Skeletal muscle PI3K/Akt signaling and ubiquitin‐related enzyme mRNA expression in lung cancer cachexia
Bibliographic record
Abstract
The progressive loss of muscle mass is common in lung cancer. Objective to assess possible changes in signal transduction events that regulate protein synthesis and degradation in skeletal muscle of patients with early lung cancer. Muscle biopsies and blood samples were collected from non-small cell lung cancer (NSCLC) subjects, 8 with and 8 without cachexia, and 8 non-cancer controls undergoing thoracic surgery. The abundance and phosphorylation of proteins of the PI3K/Akt signaling pathway were measured by immunoblotting and expression of ubiquitin ligases and deubiquitinating enzyme USP19 mRNA, by RT-qPCR. Cachectic patients had lost 11.5 ± 1.9% body weight, had lower lean and fat mass and higher serum C-reactive protein, IL-6 and IL-8. Phospho-PRAS40Thr246 and p-FoxO1/3aThr24/32 were higher in cachectic than in other groups, despite similar p-AktSer473, but Akt, PRAS40 and FoxO3a abundance was lower. The abundance of the translational inhibitor eukaryotic initiation factor (eIF) 4E binding protein (4E-BP1) was 65% higher than in controls (P =0.009); eIF4E abundance did not differ. MuRF-1, MAFbx and USP19 mRNA expression was not different among groups. Data show lower expression of components of the PI3K/Akt signaling pathway and greater abundance of 4E-BP1 in early NSCLC cachexia that may indicate decreased mRNA translation in skeletal muscle, which in turn, could lead to muscle loss. (Funded by CIHR)
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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".