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Record W1987471675 · doi:10.1371/journal.pgen.1000923

Cdh11 Acts as a Tumor Suppressor in a Murine Retinoblastoma Model by Facilitating Tumor Cell Death

2010· article· en· W1987471675 on OpenAlexafffund
Mellone N. Marchong, Christine Yurkowski, Clement Ma, Clarellen Spencer, Sanja Pajovic, Brenda L. Gallie

Bibliographic record

VenuePLoS Genetics · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
FundersNational Cancer InstituteRIKENNational Institutes of HealthHospital for Sick Children
KeywordsRetinoblastomaBiologyCarcinogenesisCancer researchGene knockdownTumor suppressor geneIn vivoNull alleleCell growthConditional gene knockoutPathologyApoptosisGeneticsGeneAlleleMedicinePhenotype

Abstract

fetched live from OpenAlex

CDH11 gene copy number and expression are frequently lost in human retinoblastomas and in retinoblastomas arising in TAg-RB mice. To determine the effect of Cdh11 loss in tumorigenesis, we crossed Cdh11 null mice with TAg-RB mice. Loss of Cdh11 had no gross morphological effect on the developing retina of Cdh11 knockout mice, but led to larger retinal volumes in mice crossed with TAg-RB mice (p = 0.01). Mice null for Cdh11 presented with fewer TAg-positive cells at postnatal day 8 (PND8) (p = 0.01) and had fewer multifocal tumors at PND28 (p = 0.016), compared to mice with normal Cdh11 alleles. However, tumor growth was faster in Cdh11-null mice between PND8 and PND84 (p = 0.003). In tumors of Cdh11-null mice, cell death was decreased 5- to 10-fold (p<0.03 for all markers), while proliferation in vivo remained unaffected (p = 0.121). Activated caspase-3 was significantly decreased and beta-catenin expression increased in Cdh11 knockdown experiments in vitro. These data suggest that Cdh11 displays tumor suppressor properties in vivo and in vitro in murine retinoblastoma through promotion of cell death.

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 score0.191
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 integrity0.0000.001
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations55
Published2010
Admission routes2
Has abstractyes

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