Evidence map›Paper›PMID 18559081›Full record

ArticleMolecular cancer2008

Identification of metabolites with anticancer properties by computational metabolomics.

Adrian K Arakaki, Roman Mezencev, Nathan J Bowen, Ying Huang, John F McDonald, Jeffrey Skolnick

Open access · goldAbstract read
In one paragraph

Article in Molecular cancer, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

11 citing papers in PubMed, 34 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Adrian K ArakakiCenter for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia, USA. adrian.arakaki@gatech.edu
Roman Mezencev
Nathan J Bowen
Ying Huang
John F McDonald
Jeffrey Skolnick
Georgia Institute of Technology · US

Funding

Interaction Pattern Based Predictor of Protein StruturesR01GM048835 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SKOLNICK, JEFFREY · 1994 to 2015
$4.0M
NIGMS NIH HHS GM-48835NIGMS NIH HHS R01 GM048835
6 · The paper itself

Abstract

backgroundCertain endogenous metabolites can influence the rate of cancer cell growth. For example, diacylglycerol, ceramides and sphingosine, NAD+ and arginine exert this effect by acting as signaling molecules, while carrying out other important cellular functions. Metabolites can also be involved in the control of cell proliferation by directly regulating gene expression in ways that are signaling pathway-independent, e.g. by direct activation of transcription factors or by inducing epigenetic processes. The fact that metabolites can affect the cancer process on so many levels suggests that the change in concentration of some metabolites that occurs in cancer cells could have an active role in the progress of the disease.

resultsCoMet, a fully automated Computational Metabolomics method to predict changes in metabolite levels in cancer cells compared to normal references has been developed and applied to Jurkat T leukemia cells with the goal of testing the following hypothesis: Up or down regulation in cancer cells of the expression of genes encoding for metabolic enzymes leads to changes in intracellular metabolite concentrations that contribute to disease progression. All nine metabolites predicted to be lowered in Jurkat cells with respect to lymphoblasts that were examined (riboflavin, tryptamine, 3-sulfino-L-alanine, menaquinone, dehydroepiandrosterone, alpha-hydroxystearic acid, hydroxyacetone, seleno-L-methionine and 5,6-dimethylbenzimidazole), exhibited antiproliferative activity that has not been reported before, while only two (bilirubin and androsterone) of the eleven tested metabolites predicted to be increased or unchanged in Jurkat cells displayed significant antiproliferative activity.

conclusionThese results: a) demonstrate that CoMet is a valuable method to identify potential compounds for experimental validation, b) indicate that cancer cell metabolism may be regulated to reduce the intracellular concentration of certain antiproliferative metabolites, leading to uninhibited cellular growth and c) suggest that many other endogenous metabolites with important roles in carcinogenesis are awaiting discovery.

Indexed as

Cell ProliferationSystems BiologyAntimetabolites, AntineoplasticDisease ProgressionDrug DesignGene Expression Regulation, EnzymologicGene Expression Regulation, NeoplasticHumansJurkat CellsLeukemia, T-CellReproducibility of ResultsAntimetabolites, Antineoplastic

Identifiers

PMID18559081
PMCPMC2453147
OpenAlexW2110130787

What OpenQuestion holds

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LicenceCC BY
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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.