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
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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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".