Genetic Interpretation of the Impacts of Honokiol and EGCG on Apoptotic and Self-Renewal Pathways in HEp-2 Human Laryngeal CD44<sup>high</sup> Cancer Stem Cells
Bibliographic record
Abstract
Most current larynx cancer therapies are generally aimed at the global mass of tumor, targeting the non-tumorigenic cells, and unfortunately sparing the tumorigenic cancer stem cells (CSCs) that are responsible for sustained growth, metastasis, and chemo- and radioresistance. Phytochemicals and herbs have recently been introduced as therapeutic sources for eliminating CSCs. Therefore, we assessed the anti-tumor effects of two herbal ingredients, the green tea extract “Epigallocatechin-3-gallate (EGCG)” and Honokiol (HNK), on parental cells or CD44high CSCs of the human laryngeal squamous cell carcinoma cell line HEp-2. Results revealed that EGCG had a preeminent apoptotic potential on HEp-2 laryngeal CSCs. HNK conferred higher cytotoxic impacts on parental cells mostly by necrosis induction, especially with higher doses, but apoptosis induction with lower doses was also observed. The Notch signaling pathway genes were more potently suppressed by EGCG than HNK. However, HNK surpassed EGCG in downregulating the β-catenin and the Sonic Hedgehog signaling pathways genes. On a genetic basis, both agents engaged the BCL-2 family-regulated and caspase-dependent intrinsic apoptotic pathway, but EGCG and HNK triggered apoptosis via p53-independent and p53-dependent pathways, respectively. Taken together, EGCG and HNK eradicated HEp-2 human larynx cancer cells through targeting multiple self-renewal pathways and activating diverse cell death modalities.
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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".