Faculty Opinions recommendation of Loss of PDZK1 expression activates PI3K/AKT signaling via PTEN phosphorylation in gastric cancer.
Bibliographic record
Abstract
Phosphorylation of PTEN plays an important role in carcinogenesis and progression of gastric cancer. However, the underlying mechanism of PTEN phosphorylation regulation remains largely elusive. In the present study, PDZK1 was identified as a novel binding protein of PTEN by association of PTEN through its carboxyl terminus and PDZ domains of PDZK1. By direct interaction with PTEN, PDZK1 inhibited the phosphorylation of PTEN at S380/T382/T383 cluster and further enhanced the capacity of PTEN to suppress PI3K/AKT activation. PDZK1 suppressed gastric cancer cell proliferation by diminishing PI3K/AKT activation via inhibition of PTEN phosphorylation in vitro and in vivo. The expression of PDZK1 was frequently downregulated in gastric cancer specimens and correlated with progression and poor prognosis of gastric cancer patients. Downregulation of PDZK1 was associated with PTEN inactivation, AKT signaling and cell proliferation activation in clinical specimens. Thus, low levels of PDZK1 in gastric cancer specimens lead to increase proliferation of gastric cancer cells via phosphorylation of PTEN at the S380/T382/T383 cluster and constitutively activation of PI3K/AKT signaling, which results in poor prognosis of gastric cancer patients.Copyright © 2019 Elsevier B.V. All rights reserved. PMID: 30930234
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".