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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".