Hemochromatosis Enhances Tumor Progression via Upregulation of Intracellular Iron in Head and Neck Cancer
Bibliographic record
Abstract
INTRODUCTION: Despite improvements in treatment strategies for head and neck squamous cell carcinoma (HNSCC), outcomes have not significantly improved; highlighting the importance of identifying novel therapeutic approaches to target this disease. To address this challenge, we proceeded to evaluate the role of iron in HNSCC. EXPERIMENTAL DESIGN: Expression levels of iron-related genes were evaluated in HNSCC cell lines using quantitative RT-PCR. Cellular phenotypic effects were assessed using viability (MTS), clonogenic survival, BrdU, and tumor formation assays. The prognostic significance of iron-related proteins was determined using immunohistochemistry. RESULTS: In a panel of HNSCC cell lines, hemochromatosis (HFE) was one of the most overexpressed genes involved in iron regulation. In vitro knockdown of HFE in HNSCC cell lines significantly decreased hepcidin (HAMP) expression and intracellular iron level. This in turn, resulted in a significant decrease in HNSCC cell viability, clonogenicity, DNA synthesis, and Wnt signalling. These cellular changes were reversed by re-introducing iron back into HNSCC cells after HFE knockdown, indicating that iron was mediating this phenotype. Concordantly, treating HNSCC cells with an iron chelator, ciclopirox olamine (CPX), significantly reduced viability and clonogenic survival. Finally, patients with high HFE expression experienced a reduced survival compared to patients with low HFE expression. CONCLUSIONS: Our data identify HFE as potentially novel prognostic marker in HNSCC that promotes tumour progression via HAMP and elevated intracellular iron levels, leading to increased cellular proliferation and tumour formation. Hence, these findings suggest that iron chelators might have a therapeutic role in HNSCC management.
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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".