A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer
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.
In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.
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.
KeywordsPancreatic cancer Metabolomics Diagnostic biomarkers Systematic review
Area under the curve
Branched-chain amino acids
Gas chromatography–mass spectrometry
Liquid chromatography–mass spectrometry
Magnetic resonance imaging
Tandem mass spectrometry
Nuclear magnetic resonance
Orthogonal projections to latent structures discriminant analysis
Principal component analysis
Pancreatic ductal adenocarcinoma
Partial least squares discriminant analysis
Partial least squares discriminant function
Cross-validated coefficient of determination
Coefficient of determination
Receiver operating characteristic curve
Supercritical fluid extraction-supercritical fluid chromatography coupled with tandem mass spectrometry
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.
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.
This article does not contain any studies with human participants performed by any of the authors.
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. arXiv:1602.04938v3.
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar