Uroporphyrinogen Decarboxylase Is a Radiosensitizing Target for Head and Neck Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it