Tumor Suppressor Pten Inhibits Nuclear Accumulation of β-Catenin and T Cell/Lymphoid Enhancer Factor 1–Mediated Transcriptional Activation
Bibliographic record
Abstract
beta-Catenin is a protein that plays a role in intercellular adhesion as well as in the regulation of gene expression. The latter role of beta-catenin is associated with its oncogenic properties due to the loss of expression or inactivation of the tumor suppressor adenomatous polyposis coli (APC) or mutations in beta-catenin itself. We now demonstrate that another tumor suppressor, PTEN, is also involved in the regulation of nuclear beta-catenin accumulation and T cell factor (TCF) transcriptional activation in an APC-independent manner. We show that nuclear beta-catenin expression is constitutively elevated in PTEN null cells and this elevated expression is reduced upon reexpression of PTEN. TCF promoter/luciferase reporter assays and gel mobility shift analysis demonstrate that PTEN also suppresses TCF transcriptional activity. Furthermore, the constitutively elevated expression of cyclin D1, a beta-catenin/TCF-regulated gene, is also suppressed upon reexpression of PTEN. Mechanistically, PTEN increases the phosphorylation of beta-catenin and enhances its rate of degradation. We define a pathway that involves mainly integrin-linked kinase and glycogen synthase kinase 3 in the PTEN-dependent regulation of beta-catenin stability, nuclear beta-catenin expression, and transcriptional activity. Our data indicate that beta-catenin/TCF-mediated gene transcription is regulated by PTEN, and this may represent a key mechanism by which PTEN suppresses 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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".