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Metabolomics in oncology

Authors: Gurparsad Singh Suri; Gurleen Kaur; Giuseppina M. Carbone; Dheeraj Shinde;

Metabolomics in oncology

Abstract

AbstractBackgroundOncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy.Recent FindingsIn this review, the “Metabolomics” is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non‐invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review.ConclusionAlthough in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity.

Keywords

Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Reviews, Medical Oncology, metabolomics, Magnetic Resonance Imaging, Neoplasms, biomarker, cancer, metabolic reprogramming, Humans, Metabolomics, metabolism, RC254-282, Biomarkers

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Top 10%
Average
Top 10%
Green
gold