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Record W2058344326 · doi:10.1158/0008-5472.can-05-1986

<i>Pten</i> Deficiency Activates Distinct Downstream Signaling Pathways in a Tissue-Specific Manner

2006· article· en· W2058344326 on OpenAlexfundno aff
Lina I. Yoo, David W. Liu, Sandrine Le Vu, Roderick T. Bronson, Hong Wu, Junying Yuan

Bibliographic record

VenueCancer Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsnot available
FundersYork UniversityNational Cancer InstituteWashington University in St. LouisNational Institutes of HealthHarvard UniversityU.S. Department of Defense
KeywordsPTENCancer researchCarcinogenesisProtein kinase BBiologyProstatePI3K/AKT/mTOR pathwaySignal transductionEpitheliumProstate cancerHyperplasiaTumor suppressor geneCancerCell biologyEndocrinologyGenetics

Abstract

fetched live from OpenAlex

PTEN deficiency predisposes to a subset of human cancers, but the mechanism that underlies such selectivity is unknown. We have generated a mouse line that conditionally deletes Pten in urogenital epithelium. These mice develop carcinomas at high frequency in the prostate but at relatively low frequency in the bladder, despite early and complete penetrance of hyperplasia in both organs. Cell proliferation is initially high in the bladder of newborn Pten-deficient mice but within days is inhibited by p21 induction. In contrast, proliferation remains elevated in Pten-deficient prostate, where p21 is never induced, suggesting that p21 induction is a bladder-specific compensatory mechanism to inhibit proliferation caused by Pten deletion. Furthermore, the AKT/mammalian target of rapamycin growth pathway, which is highly activated in Pten-deficient prostate, is not activated in bladder epithelium. Our results reveal alternative downstream signaling pathways activated by Pten deficiency that lead to tissue-specific susceptibilities to tumorigenesis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.096
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.343
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations63
Published2006
Admission routes1
Has abstractyes

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