Uroporphyrinogen Decarboxylase Is a Radiosensitizing Target for Head and Neck Cancer
Bibliographic record
Abstract
Head and neck cancer (HNC) is the eighth most common malignancy worldwide, comprising a diverse group of cancers affecting the head and neck region. Despite advances in therapeutic options over the last few decades, treatment toxicities and overall clinical outcomes have remained disappointing, thereby underscoring a need to develop novel therapeutic approaches in HNC treatment. Uroporphyrinogen decarboxylase (UROD), a key regulator of heme biosynthesis, was identified from an RNA interference-based high-throughput screen as a tumor-selective radiosensitizing target for HNC. UROD knockdown plus radiation induced caspase-mediated apoptosis and cell cycle arrest in HNC cells in vitro and suppressed the in vivo tumor-forming capacity of HNC cells, as well as delayed the growth of established tumor xenografts in mice. This radiosensitization appeared to be mediated by alterations in iron homeostasis and increased production of reactive oxygen species, resulting in enhanced tumor oxidative stress. Moreover, UROD was significantly overexpressed in HNC patient biopsies. Lower preradiation UROD mRNA expression correlated with improved disease-free survival, suggesting that UROD could potentially be used to predict radiation response. UROD down-regulation also radiosensitized several different models of human cancer, as well as sensitized tumors to chemotherapeutic agents, including 5-fluorouracil, cisplatin, and paclitaxel. Thus, our study has revealed UROD as a potent tumor-selective sensitizer for both radiation and chemotherapy, with potential relevance to many human malignancies.
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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".