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Record W2155473026 · doi:10.1517/14728222.2010.487866

Gap junctions and connexins as therapeutic targets in cancer

2010· review· en· W2155473026 on OpenAlexafffund
Mustapha Kandouz, Gerald Batist

Bibliographic record

VenueExpert Opinion on Therapeutic Targets · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsConnexinGap junctionBystander effectCancerBiologyCancer cellMetastasisCell biologyNeuroscienceCancer researchIntracellularImmunologyGenetics

Abstract

fetched live from OpenAlex

IMPORTANCE OF THE FIELD: Connexins (Cxs) and gap junctional intercellular communications (GJICs) play roles in cancer development, growth and metastasis. Experimental studies suggest that targeting Cxs may be a novel technique, either to inhibit tumor cell growth directly or to sensitize to various therapies. AREAS COVERED IN THIS REVIEW: A brief introduction to the role of Cxs in cancer. The focus is mainly on data available in the literature regarding therapeutic aspects. WHAT THE READER WILL GAIN: This article reviews the various strategies that take advantage of gap junctions and connexins to eliminate cancer cells, including use of the bystander effect (BE) in gene therapy, the effect of connexins on chemosensitization, the role of apoptotic processes and interactions with the microenvironment. Attempts to restore connexin expression at the transcriptional and post-transcriptional levels are described, as well as promising strategies recently explored. The potential and limitations of the approaches are discussed. TAKE HOME MESSAGE: Connexins have multiple facets, singly, in hemichannel complexes, in gap junctions or interacting with different proteins. The regulation of their expression is not fully resolved and selective manipulation of Cxs expression is therefore a challenge. Although the therapeutic potential of connexins is undeniable, more effort is needed to study the regulation and functions of these proteins.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.386
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations128
Published2010
Admission routes2
Has abstractyes

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