoogle Scholar
  43. Kind, T., Tsugawa, H., Cajka, T., Ma, Y., Lai, Z., Mehta, S. S., et al. (2017). Identification of small molecules using accurate mass MS/MS search. Mass Spectrometry Reviews, 9999, 1–20.Google Scholar
  44. Kobayashi, T., Nishiumi, S., Ikeda, A., Yoshie, T., Sakai, A., Matsubara, A., et al. (2013). A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiology Prevention Biomarkers, 22, 571–579.CrossRefGoogle Scholar
  45. Kondo, T. (2014). Inconvenient truth: cancer biomarker development by using proteomics. Biochimica et Biophysica Acta, 1844, 861–865.PubMedCrossRefGoogle Scholar
  46. Konforte, D., & Diamandis, E. P. (2013). Is early detection of cancer with circulating biomarkers feasible? Clinical Chemistry, 59, 35–37.PubMedCrossRefGoogle Scholar
  47. Kuan, P. F. (2014). Propensity score method for partially matched omics studies. Cancer Informatics, 13, 1–10.PubMedPubMedCentralGoogle Scholar
  48. Kumar, N., Shahjaman, M., Mollah, M. N. H., Islam, S. M. S., & Hoque, M. A. (2017). Serum and plasma metabolomic biomarkers for lung cancer. Bioinformation, 13, 202–208.PubMedPubMedCentralCrossRefGoogle Scholar
  49. Leake, I. (2014). Early events in pancreatic cancer. Nature Reviews Gastroenterology & Hepatology, 11, 703.CrossRefGoogle Scholar
  50. Leichtle, A. B., Ceglarek, U., Weinert, P., Nakas, C. T., Nuoffer, J.-M., Kase, J., et al. (2013). Pancreatic carcinoma, pancreatitis, and healthy controls: Metabolite models in a three-class diagnostic dilemma. Metabolomics, 9, 677–687.PubMedCrossRefGoogle Scholar
  51. Li, D. (2012). Diabetes and pancreatic cancer. Molecular Carcinogenesis, 51, 64–74.PubMedPubMedCentralCrossRefGoogle Scholar
  52. Liesenfeld, D. B., Habermann, N., Owen, R. W., Scalbert, A., & Ulrich, C. M. (2013). Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiology and Prevention Biomarkers, 22, 2182–2201.CrossRefGoogle Scholar
  53. Lin, W. J., & Chen, J. J. (2013). Class-imbalanced classifiers for high-dimensional data. Brief Bioinformatics, 14, 13–26.PubMedCrossRefGoogle Scholar
  54. Lindahl, A., Heuchel, R., Forshed, J., Lehtio, J., Lohr, M., & Nordstrom, A. (2017). Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics. Metabolomics, 13, 61.PubMedPubMedCentralCrossRefGoogle Scholar
  55. Lu, W., Su, X., Klein, M. S., Lewis, I. A., Fiehn, O., & Rabinowitz, J. D. (2017). Metabolite measurement: Pitfalls to avoid and practices to follow. Annual Review of Biochemistry, 86, 277–304.PubMedPubMedCentralCrossRefGoogle Scholar
  56. Lumbreras, B., Parker, L. A., Porta, M., Pollan, M., Ioannidis, J. P., & Hernandez-Aguado, I. (2009). Overinterpretation of clinical applicability in molecular diagnostic research. Clinical Chemistry, 55, 786–794.PubMedCrossRefGoogle Scholar
  57. Lumbreras, B., Porta, M., Marquez, S., Pollan, M., Parker, L. A., & Hernandez-Aguado, I. (2008). 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. Clinical Biochemistry, 41, 1316–1325.PubMedCrossRefGoogle Scholar
  58. Lynch, C. J., & Adams, S. H. (2014). Branched-chain amino acids in metabolic signalling and insulin resistance. Nature Reviews Endocrinology, 10, 723.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Makohon-Moore, A., & Iacobuzio-Donahue, C. A. (2016). Pancreatic cancer biology and genetics from an evolutionary perspective. Nature Reviews Cancer, 16, 553–565.PubMedPubMedCentralCrossRefGoogle Scholar
  60. Mayerle, J., Kalthoff, H., Reszka, R., Kamlage, B., Peter, E., Schniewind, B., et al. (2018). Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis. Gut, 67, 128–137.PubMedCrossRefGoogle Scholar
  61. Mayers, J. R., Wu, C., Clish, C. B., Kraft, P., Torrence, M. E., Fiske, B. P., et al. (2014). Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nature Medicine, 20, 1193–1198.PubMedPubMedCentralCrossRefGoogle Scholar
  62. McCormick, F. C., & Lemoine, N. R. (1998). Molecular basis of pancreatic cancer: Strategies for genetic diagnosis and therapy. In: C. D. Johnson & C. W. Imrie (Eds.), Pancreatic disease: Towards the year 2000 (2nd ed.). New York: Springer.Google Scholar
  63. Moons, K. G., Altman, D. G., Reitsma, J. B., Ioannidis, J. P., Macaskill, P., Steyerberg, E. W., et al. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration. Annals of Internal Medicine, 162, W1–W73.PubMedCrossRefGoogle Scholar
  64. Napoli, C., Sperandio, N., Lawlor, R. T., Scarpa, A., Molinari, H., & Assfalg, M. (2012). Urine metabolic signature of pancreatic ductal adenocarcinoma by 1H nuclear magnetic resonance: Identification, mapping, and evolution. Journal of Proteome Research, 11, 1274–1283.PubMedCrossRefGoogle Scholar
  65. Nguyen, V., Hurton, S., Ayloo, S., & Molinari, M. (2015). Advances in pancreatic cancer: The role of metabolomics. JOP: Journal of the Pancreas, 16, 244–248.Google Scholar
  66. Nishiumi, S., Shinohara, M., Ikeda, A., Yoshie, T., Hatano, N., Kakuyama, S., et al. (2010). Serum metabolomics as a novel diagnostic approach for pancreatic cancer. Metabolomics, 6, 518–528.CrossRefGoogle Scholar
  67. Oberstein, P. E., & Olive, K. P. (2013). Pancreatic cancer: Why is it so hard to treat? Therapeutic Advances in Gastroenterology, 6, 321–337.PubMedPubMedCentralCrossRefGoogle Scholar
  68. Paglia, G., & Astarita, G. (2017). Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry. Nature Protocols, 12, 797.PubMedCrossRefGoogle Scholar
  69. Pannala, R., Basu, A., Petersen, G. M., & Chari, S. T. (2009). New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer. Lancet Oncology, 10, 88–95.PubMedCrossRefGoogle Scholar
  70. Parker, L. A., GómezSaez, N., Lumbreras, B., Porta, M., & Hernández-Aguado, I. (2010). Methodological deficits in diagnostic research using ‘-Omics’ technologies: Evaluation of the QUADOMICS tool and quality of recently published studies. PLoS ONE 5, e11419.PubMedPubMedCentralCrossRefGoogle Scholar
  71. Partyka, K., Maupin, K. A., Brand, R. E., & Haab, B. B. (2012). Diverse monoclonal antibodies against the CA 19–9 antigen show variation in binding specificity with consequences for clinical interpretation. Proteomics, 12, 2212–2220.PubMedPubMedCentralCrossRefGoogle Scholar
  72. Patti, G. J., Tautenhahn, R., & Siuzdak, G. (2012). Meta-analysis of untargeted metabolomic data: combining results from multiple profiling experiments. Nature Protocols, 7, 508–516.PubMedPubMedCentralCrossRefGoogle Scholar
  73. Pavlova, N. N., & Thompson, C. B. (2016). The emerging hallmarks of cancer metabolism. Cell Metabolism, 23, 27–47.PubMedPubMedCentralCrossRefGoogle Scholar
  74. Pepe, M. S., Feng, Z., Janes, H., Bossuyt, P. M., & Potter, J. D. (2008). Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. Journal of the National Cancer Institute, 100, 1432–1438.PubMedPubMedCentralCrossRefGoogle Scholar
  75. Perez-Rambla, C., Puchades-Carrasco, L., Garcia-Flores, M., Rubio-Briones, J., Lopez-Guerrero, J. A., & Pineda-Lucena, A. (2017). Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia. Metabolomics, 13, 52.PubMedPubMedCentralCrossRefGoogle Scholar
  76. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. arXiv:1602.04938v3.
  77. Rios Peces, S., Diaz Navarro, C., Marquez Lopez, C., Caba, O., Jimenez-Luna, C., Melguizo, C., et al. (2017). Untargeted LC-HRMS-based metabolomics for searching new biomarkers of pancreatic ductal adenocarcinoma: A pilot study. SLAS Discovery, 22, 348–359.PubMedGoogle Scholar
  78. Ritchie, S. A., Akita, H., Takemasa, I., Eguchi, H., Pastural, E., Nagano, H., et al. (2013). Metabolic system alterations in pancreatic cancer patient serum: Potential for early detection. BMC Cancer, 13, 416.PubMedPubMedCentralCrossRefGoogle Scholar
  79. Rodrigues, D., Jeronimo, C., Henrique, R., Belo, L., de Lourdes Bastos, M., de Pinho, P. G., et al. (2016). Biomarkers in bladder cancer: A metabolomic approach using in vitro and ex vivo model systems. International Journal of Cancer, 139, 256–268.PubMedCrossRefGoogle Scholar
  80. Ryan, D. P., Hong, T. S., & Bardeesy, N. (2014). Pancreatic adenocarcinoma. New England Journal of Medicine, 371, 1039–1049.PubMedCrossRefGoogle Scholar
  81. Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10, e0118432.PubMedPubMedCentralCrossRefGoogle Scholar
  82. Sakai, A., Suzuki, M., Kobayashi, T., Nishiumi, S., Yamanaka, K., Hirata, Y., et al. (2016). Pancreatic cancer screening using a multiplatform human serum metabolomics system. Biomarkers in Medicine, 10, 577–586.PubMedCrossRefGoogle Scholar
  83. Scheubert, K., Hufsky, F., Petras, D., Wang, M., Nothias, L.-F., Dührkop, K., et al. (2017). Significance estimation for large scale metabolomics annotations by spectral matching. Nature Communications, 8, 1494.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Shin, E. J., & Canto, M. I. (2012). Pancreatic cancer screening. Gastroenterology Clinics of North America, 41, 143–157.PubMedPubMedCentralCrossRefGoogle Scholar
  85. Sidaway, P. (2017). Pancreatic cancer: TCGA da ta reveal a highly heterogeneous disease. Nature Reviews Clinical Oncology, 14, 648.PubMedGoogle Scholar
  86. Singh, S., Tang, S. J., Sreenarasimhaiah, J., Lara, L. F., & Siddiqui, A. (2011). The clinical utility and limitations of serum carbohydrate antigen (CA19-9) as a diagnostic tool for pancreatic cancer and cholangiocarcinoma. Digestive Diseases and Science, 56, 2491–2496.CrossRefGoogle Scholar
  87. Smialowski, P., Frishman, D., & Kramer, S. (2010). Pitfalls of supervised feature selection. Bioinformatics, 26, 440–443.PubMedCrossRefGoogle Scholar
  88. Spicer, R. A., & Steinbeck, C. (2017). A lost opportunity for science: Journals promote data sharing in metabolomics but do not enforce it. Metabolomics, 14, 16.PubMedPubMedCentralCrossRefGoogle Scholar
  89. Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics, 6, 78–95.PubMedCrossRefGoogle Scholar
  90. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3, 211–221.PubMedPubMedCentralCrossRefGoogle Scholar
  91. Suzuki, M., Nishiumi, S., Kobayashi, T., Sakai, A., Iwata, Y., Uchikata, T., et al. (2017). Use of on-line supercritical fluid extraction-supercritical fluid chromatography/tandem mass spectrometry to analyze disease biomarkers in dried serum spots compared with serum analysis using liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 31, 886–894.PubMedCrossRefGoogle Scholar
  92. Takhar, A. S., Palaniappan, P., Dhingsa, R., & Lobo, D. N. (2004). Recent developments in diagnosis of pancreatic cancer. BMJ, 329, 668–673.PubMedPubMedCentralCrossRefGoogle Scholar
  93. Tanase, C. P., Neagu, M., Albulescu, R., Codorean, E., & Dima, S. O. (2009). Biomarkers in the diagnosis and early detection of pancreatic cancer. Expert Opinion on Medical Diagnostics, 3, 533–546.PubMedCrossRefGoogle Scholar
  94. Tumas, J., Kvederaviciute, K., Petrulionis, M., Kurlinkus, B., Rimkus, A., Sakalauskaite, G., et al. (2016). Metabolomics in pancreatic cancer biomarkers research. Medical Oncology, 33, 133.PubMedCrossRefGoogle Scholar
  95. Turakhia, M. P., & Sabatine, M. S. (2017). How we evaluate biomarker studies. JAMA Cardiology, 2, 524–524.PubMedCrossRefGoogle Scholar
  96. Urayama, S., Zou, W., Brooks, K., & Tolstikov, V. (2010). Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. Rapid Communications in Mass Spectrometry, 24, 613–620.PubMedCrossRefGoogle Scholar
  97. Usher-Smith, J. A., Sharp, S. J., & Griffin, S. J. (2016). The spectrum effect in tests for risk prediction, screening, and diagnosis. BMJ, 353, i3139.PubMedPubMedCentralCrossRefGoogle Scholar
  98. Vinaixa, M., Samino, S., Saez, I., Duran, J., Guinovart, J. J., & Yanes, O. (2012). A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites, 2, 775–795.PubMedPubMedCentralCrossRefGoogle Scholar
  99. Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., et al. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155, 529–536.PubMedCrossRefGoogle Scholar
  100. Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., et al. (2017). HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608–D617.PubMedCentralCrossRefGoogle Scholar
  101. Wolpin, B. M., Bao, Y., Qian, Z. R., Wu, C., Kraft, P., Ogino, S., et al. (2013). Hyperglycemia, insulin resistance, impaired pancreatic β-cell function, and risk of pancreatic cancer. JNCI: Journal of the National Cancer Institute, 105, 1027–1035.PubMedPubMedCentralCrossRefGoogle Scholar
  102. Xia, J., Broadhurst, D. I., Wilson, M., & Wishart, D. S. (2013). Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics, 9, 280–299.PubMedCrossRefGoogle Scholar
  103. Xia, J., & Wishart, D. S. (2016). Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis. Curr Protoc Bioinformatics, 55(1), 14–10.PubMedCrossRefGoogle Scholar
  104. Xie, G., Lu, L., Qiu, Y., Ni, Q., Zhang, W., Gao, Y. T., et al. (2015). Plasma metabolite biomarkers for the detection of pancreatic cancer. Journal of Proteome Research, 14, 1195–1202.PubMedCrossRefGoogle Scholar
  105. Yang, W., Chen, Y., Xi, C., Zhang, R., Song, Y., Zhan, Q., et al. (2013). Liquid chromatography-tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites’ stability on reliability of potential biomarkers. Analytical Chemistry, 85, 2606–2610.PubMedCrossRefGoogle Scholar
  106. Yang, W., Yoshigoe, K., Qin, X., Liu, J. S., Yang, J. Y., Niemierko, A., et al. (2014). Identification of genes and pathways involved in kidney renal clear cell carcinoma. BMC Bioinformatics, 15(Suppl 17), S2.PubMedPubMedCentralCrossRefGoogle Scholar
  107. Yin, L., Ge, Y., Xiao, K., Wang, X., & Quan, X. (2013). Feature selection for high-dimensional imbalanced data. Neurocomputing, 105, 3–11.CrossRefGoogle Scholar
  108. Zhang, H., Wang, Y., Gu, X., Zhou, J., & Yan, C. (2011). Metabolomic profiling of human plasma in pancreatic cancer using pressurized capillary electrochromatography. Electrophoresis, 32, 340–347.PubMedCrossRefGoogle Scholar
  109. Zhang, L., Jin, H., Guo, X., Yang, Z., Zhao, L., Tang, S., et al. (2012). Distinguishing pancreatic cancer from chronic pancreatitis and healthy individuals by (1)H nuclear magnetic resonance-based metabonomic profiles. Clinical Biochemistry, 45, 1064–1069.PubMedCrossRefGoogle Scholar
  110. Zhang, Y., Qiu, L., Wang, Y., Qin, X., & Li, Z. (2014). High-throughput and high-sensitivity quantitative analysis of serum unsaturated fatty acids by chip-based nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry: early stage diagnostic biomarkers of pancreatic cancer. Analyst, 139, 1697–1706.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Nguyen Phuoc Long
    • 1
  • Sang Jun Yoon
    • 1
  • Nguyen Hoang Anh
    • 1
  • Tran Diem Nghi
    • 2
  • Dong Kyu Lim
    • 1
  • Yu Jin Hong
    • 1
  • Soon-Sun Hong
    • 3
  • Sung Won Kwon
    • 1
  1. 1.Research Institute of Pharmaceutical Sciences and College of PharmacySeoul National UniversitySeoulSouth Korea
  2. 2.School of MedicineVietnam National UniversityHo Chi Minh CityVietnam
  3. 3.Department of Drug Development, College of MedicineInha UniversityIncheonSouth Korea

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