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Record W2035353347 · doi:10.1158/0008-5472.can-05-1169

Overexpressed β-Catenin Blocks Nitric Oxide–Induced Apoptosis in Colonic Cancer Cells

2005· article· en· W2035353347 on OpenAlexaff
Hongying Wang, Wallace K. MacNaughton

Bibliographic record

VenueCancer Research · 2005
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNitric oxideApoptosisNitric oxide synthaseCarcinogenesisCancer researchProgrammed cell deathCytochrome cInflammationProtein kinase BCell biologyChemistryBiologyCancerEndocrinologyInternal medicineBiochemistryImmunologyMedicine

Abstract

fetched live from OpenAlex

Beta-catenin plays an important role in colonic tumorigenesis whereas inducible nitric oxide synthase and nitric oxide are elevated in colonic inflammation. Resistance of colonic epithelial cells to the induction of apoptosis may contribute to tumor development. Nitric oxide can stimulate apoptosis and, paradoxically, is implicated in the development of colon cancer. Our hypothesis was that beta-catenin could increase the resistance of colonic cancer cells to nitric oxide-induced apoptotic cell death. Here we show, using a beta-catenin overexpression system, that increased cytosolic beta-catenin renders colonic epithelial cells more resistant to nitric oxide-induced apoptotic cell death, independently of nitric oxide-induced accumulation of p53. Furthermore, we show that this occurs through inhibition of nitric oxide-induced release of cytochrome c from mitochondria and by blocking both the nitric oxide-induced suppression of the antiapoptotic protein, Bcl-xL, and the phosphorylation of Akt. We contend that increased nitric oxide production, such as that which occurs in chronic colonic inflammation, may select the cells with oncogenic mutant beta-catenin regulatory genes and contribute to human colonic carcinogenesis and tumor progression.

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 categoriesInsufficient payload (model declined to judge)
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 score0.058
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.106
GPT teacher head0.418
Teacher spread0.313 · 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

Citations45
Published2005
Admission routes1
Has abstractyes

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