PGC-1α overexpression is not sufficient to mitigate cancer cachexia in either male or female mice
Bibliographic record
Abstract
Cancer cachexia (CC) accounts for 20%–40% of cancer-related deaths. Mitochondrial aberrations have been shown to precede muscle atrophy in different atrophy models, including cancer. Therefore, this study investigated potential protection from the cachectic phenotype through overexpression of peroxisome proliferator-activated receptor γ coactivator-1 α (PGC-1α). First, to establish potential of mitochondria-based approaches we showed that the mitochondrial antioxidant MitoTEMPO (MitoT) attenuates myotube atrophy induced by Lewis lung carcinoma (LLC) cell conditioned media. Next, cachexia was induced in muscle-specific PGC-1α overexpressing (MCK-PCG1α) or wildtype (WT) littermate mice by LLC implantation. MCK-PCG1α did not protect LLC-induced muscle mass loss. In plantaris, Atrogin mRNA content was 6.2-fold and ∼11-fold greater in WT-LLC vs WT-phosphate-buffered saline (PBS) for males and females, respectively ( p < 0.05). MitoTimer red:green ratio for male PGC was ∼65% higher than WT groups ( p < 0.05), with ∼3-fold more red puncta in LLC than PBS ( p < 0.05). Red:green ratio was ∼56% lower in females WT-LLC vs PGC-LLC ( p < 0.05). In females, no change in red puncta was noted across conditions. Lc3 mRNA content was ∼73% and 2-fold higher in male and female LLC mice, respectively, vs PBS ( p < 0.05). While MitoT could mitigate cancer-induced atrophy in vitro, PGC-1α overexpression was insufficient to protect muscle mass and mitochondrial health in vivo despite mitigation of cachexia-associated signaling pathways.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".