Evidence map›Paper›PMID 22956039›Full record

ArticleCancer gene therapy2012

A novel approach to cancer treatment using structural hybrids of the p53 gene family.

Y Sasaki, Y Oshima, R Koyama, M Tamura, L Kashima, M Idogawa, T Yamashita, M Toyota, K Imai, Y Shinomura and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Cancer gene therapy, 2012. 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
0.8field-weighted citation impact, top 28% 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, 7 citations in OpenAlex.

  1. Review
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

11 authors at 2 institutions in 1 country.

Y SasakiDepartment of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Sapporo, Japan.
Y Oshima
R Koyama
M Tamura
L Kashima
M Idogawa
T Yamashita
M Toyota
K Imai
Y Shinomura
T Tokino
Sapporo Medical University · JPTokyo University of Science · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The p53 tumor suppressor belongs to a gene family that includes two other structurally and functionally related members: p73 and p63. The regulation of p53 activity differs significantly from that of p73 and p63. To enhance the tumor suppressive activity of p53, we constructed six recombinant adenoviruses that encode hybrid proteins with three functional domains derived from either p53 or TAp63γ. The potency of these hybrid molecules in suppressing tumorigenesis was evaluated using in vitro and in vivo models. Of the hybrid molecules tested, one hybrid named p63-53O was the most potent activator of apoptosis in human cancer cells. The p63-53O hybrid is composed of the transcriptional activation domain and DNA-binding domain of TAp63γ and the oligomerization domain of p53. The p63-53O hybrid efficiently transactivated p53AIP1. Moreover, silencing of p53AIP1 partially abolished the apoptotic response to p63-53O in human cancer cells. The p53-p63 hybrid molecule is a novel potent anti-proliferative agent for the treatment of cancer.

Indexed as

Gene Expression Regulation, NeoplasticGenes, p53AdenoviridaeAnimalsAntineoplastic AgentsApoptosisApoptosis Regulatory ProteinsCell Line, TumorCell Transformation, NeoplasticFemaleGenetic VectorsHumansMiceMice, Inbred BALB CMice, NudeNeoplasmsAntineoplastic AgentsApoptosis Regulatory ProteinsP53AIP1 protein, humanRecombinant Fusion ProteinsTP53 protein, humanTP63 protein, humanTranscription FactorsTumor Suppressor Protein p53Tumor Suppressor Proteins

Identifiers

PMID22956039
OpenAlexW2034283662

What OpenQuestion holds

Textmetadata
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.