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

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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.

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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

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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.

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Authors and Affiliations

  1. Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea

    Nguyen Phuoc Long, Sang Jun Yoon, Nguyen Hoang Anh, Dong Kyu Lim, Yu Jin Hong & Sung Won Kwon

  2. School of Medicine, Vietnam National University, Ho Chi Minh City, 700000, Vietnam

    Tran Diem Nghi

  3. Department of Drug Development, College of Medicine, Inha University, Incheon, 22212, South Korea

    Soon-Sun Hong

Authors
  1. Nguyen Phuoc Long
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  2. Sang Jun Yoon
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  3. Nguyen Hoang Anh
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  4. Tran Diem Nghi
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  5. Dong Kyu Lim
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  6. Yu Jin Hong
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  7. Soon-Sun Hong
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  8. Sung Won Kwon
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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.

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Correspondence to Sung Won Kwon.

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The authors declare that they have no competing interests.

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

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Long, N.P., Yoon, S.J., Anh, N.H. et al. A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. Metabolomics 14, 109 (2018). https://doi.org/10.1007/s11306-018-1404-2

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