Characterization of ferroptosis driver gene signature in head and neck squamous cell carcinoma (HNSC).
Bibliographic record
Abstract
BACKGROUND: Head and neck squamous cell carcinoma (HNSC), a prevalent malignant tumor with a low survival rate, is often accompanied by ferroptosis, which is a recently-described type ofprogrammed cell death. Investigating the significance of ferroptosis driver genes in HNSC, this study aimed to assess their diagnostic and prognostic values, as well as their impact on treatment and tumor immune function. The results of this investigation provide novel insight into using ferroptosis-related genes as molecular biomarkers as well as precise chemotherapeutic targets for the therapy of HNSC. METHODOLOGY: A detailed in silico and in vitro experiment-based methodology was adopted to achieve the goals. RESULTS: A total of 233 ferroptosis driver genes were downloaded from the FerrDB database. After comprehensively analyzing these 233 ferroptosis driver genes by various TCGA databases, RNA-sequencing (RNA-seq), and Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) techniques, TP53 (tumor protein 53), PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), and HRAS (Harvey Rat sarcoma virus) were identified as differentially expressed hub genes. Interestingly, these hub genes were found to have significant (P < 0.05) variations in their mRNA and protein expressions and effects on overall survival of the HNSC patients. Moreover, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed that promoter hypomethylation pattern was associated with up-regulation of hub genes (TP53, PTEN, KRAS, and HRAS). In addition to this, hub genes were involved in diverse oncogenic pathways. CONCLUSION: Since HNSC pathogenesis is a complex process, using ferroptosis driver hub genes (TP53, PTEN, KRAS, and HRAS) as a diagnostic and prognostic tool, and therapeutically targeting those genes through appropriate drugs could bring a milestone change in the drug discovery and management and survival in HNSC.
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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 | 0.001 | 0.001 |
| 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".