β-Catenin and NF-κB co-activation triggered by TLR3 stimulation facilitates stem cell-like phenotypes in breast cancer
Bibliographic record
Abstract
Cancer stem cells (CSCs) are responsible for tumor initiation and progression. Toll-like receptors (TLRs) are highly expressed in cancer cells and associated with poor prognosis. However, a linkage between CSCs and TLRs is unclear, and potential intervention strategies to prevent TLR stimulation-induced CSC formation and underlying mechanisms are lacking. Here, we demonstrate that stimulation of toll-like receptor 3 (TLR3) promotes breast cancer cells toward a CSC phenotype in vitro and in vivo. Importantly, conventional NF-κB signaling pathway is not exclusively responsible for TLR3 activation-enriched CSCs. Intriguingly, simultaneous activation of both β-catenin and NF-κB signaling pathways, but neither alone, is required for the enhanced CSC phenotypes. We have further identified a small molecule cardamonin that can concurrently inhibit β-catenin and NF-κB signals. Cardamonin is capable of effectively abolishing TLR3 activation-enhanced CSC phenotypes in vitro and successfully controlling TLR3 stimulation-induced tumor growth in human breast cancer xenografts. These findings may provide a foundation for developing new strategies to prevent the induction of CSCs during cancer therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".