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

Long, Nguyen Phuoc; Yoon, Sang Jun; Anh, Nguyễn Hoàng; Nghi, Tran Diem; Lim, Dong Kyu; Hong, Yu Jin; Hong, Sung Kyung; Kwon, Sung Won

doi:10.1007/s11306-018-1404-2

Cited by 54 publications

(58 citation statements)

References 107 publications

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“…The utility of a single biomarker is limited in terms of the actual potential of biomarker candidates in clinical settings. Multiplex biomarker panels combined with state-of-the-art statistical learning are expected to help improve the clinical usability of FOXs in early detection, diagnosis, prognosis, and treatment of cancer patients [ 216 ].…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

The Dominant Role of Forkhead Box Proteins in Cancer

Bach

Long

Luu

et al. 2018

IJMS

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Forkhead box (FOX) proteins are multifaceted transcription factors that are significantly implicated in cancer, with various critical roles in biological processes. Herein, we provide an overview of several key members of the FOXA, FOXC, FOXM1, FOXO and FOXP subfamilies. Important pathophysiological processes of FOX transcription factors at multiple levels in a context-dependent manner are discussed. We also specifically summarize some major aspects of FOX transcription factors in association with cancer research such as drug resistance, tumor growth, genomic alterations or drivers of initiation. Finally, we suggest that targeting FOX proteins may be a potential therapeutic strategy to combat cancer.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

The Dominant Role of Forkhead Box Proteins in Cancer

Bach

Long

Luu

et al. 2018

IJMS

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“…Hence, meta-analysis has evolved as a method of choice to enhance the study's robustness and minimize the risk of false discoveries [141]. Finally, the value of meta-analysis in metabolomics has been demonstrated to increase the probability of determining metabolites associated with the phenotype of interest [142,143]. Robust meta-analysis and network-based interaction visualization frameworks should be investigated for metabolomics datasets to bring the interpretation of metabolomics data to the next level.…”

Section: Toward Reproducible Data Analysis and Interpretation In Clinmentioning

confidence: 99%

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

Self Cite

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Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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“…Significant features were assumed to be either [M-H]or [M+H] + ions. All mass windows were differences relative to one of the two states: [0.994, 1.012] = 1 13…”

Section: Adduct and Isotope Determinationmentioning

confidence: 99%

“…Metabolomics rapidly supplies information on thousands of molecules, and represents a method for biofluid-based diagnostics [7][8][9] . To date, serum, plasma, urine and cerebrospinal fluid (CSF) metabolomics has been applied to many health states, ranging from cancers [10][11][12][13] and infectious diseases 14,15 to Chronic Obstructive Pulmonary Disease (COPD) 16 , smoking 17 and Alzheimer's disease 18,19 . These studies are regularly performed using analytical instrumentation like liquid or gas chromatography mass spectrometry (LC-MS and GC-MS respectively) as well as nuclear magnetic resonance (NMR) 20 .…”

mentioning

confidence: 99%

Predicting human health from biofluid-based metabolomics using machine learning

Evans

Duvallet

Oberst

et al. 2020

Preprint

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Biofluid-based metabolomics enables the profiling of thousands of molecules and has the potential to provide highly accurate, minimally invasive diagnostics for a range of health conditions. However, typical metabolomics studies focus on only a few statistically significant features. We study the applicability of machine learning for health state-prediction across 35 human mass spectrometry-based metabolomics studies. Models trained on all features outperform those using only significant features and frequently provide high predictive performance across nine health states, despite disparate experimental conditions and disease contexts. Combining data from different experimental settings (e.g. sample type, instrument, chromatography) within a study minimally alters predictive performance, suggesting information overlap between different methods. Using only non-significant features, we still often obtain high predictive performance. To facilitate further advances, we provide all data online. This work highlights the applicability of biofluid-based metabolomics with data-driven analysis for health state diagnostics.

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

Cited by 54 publications

References 107 publications

The Dominant Role of Forkhead Box Proteins in Cancer

The Dominant Role of Forkhead Box Proteins in Cancer

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Predicting human health from biofluid-based metabolomics using machine learning

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