Correlation of ABI1 and PTEN expression during prostate tumor progression.
Bibliographic record
Abstract
172 Background: Mechanisms of tumor invasion are not well defined. PTEN, a key tumor suppressor frequently inactivated in epithelial cancers, acts as a central node that controls tumor invasion. Despite PI-3 kinase-phospho-Akt pathway activation resulting in enhanced tumor growth, prostate tumors with PTEN loss undergo p53-mediated senescence that restricts tumor invasion. Methods: ABI1 downregulation is associated with epithelial-mesenchymal transition in highly invasive prostate tumors; these tumors frequently loose PTEN; therefore we set to examine genetic interaction of ABI1 and PTEN using novel mouse model of prostate cancer. We analyzed the correlation of ABI1 and PTEN expression in human PCa tumor tissue. Results: Here, using Abi1/Pten KO mouse model we identified a novel mechanism that guards tumor invasion. In Pten-null tumors upregulation of Abi1 leads to sequestration of activated Src kinase. In the absence of Abi1, this regulation is lost leading to activation of non-canonical WNT-SRC-STAT3 axis and enhanced invasion through activation of MMP2 activity. This molecular mechanism explains progression of tumors with Pten loss from PIN to invasive carcinoma upon concomitant Abi1 inactivation. In human tumors with low Abi1 and Pten are associated with aggressive phenotype, biochemical recurrence and metastasis. Conclusions: ABI1 acts as failsafe mechanism in PTEN null tumors by restricting SRC-mediated tumor invasion. ABI1 might have a predictive value in clinical setting in context of PTEN levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".