Evidence map›Paper›PMID 27616976›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics.

Emily G Armitage, Andrew D Southam

Open access · hybridAbstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 1 synthesis or guideline pooled it, 116 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  8. A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024
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  16. Cancer 3D Models for Metallodrug Preclinical Testing.International journal of molecular sciences · 2023
    Review
  17. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Emily G ArmitageCentre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain ; Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8TA UK ; Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH UK.
Andrew D SouthamSchool of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.ORCID 0000-0003-3030-7663
Universidad San Pablo CEU · ESUniversity of Birmingham · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

objectivesDiscuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

methodsA literature search of the current relevant primary research was performed.

resultsMetabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

conclusionMetabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

Indexed as

Drug redeploymentLeukemiaMass spectrometryNuclear magnetic resonanceNutraceuticalStratified medicine

Identifiers

PMID27616976
PMCPMC4987388
OpenAlexW2511226988

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.