EGCG Prevents the Onset of an Inflammatory and Cancer-Associated Adipocyte-like Phenotype in Adipose-Derived Mesenchymal Stem/Stromal Cells in Response to the Triple-Negative Breast Cancer Secretome
Bibliographic record
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) cells secretome induces a pro-inflammatory microenvironment within the adipose tissue, which hosts both mature adipocytes and adipose-derived mesenchymal stem/stromal cells (ADMSC). The subsequent acquisition of a cancer-associated adipocyte (CAA)-like phenotype is, however, unknown in ADMSC. While epidemiological studies suggest that consuming a polyphenol-rich diet reduces the incidence of some obesity-related cancers, the chemopreventive impact of green tea-derived epigallocatechin-3-gallate (EGCG) against the cues that trigger the CAA phenotype remain undocumented in ADMSC. METHODS: Human ADMSC were exposed to human TNBC-derived MDA-MB-231 conditioned media (TNBC cells secretome) supplemented or not with EGCG. Differential gene expression was assessed through RNA-Seq analysis and confirmed by RT-qPCR. Protein expression levels and the activation status of signal transduction pathways mediators were determined by Western blotting. ADMSC chemotaxis was assessed by a real-time cell migration assay. RESULTS: The TNBC cells secretome induced in ADMSC the expression of the CAA cytokines CCL2, CCL5, IL-1β, and IL-6, and of immunomodulators COX2, HIF-1α, VEGFα, and PD-L1. The epithelial-to-mesenchymal biomarker Snail was found to control the CAA phenotype. EGCG inhibited the induction of CAA genes and the activation status of Smad2 and NF-κB. The induced chemotactic response was also inhibited by EGCG. CONCLUSION: The induction of an inflammatory and CAA-like phenotype in ADMSC can be triggered by the TNBC cells secretome, while still efficiently prevented by diet-derived polyphenols.
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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 | 0.001 |
| 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".