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Record W2140966153 · doi:10.1158/0008-5472.can-11-2502

Vav3-Rac1 Signaling Regulates Prostate Cancer Metastasis with Elevated Vav3 Expression Correlating with Prostate Cancer Progression and Posttreatment Recurrence

2012· article· en· W2140966153 on OpenAlexaff
Kai‐Ti Lin, Jianli Gong, Chien‐Feng Li, Te‐Hsuan Jang, Wen‐Ling Chen, Huei-Jane Chen, Lu‐Hai Wang

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsColumbia College
FundersChi Mei Medical CenterNational Health Research Institutes
KeywordsProstate cancerMetastasisMedicineCancerProstateCancer researchInternal medicineRAC1OncologyBone metastasisSignal transductionBiology

Abstract

fetched live from OpenAlex

Prostate cancer remains the second leading cause of cancer death in men in the Western world. Yet current therapies do not significantly improve the long-term survival of patients with distant metastasis. In this study, we investigated the role of the guanine nucleotide exchange factor Vav3 in prostate cancer progression and metastasis and found that Vav3 expression correlated positively with prostate cancer cell migration and invasion. Stimulation of the receptor tyrosine kinase EphA2 by ephrinA1 resulted in recruitment and tyrosine phosphorylation of Vav3, leading to Rac1 activation as well as increased migration and invasion in vitro. Reduction of Vav3 resulted in fewer para-aortic lymph nodes and bone metastasis in vivo. Clinically, expression of Vav3 and EphA2 was elevated in late-stage and metastatic prostate cancers. Among patients with stage IIB or earlier prostate cancer, higher Vav3 expression correlated with lower cumulative biochemical failure-free survival, suggesting that Vav3 may represent a prognostic marker for posttreatment recurrence of prostate cancer. Together, our findings provide evidence that the Vav3-mediated signaling pathway may serve as a therapeutic target for prostate cancer metastasis.

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 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 score0.088
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.103
GPT teacher head0.403
Teacher spread0.301 · 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

Citations69
Published2012
Admission routes1
Has abstractyes

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