Evidence map›Paper›PMID 26835512›Full record

ArticleJournal of cancer sciences2015

HTLV-I Tax-Mediated Inactivation of Cell Cycle Checkpoints and DNA Repair Pathways Contribute to Cellular Transformation: "A Random Mutagenesis Model".

Christophe Nicot

Open access · diamondAbstract read
In one paragraph

Article in Journal of cancer sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

27 citing papers in PubMed, 37 citations in OpenAlex.

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  8. IncreasedInternational journal of molecular sciences · 2024
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  18. Molecular mechanisms of viral oncogenesis in humans.Nature reviews. Microbiology · 2018
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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

1 author at 1 institution in 1 country.

Christophe NicotDepartment of Pathology and Laboratory Medicine, Center for Viral Oncology, University of Kansas Medical Center, USA.
University of Kansas Medical Center · US

Funding

Mechanisms of human T-cell immortalization by HTLV-I TaxR01CA106258 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI NICOT, CHRISTOPHE P · 2005 to 2015
$2.2M
NCI NIH HHS R01 CA106258
6 · The paper itself

Abstract

To achieve cellular transformation, most oncogenic retroviruses use transduction by proto-oncogene capture or insertional mutagenesis, whereby provirus integration disrupts expression of tumor suppressors or proto-oncogenes. In contrast, the Human T-cell leukemia virus type 1 (HTLV-I) has been classified in a separate class referred to as "transactivating retroviruses". Current views suggest that the viral encoded Tax protein transactivates expression of cellular genes leading to deregulated growth and transformation. However, if Tax-mediated transactivation was indeed sufficient for cellular transformation, a fairly high frequency of infected cells would eventually become transformed. In contrast, the frequency of transformation by HTLV-I is very low, likely less than 5%. This review will discuss the current understanding and recent discoveries highlighting critical functions of Tax in cellular transformation. HTLV-I Tax carries out essential functions in order to override cell cycle checkpoints and deregulate cellular division. In addition, Tax expression is associated with increased DNA damage and genome instability. Since Tax can inhibit multiple DNA repair pathways and stimulate unfaithful DNA repair or bypass checkpoints, these processes allow accumulation of genetic mutations in the host genome. Given this, a "Random Mutagenesis" transformation model seems more suitable to characterize the oncogenic activities of HTLV-I.

Identifiers

PMID26835512
PMCPMC4729388
OpenAlexW2469194420

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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.