TGF-β1 stimulates migration of type II endometrial cancer cells by down-regulating PTEN via activation of SMAD and ERK1/2 signaling pathways
Bibliographic record
Abstract
// Siyuan Xiong 1 , Jung-Chien Cheng 1 , Christian Klausen 1 , Jianfang Zhao 1 , Peter C.K. Leung 1 1 Department of Obstetrics and Gynaecology, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia, Canada V5Z 4H4 Correspondence to: Peter C.K. Leung, email: peter.leung@ubc.ca Keywords: TGF-β1, PTEN, type II endometrial cancer, migration Received: April 12, 2016 Accepted: August 10, 2016 Published: August 16, 2016 ABSTRACT PTEN acts as a tumor suppressor primarily by antagonizing the PI3K/AKT signaling pathway. PTEN is frequently mutated in human cancers; however, in type II endometrial cancers its mutation rate is very low. Overexpression of TGF-β1 and its receptors has been reported to correlate with metastasis of human cancers and reduced survival rates. Although TGF-β1 has been shown to regulate PTEN expression through various mechanisms, it is not yet known if the same is true in type II endometrial cancer. In the present study, we show that treatment with TGF-β1 stimulates the migration of two type II endometrial cancer cell lines, KLE and HEC-50. In addition, TGF-β1 treatment down-regulates both mRNA and protein levels of PTEN. Overexpression of PTEN or inhibition of PI3K abolishes TGF-β1-stimulated cell migration. TGF-β1 induces SMAD2/3 phosphorylation and knockdown of common SMAD4 inhibits the suppressive effects of TGF-β1 on PTEN mRNA and protein. Interestingly, TGF-β1 induces ERK1/2 phosphorylation and pre-treatment with a MEK inhibitor attenuates the suppression of PTEN protein, but not mRNA, by TGF-β1. This study provides important insights into the molecular mechanisms mediating TGF-β1-induced down-regulation of PTEN and demonstrates an important role of PTEN in the regulation of type II endometrial cancer cell migration.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".