Perturbed BMP signaling and denervation promote muscle wasting in cancer cachexia
Bibliographic record
Abstract
Most patients with advanced solid cancers exhibit features of cachexia, a debilitating syndrome characterized by progressive loss of skeletal muscle mass and strength. Because the underlying mechanisms of this multifactorial syndrome are incompletely defined, effective therapeutics have yet to be developed. Here, we show that diminished bone morphogenetic protein (BMP) signaling is observed early in the onset of skeletal muscle wasting associated with cancer cachexia in mouse models and in patients with cancer. Cancer-mediated factors including Activin A and IL-6 trigger the expression of the BMP inhibitor Noggin in muscle, which blocks the actions of BMPs on muscle fibers and motor nerves, subsequently causing disruption of the neuromuscular junction (NMJ), denervation, and muscle wasting. Increasing BMP signaling in the muscles of tumor-bearing mice by gene delivery or pharmacological means can prevent muscle wasting and preserve measures of NMJ function. The data identify perturbed BMP signaling and denervation of muscle fibers as important pathogenic mechanisms of muscle wasting associated with tumor growth. Collectively, these findings present interventions that promote BMP-mediated signaling as an attractive strategy to counteract the loss of functional musculature in patients with 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 | 0.001 |
| 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".