Breast cancer-secreted factors induce atrophy and reduce basal and insulin-stimulated glucose uptake by inhibiting Rac1 activation in rat myotubes
Bibliographic record
Abstract
Abstract Background Metabolic disorders are prevalent in women with breast cancer, increasing mortality and cancer recurrence rates. Despite clinical implications, the cause of breast cancer-associated metabolic dysfunction remains poorly understood. Skeletal muscle is crucial for insulin-stimulated glucose uptake, thus key to whole-body glucose homeostasis. In this study, we determined the effect of breast cancer cell-conditioned media on skeletal muscle glucose uptake in response to insulin. Method L6 myotubes overexpressing myc-tagged GLUT4 (GLUT4myc-L6) were incubated with 40% conditioned media from tumorigenic MCF7 or BT474, or non-tumorigenic control MCF10A breast cells. Mass-spectrometry-based proteomics was applied to detect molecular rewiring in response to breast cancer in the muscle. Expression of myogenesis and inflammation markers, GLUT4 translocation, [ 3 H]-2-deoxyglucose (2DG) uptake, and intramyocellular insulin signalling were determined. Results Breast cancer cell-conditioned media induced proteomic changes in pathways related to sarcomere organisation, actin filament binding, and vesicle trafficking, disrupted myogenic differentiation, activated an inflammatory response via NF-κB, and induced muscle atrophy. Basal and insulin-stimulated GLUT4 translocation and 2DG uptake were reduced in myotubes treated with breast cancer cell-conditioned media compared to the control. Insulin signalling via the Rho GTPase Rac1 was blocked in breast cancer-treated myotubes, while Akt-TBC1D4 signalling was unaffected. Conclusion Conditioned media from MCF7 and BT474 breast cancer cells reduced skeletal muscle glucose uptake via inhibition of GLUT4 translocation and intramyocellular insulin signalling by selectively blocking Rac1 activation and inducing inflammation. These findings indicate that the rewiring of skeletal muscle proteome, inflammation, and insulin signalling could play a role in metabolic dysfunction in patients with breast 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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".