EGCG inhibits the inflammation and senescence inducing properties of MDA-MB-231 triple-negative breast cancer (TNBC) cells-derived extracellular vesicles in human adipose-derived mesenchymal stem cells
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Bibliographic record
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) cells' secretome can induce a pro-inflammatory phenotype in human adipose-derived mesenchymal stem cells (hADMSC). This can be prevented by the green tea polyphenol epigallocatechin-3-gallate (EGCG). The impact of EGCG on the paracrine regulation that the extracellular vesicles (EVs) specifically exert within the TNBC secretome remains unknown. METHODS: ) culture conditions. RNA-Seq analysis was used to assess the EVs' genetic content. The modulation of inflammatory and senescence markers in hADMSC was evaluated by RT-qPCR using cDNA arrays and validated by immunoblotting. A protein profiler phospho-kinase array was used to explore signaling pathways. RESULTS: While hypoxic culture conditions did not significantly alter the genetic content of MDA-MB-231-secreted EVs, the addition of EGCG significantly modified EVs genetic material at low oxygen tension. Gene expression of cancer-associated adipocyte pro-inflammatory markers CXCL8, CCL2 and IL-1β was increased in hADMSC treated with EVs. Concomitantly, EVs isolated from MDA-MB-231 treated with EGCG (EGCG-EVs) downregulated CCL2 and IL-1β, while inducing higher expression of CXCL8 and IL-6 levels. EVs activated CHK-2, c-Jun, AKT and GSK-3β signaling pathways in hADMSC, whereas EGCG-EVs specifically reduced the latter two as well as the serum starvation-induced senescence markers p21 and β-galactosidase. Finally, the mitochondrial content within the TNBC cells-derived EVs was found reduced upon EGCG treatment. CONCLUSION: This proof of concept study demonstrates that the chemopreventive properties of diet-derived polyphenols may efficiently target the paracrine regulation that TNBC cells could exert upon their surrounding adipose tissue microenvironment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it