Myokines in treatment-naïve patients with cancer-associated cachexia
Bibliographic record
Abstract
Cancer-associated cachexia is a complex metabolic syndrome characterized by weight loss and systemic inflammation. Muscle loss and fatty infiltration into muscle are associated with poor prognosis in cancer patients. Skeletal muscle secretes myokines, factors with autocrine, paracrine and/or endocrine action, which may be modified by or play a role in cachexia. This study examined myokine content in the plasma, skeletal muscle and tumor homogenates from treatment-naïve patients with gastric or colorectal stages I-IV cancer with cachexia (CC, N = 62), or not (weight stable cancer, WSC, N = 32). Myostatin, interleukin (IL) 15, follistatin-like protein 1 (FSTL-1), fatty acid binding protein 3 (FABP3), irisin and brain-derived neurotrophic factor (BDNF) protein content in samples was measured with Multiplex technology; body composition and muscle lipid infiltration were evaluated in computed tomography, and quantification of triacylglycerol (TAG) in the skeletal muscle. Cachectic patients presented lower muscle FSTL-1 expression (p = 0.047), higher FABP3 plasma content (p = 0.0301) and higher tumor tissue expression of FABP3 (p = 0.0182), IL-15 (p = 0.007) and irisin (p = 0.0110), compared to WSC. Neither muscle TAG content, nor muscle attenuation were different between weight stable and cachectic patients. Lumbar adipose tissue (AT) index, visceral AT index and subcutaneous AT index were lower in CC (p = 0.0149, p = 0.0455 and p = 0.0087, respectively), who also presented lower muscularity in the cohort (69.2% of patients; p = 0.0301), compared to WSC. The results indicate the myokine profile in skeletal muscle, plasma and tumor is impacted by cachexia. These findings show that myokines eventually affecting muscle wasting may not solely derive from the muscle itself (as the tumor also may contribute to the systemic scenario), and put forward new perspectives on cachexia treatment targeting myokines and associated receptors and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".