Evidence map›Paper›PMID 16543917›Full record

ArticleCancer gene therapy2006

Suppression of tumor growth using a recombinant adenoviral vector carrying the dominant-negative mutant gene Survivin-D53A in a nude mice model.

D-E Zhu, N Höti, Z Song, L Jin, Z Wu, Q Wu, M Wu

Abstract read
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In one paragraph

Article in Cancer gene therapy, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

D-E ZhuHefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, People's Republic of China.
N Höti
Z Song
L Jin
Z Wu
Q Wu
M Wu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Survivin (SVV), an inhibitor of apoptosis protein, is found to be upregulated in many cancers. We previously demonstrated that a dominant-negative mutant SVV-D53A was able to induce apoptosis in a p53-independent manner. Here, we report the construction and characterization of a recombinant replication-deficient adenoviral vector encoding a human SVV-D53A gene for its effectiveness against tumor growth both in vitro and in vivo. Transfection of liver tumor cells QGY-7703 with Ad-SVV-D53A results in significant apoptosis as measured by an increase in sub-G1 DNA content, procaspase-9 activation and further downstream PARP-1 cleavage. Furthermore, animal studies using QGY-7703 liver carcinoma xenografts in nude mice revealed that treatment of QGY-7703 cells with dominant-negative SVV-D53A, but not with wild-type SVV-adenovirus, prevents tumor outgrowth, inhibits growth of established tumors and results in a notably improved survival advantages in xenograft studies. Both the transferase-mediated dUTP nick-end labeling assay and immunostaining experiment demonstrated that tumor growth inhibition is associated with apoptosis induced by SVV-D53A expression. Taken together, these data suggest that recombinant adenovirus Ad-SVV-D53A carrying a Survivin dominant-negative gene SVV-D53A promotes apoptosis-mediated tumor suppression and could potentially be a promising candidate for cancer therapies.

Indexed as

TransfectionAdenoviridaeAnimalsApoptosisCarcinoma, HepatocellularCell Line, TumorFemaleGenetic TherapyGenetic VectorsHumansInfusions, ParenteralInhibitor of Apoptosis ProteinsInjections, IntralesionalLiver NeoplasmsMiceMice, Inbred BALB CBIRC5 protein, humanInhibitor of Apoptosis ProteinsMicrotubule-Associated ProteinsNeoplasm ProteinsSurvivin

Identifiers

PMID16543917

What OpenQuestion holds

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Registered trials

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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.