mTOR Activation Initiates Renal Cell Carcinoma Development by Coordinating ERK and p38MAPK
Bibliographic record
Abstract
Abstract Renal cell carcinoma (RCC) mainly originates from renal proximal tubules. Intriguingly, disruption of genes frequently mutated in human RCC samples thus far has only generated RCC originated from other renal tubule parts in mouse models. This hampers our understanding of the pathogenesis of RCC. Here we show that mTOR signaling, often activated in RCC samples, initiates RCC development from renal proximal tubules. Ablation of Tsc1, encoding an mTOR suppressor, in proximal tubule cells led to multiple precancerous renal cysts. mTOR activation increased MEK1 expression and ERK activation, and Mek1 ablation or inhibition diminished cyst formation in Tsc1-deficient mice. mTOR activation also increased MKK6 expression and p38MAPK activation, and ablation of the p38α-encoding gene further enhanced cyst formation and led to RCC with clear cell RCC features. Mechanistically, Tsc1 deletion induced p53 and p16 expression in a p38MAPK-dependent manner, and deleting Tsc1 and Trp53 or Cdkn2a (encoding p16) enhanced renal cell carcinogenesis. Thus, mTOR activation in combination with inactivation of the p38MAPK–p53/p16 pathway drives RCC development from renal proximal tubules. Moreover, this study uncovers previously unidentified mechanisms by which mTOR controls cell proliferation and suggests the MEK–ERK axis to be a potential target for treatment of RCC. Significance: Mouse modeling studies show that mTOR activation in combination with inactivation of the p38MAPK–p53/p16 axis initiates renal cell carcinoma that mimics human disease, identifying potential therapeutic targets for RCC treatment.
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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 | 0.000 |
| 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".