Gap junctions and connexins as therapeutic targets in cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".