Expression of Keratin-1 Predicts Recurrence and Treatment Response in Advanced Laryngeal Cancer: A Potential Therapeutic Target
Bibliographic record
Abstract
The survival rate of patients with advanced laryngeal cancer has not substantially improved over time. RNA sequencing analysis identified Keratin-1 (KRT1) as a gene potentially associated with cancer recurrence. This study investigated the association between KRT1 expression and recurrence in advanced laryngeal cancer. RNA sequencing was performed to identify candidate genes associated with recurrence. The effects of KRT1 expression on clinical outcomes were evaluated in patients with laryngeal cancer. Multiple experimental techniques were utilized. RNA sequencing of patient samples demonstrated higher KRT1 gene expression in the recurrence group than in non-recurrent cases. Patients with KRT1-positive immunostaining exhibited trends of worse overall survival (OS) and recurrence-free survival (RFS). In vitro studies showed that KRT1 knockdown suppressed tumor cell invasion, cell migration, and expression of epithelial-mesenchymal transition (EMT)-related genes in human head and neck squamous cell carcinoma (HNSCC) cell lines. KRT1 knockdown enhanced tumor cell apoptosis and exhibited synergistic effects with conventional radiation and chemotherapy treatments. KRT1 may serve as a biomarker for predicting advanced laryngeal cancer recurrence and assist with selecting patients to receive concurrent chemoradiotherapy (CCRT). Further molecular investigations are warranted to determine its effects, but KRT1 has potential as a therapeutic target.
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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) | 0.001 | 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".