» Articles » PMID: 30830397

A Systematic Review on Metabolomics-based Diagnostic Biomarker Discovery and Validation in Pancreatic Cancer

Overview
Journal Metabolomics
Publisher Springer
Specialty Endocrinology
Date 2019 Mar 5
PMID 30830397
Citations 33
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.

Jain S, Bansal S, Bansal S, Singh B, Klotzbier W, Mehta K Int J Mol Sci. 2024; 25(11).

PMID: 38892088 PMC: 11172734. DOI: 10.3390/ijms25115901.


The prowess of metabolomics in cancer research: current trends, challenges and future perspectives.

Taunk K, Jajula S, Bhavsar P, Choudhari M, Bhanuse S, Tamhankar A Mol Cell Biochem. 2024; 480(2):693-720.

PMID: 38814423 DOI: 10.1007/s11010-024-05041-w.


Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Dorrani M, Zhao J, Bekhti N, Trimigno A, Min S, Ha J Metabolites. 2024; 14(5).

PMID: 38786757 PMC: 11123370. DOI: 10.3390/metabo14050280.


Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank.

Borgmastars E, Jacobson S, Simm M, Johansson M, Billing O, Lundin C J Gastrointest Oncol. 2024; 15(2):755-767.

PMID: 38756646 PMC: 11094504. DOI: 10.21037/jgo-23-930.


Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis.

Anh N, Long N, Min Y, Ki Y, Kim S, Jung C Metabolites. 2023; 13(11).

PMID: 37999208 PMC: 10672761. DOI: 10.3390/metabo13111112.


References
1.
Dunn W, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N . Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011; 6(7):1060-83. DOI: 10.1038/nprot.2011.335. View

2.
Napoli C, Sperandio N, Lawlor R, Scarpa A, Molinari H, Assfalg M . Urine metabolic signature of pancreatic ductal adenocarcinoma by (1)h nuclear magnetic resonance: identification, mapping, and evolution. J Proteome Res. 2011; 11(2):1274-83. DOI: 10.1021/pr200960u. View

3.
cajka T, Smilowitz J, Fiehn O . Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms. Anal Chem. 2017; 89(22):12360-12368. DOI: 10.1021/acs.analchem.7b03404. View

4.
Ananieva E, Wilkinson A . Branched-chain amino acid metabolism in cancer. Curr Opin Clin Nutr Metab Care. 2017; 21(1):64-70. PMC: 5732628. DOI: 10.1097/MCO.0000000000000430. View

5.
Kind T, Tsugawa H, cajka T, Ma Y, Lai Z, Mehta S . Identification of small molecules using accurate mass MS/MS search. Mass Spectrom Rev. 2017; 37(4):513-532. PMC: 8106966. DOI: 10.1002/mas.21535. View