Anticachectic regulator analysis reveals Perp-dependent antitumorigenic properties of 3-methyladenine in pancreatic cancer
Bibliographic record
Abstract
Approximately 80% of pancreatic cancer patients suffer from cachexia, and one-third die due to cachexia-related complications such as respiratory failure and cardiac arrest. Although there has been considerable research into cachexia mechanisms and interventions, there are, to date, no FDA-approved therapies. A major contributing factor for the lack of therapy options could be the failure of animal models to accurately recapitulate the human condition. In this study, we generated an aged model of pancreatic cancer cachexia to compare cachexia progression in young versus aged tumor-bearing mice. Comparative skeletal muscle transcriptome analyses identified 3-methyladenine (3-MA) as a candidate antiwasting compound. In vitro analyses confirmed antiwasting capacity, while in vivo analysis revealed potent antitumor effects. Transcriptome analyses of 3-MA-treated tumor cells implicated Perp as a 3-MA target gene. We subsequently (a) observed significantly higher expression of Perp in cancer cell lines compared with control cells, (b) noted a survival disadvantage associated with elevated Perp, and (c) found that 3-MA-associated Perp reduction inhibited tumor cell growth. Finally, we have provided in vivo evidence that survival benefits conferred by 3-MA administration are independent of its effect on tumor progression. Taken together, we report a mechanism linking 3-MA to Perp inhibition, and we further implicate Perp as a tumor-promoting factor in pancreatic cancer.
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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".