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Record W2065864502 · doi:10.1158/1535-7163.mct-08-0930

<i>Connexin43</i> pseudogene in breast cancer cells offers a novel therapeutic target

2009· article· en· W2065864502 on OpenAlexaff
Andrew Bier, Irene Oviedo-Landaverde, Jing Zhao, Yaël Mamane, Mustapha Kandouz, Gerald Batist

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPseudogeneGene knockdownCancerCancer researchBiologyCancer cellBreast cancerCytotoxic T cellGeneCarcinogenesisIn vitroMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Connexin43 (Cx43) is often deregulated in breast cancer tissue compared with normal adjacent tissue. Stable reexpression of Cx43 in cancer slows growth and renders the cells more sensitive to cytotoxic chemotherapeutics. Pseudogenes are often considered nonfunctional copies of DNA. The Cx43 pseudogene (PsiCx43) possesses all the features of an expressed gene and is exclusively transcribed in breast cancer cell lines and not in normal cells. PsiCx43 can be translated in vivo, and its protein exhibits growth-suppressive behavior similar to Cx43. We showed that PsiCx43 binds to the polyribosomes in breast cancer cells and that exogenous expression of PsiCx43 induces translational inhibition of Cx43. Furthermore, PsiCx43 is translated and binds more efficiently to the translational machinery than does Cx43 in an in vitro system. Following knockdown of PsiCx43 in breast cancer cells, we observed an increase in Cx43 RNA and protein. This results in increased cellular sensitivity to cytotoxic chemotherapy. Our results show that PsiCx43 acts as a posttranscriptional regulator of Cx43 in breast cancer cells, and that this represents an example of the regulation of genes by pseudogenes with potential therapeutic implications in cancer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations49
Published2009
Admission routes1
Has abstractyes

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