Widespread expression of Sonic hedgehog (Shh) and Nrf2 in patients treated with cisplatin predicts outcome in resected tumors and are potential therapeutic targets for HPV-negative head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Sonic hedgehog (Shh) and Nrf2 play a critical role in chemotherapeutic resistance. These two genes have been found to be dysregulated in head and neck squamous cell carcinomas (HNSCC). The purpose of this study was to analyze the expression, function and clinical prognostic relationship of Shh and Nrf2 in HNSCC in the context of therapeutic resistance and cancer stem cells (CSCs). METHODS: We analyzed a cohort of patients with HNSCC to identify potential therapeutic biomarkers correlating with overall survival (OS) as well as disease-free survival (DFS) from our own data and validated these results using The Cancer Genome Atlas dataset. Expression of Shh and Nrf2 was knocked down by siRNA and cell growth, sphere growth and chemotherapeutic resistance were evaluated. RESULTS: Widespread abundant expression of Shh and Nrf2 proteins were associated with shorter OS and DFS. The combination of Shh and Nrf2 expression levels was found to be a significant predictor of patient DFS. The tumor stromal index was correlated with Shh expression and inversely associated with shorter OS and DFS. Inhibition of Shh by siRNA or cyclopamine resulted in the attenuation of resistant CSC self-renewal, invasion, clonogenic growth and re-sensitization to the chemotherapeutic agents. Concomitant upregulation of Shh and Nrf2 proved to be an independent predictor of poor OS and DFS in patients with HNSCC. CONCLUSIONS: These findings suggest that Shh and Nrf2 could serve as therapeutic targets as well as promising dual prognostic therapeutic biomarkers for HNSCC.
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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 | 0.001 |
| 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) | 0.002 | 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".