Evidence map›Paper›PMID 24454532›Full record

ArticleComputational and mathematical methods in medicine2013

Mathematical model of dynamic protein interactions regulating p53 protein stability for tumor suppression.

Hua Wang, Guang Peng

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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 1 country.

Hua WangMathematical Sciences, Georgia Southern University, Statesboro, GA 30458, USA.
Guang PengDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Georgia Southern University · USThe University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
NCI NIH HHS CA016672NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

In the field of cancer biology, numerous genes or proteins form extremely complex regulatory network, which determines cancer cell fate and cancer cell survival. p53 is a major tumor suppressor that is lost in more than 50% of human cancers. It has been well known that a variety of proteins regulate its protein stability, which is essential for its tumor suppressive function. It remains elusive how we could understand and target p53 stabilization process through network analysis. In this paper we discuss the use of random walk and stationary distribution to measure the compound effect of a network of genes or proteins. This method is applied to the network of nine proteins that influence the protein stability of p53 via regulating the interaction between p53 and its regulator MDM2. Our study identifies that some proteins such as HDAC1 in the network of p53 regulators may have more profound effects on p53 stability, agreeing with the established findings on HDAC1. This work shows the importance of using mathematical analysis to dissect the complexity of biology networks in cancer.

Indexed as

Gene Expression RegulationAlgorithmsCell CycleComputational BiologyDNA DamageGene Expression ProfilingGenes, Tumor SuppressorHistone Deacetylase 1HumansModels, BiologicalNeoplasmsProtein Interaction MappingProtein StabilitySignal TransductionSoftwareTumor Suppressor Protein p53HDAC1 protein, humanHistone Deacetylase 1TP53 protein, humanTumor Suppressor Protein p53

Identifiers

PMID24454532
PMCPMC3888710
OpenAlexW2156882828

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.