Genetic alterations of Keap1 confers chemotherapeutic resistance through functional activation of Nrf2 and Notch pathway in head and neck squamous cell carcinoma
Bibliographic record
Abstract
Keap1 mutations regulate Nrf2 activity and lead to chemoresistance in cancers. Yet the underlying molecular mechanisms of chemoresistance are poorly explored. By focusing and genotyping head and neck squamous cell carcinoma (HNSCC) that had available pathologic and clinical data, we provide evidence that Keap1 displays frequent alterations (17%) in HNSCC. Functional loss of Keap1 results in significant activation of Nrf2 and promotes cancer cell growth, proliferation, and elevated cancer stem cell (CSCs) self-renewal efficiency and resistance to oxidative stress. Furthermore, decreased Keap1 activity in these cells increased nuclear accumulation of Nrf2 and activation of the Notch pathway, causing enhanced transcriptional alterations of antioxidants, xenobiotic metabolism enzymes, and resistance to chemotherapeutic treatment. Limiting the Nrf2 activity by either Keap1 complementation or by Nrf2 silencing increased the sensitivity to chemotherapy in Keap1-mutated cells and repressed the CSC self-renewal activity. Our findings suggest that Keap1 mutations define a distinct disease phenotype and the Keap1-Nrf2 pathway is one of the leading molecular mechanisms for clinical chemotherapeutic resistance. Targeting this pathway may provide a potential and attractive personalized treatment strategy for overcoming chemotherapeutic resistance conferred by Keap1 mutations.
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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".