Epigallocatechin-3-Gallate Prevents the Acquisition of a Cancer Stem Cell Phenotype in Ovarian Cancer Tumorspheres through the Inhibition of Src/JAK/STAT3 Signaling
Why this work is in the frame
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Bibliographic record
Abstract
Three-dimensional tumorsphere cultures recapitulate the expression of several cancer stem cell (CSC) biomarkers and represent an effective in vitro platform to screen the anti-CSC properties of drugs. Whereas ovarian carcinoma is among the leading causes of death for women, ovarian CSC (OvCSC), a highly malignant subpopulation of ovarian cancer cells, is thought to be responsible for therapy resistance, metastasis, and tumor relapse. Epigallocatechin-3-gallate (EGCG), a diet-derived active polyphenol found in green tea leaves, can suppress ovarian cancer cell proliferation and induce apoptosis. However, its capacity to prevent the acquisition of cancer stemness traits in ovarian malignancies remains unclear. Here, we exploited the in vitro three-dimensional tumorsphere culture model to explore the capacity of EGCG to alter CSC biomarkers expression, signal transducing events and cell chemotaxis. Total RNA and protein lysates were isolated from human ES-2 ovarian cancer cell tumorspheres for gene assessment by RT-qPCR and protein expression by immunoblot. Real-time cell chemotaxis was assessed with xCELLigence. Compared with their parental adherent cells, tumorspheres expressed increased levels of the CSC markers NANOG, SOX2, PROM1, and Fibronectin. EGCG treatment reduced dose-dependently tumorspheres size and inhibited the transcriptional regulation of those genes. Src and JAK/STAT3 signaling pathways appeared to be relevant for CSC phenotype and chemotactic response. In conclusion, these data highlight and support the chemopreventive benefits of the diet-derived EGCG and its capacity to target intracellular transducing events that regulate the acquisition of an invasive CSC phenotype.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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