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Record W2163894821 · doi:10.1158/0008-5472.can-03-3038

Hypoxia Enhances Metastatic Efficiency by Up-Regulating Mdm2 in KHT Cells and Increasing Resistance to Apoptosis

2004· article· en· W2163894821 on OpenAlexaff
Li Zhang, Rićhard P. Hill

Bibliographic record

VenueCancer Research · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
FundersNational Institute on Aging
KeywordsMdm2Hypoxia (environmental)ApoptosisCancer researchTransactivationTransfectionBiologyProgrammed cell deathTumor hypoxiaMetastasisGeneChemistryTranscription factorInternal medicineMedicineCancerGeneticsOxygen

Abstract

fetched live from OpenAlex

Tumor hypoxia has been reported to be a negative prognostic factor in a number of tumor sites. Both clinical and experimental studies have suggested a positive correlation between tumor hypoxia and increased metastatic efficiency; however, the mechanisms are not understood. In this study, the mechanisms of hypoxia-enhanced metastasis have been investigated in murine KHT fibrosarcoma and SCC VII cells. We have observed that hypoxia-pretreated KHT-C cells have a higher survival rate than control KHT-C cells after being arrested in mouse lungs. cDNA microarray analysis revealed many hypoxia-regulated genes, most of which have been reported to be involved in cell survival and growth. Among these genes, we have confirmed the up-regulation of Mdm2 by hypoxia and have demonstrated that this up-regulation is p53 independent. The up-regulation of Mdm2 by hypoxia is associated with decreased p53 protein and inhibition of the transactivation of p53 downstream proapoptotic genes. Overexpression of Mdm2 or suppression of p53 by transient transfection increased metastatic efficiency in KHT-C cells. These data suggest that hypoxia can increase tumor cell metastatic efficiency by rendering the tumor cells less sensitive to stress-induced cell death, e.g., through modifying the levels of Mdm2 and p53.

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 score0.039
Threshold uncertainty score0.705

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 science0.0000.000
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 head0.025
GPT teacher head0.342
Teacher spread0.317 · 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

Citations104
Published2004
Admission routes1
Has abstractyes

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