Co-existence of high levels of the PTEN protein with enhanced Akt activation in renal cell carcinoma
Bibliographic record
Abstract
Recruiting Akt to the membrane-bound phosphatidylinositol (3,4,5) trisphosphate (PIP3) is required for Akt activation. While PI3 kinase (PI3K) produces PIP3, PTEN dephosphorylates the 3-position phosphate from PIP3, thereby directly inhibiting Akt activation. PTEN is the dominant PIP3 phosphatase, as knockdown of PTEN results in increases in Akt activation in mice. The PTEN tumor suppressor gene is frequently mutated in a variety of human cancers, consistent with an inverse correlation between levels of the PTEN protein and Akt activation. We have examined PTEN expression and Akt activation in 35 primary clear cell renal cell carcinomas RCCs (ccRCCs) and 9 papillary RCCs (pRCCs) and their respective non-tumor kidney tissues. The PTEN protein was reduced in 16 ccRCCs (16/35=45.7%) and 8 pRCCs (8/9=88.9%). In these RCCs, 25.0% (4/16) of ccRCCs and 25.0% (2/8) of pRCCs expressed elevated Akt activation. 19 ccRCCc (19/35=54.3%) expressed comparable or higher levels of PTEN. Of these ccRCCs, 31.6% (6/19) showed increases in Akt activation. As PTEN dominantly inhibits Akt activation, the coexistence of high levels of the PTEN protein with enhanced Akt activation suggests the existence of novel mechanisms which attenuate PTEN function in ccRCC. These mechanisms may reduce PTEN function or increase PIP3 production.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".