Loss of Phosphatase and Tensin Homologue Increases Transforming Growth Factor β–Mediated Invasion with Enhanced SMAD3 Transcriptional Activity
Bibliographic record
Abstract
In normal epithelial tissues, the multifunctional cytokine transforming growth factor-beta (TGF-beta) acts as a tumor suppressor through growth inhibition and induction of differentiation whereas in advanced cancers, TGF-beta promotes tumor progression through induction of tumor invasion, neoangiogenesis, and immunosuppression. The molecular mechanisms through which TGF-beta shifts from a tumor suppressor to a tumor enhancer are poorly understood. We now show a role for the tumor suppressor phosphatase and tensin homologue deleted on chromosome 10 (PTEN) in repressing the protumorigenic effects of TGF-beta. The TGF-beta effector SMAD3 inducibly interacts with PTEN on TGF-beta treatment under endogenous conditions. RNA interference (RNAi) suppression of PTEN expression enhances SMAD3 transcriptional activity and TGF-beta-mediated induction of SMAD3 target genes whereas reconstitution of PTEN in a null cancer cell line represses the expression of TGF-beta-regulated target genes. Targeting PTEN expression through RNAi in a PTEN wild-type cell line increases TGF-beta-mediated invasion but does not affect TGF-beta-mediated growth inhibition. Reconstitution of PTEN expression in a PTEN-null cell line blocks TGF-beta-induced invasion but does not modulate TGF-beta-mediated growth regulation. These effects are distinct from Akt and Forkhead family members that also interact with SMAD3 to regulate apoptosis or proliferation, respectively. Pharmacologic inhibitors targeting TGF-beta receptors and phosphatidylinositol 3-kinase signaling downstream from PTEN cooperate to block TGF-beta-mediated invasion. Thus, the loss of PTEN expression in human cancers may contribute to a role for TGF-beta as a tumor enhancer with specific effects on cellular motility and invasion.
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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 | 0.000 | 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 | 0.001 |
| 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".