Establishment of a chemoresistant laryngeal cancer cell model to study chemoresistance and chemosensitization responses via transcriptomic analysis and a tumor‐on‐a‐chip platform
Bibliographic record
Abstract
Tumor resistance to chemotherapy is a common cause of cancer recurrence in patients with head and neck squamous cell carcinoma. The goal of this study is to establish and characterize a chemoresistant laryngeal cancer cell model and test its potential utility for chemosensitizing therapy. At the genotypic level, RNA sequencing confirmed that the cells acquired putative resistance with upregulated docetaxel-resistant (DR) genes (e.g., TUBB3, CYP24A1) and signaling pathways (e.g., PI3K/mTOR, autophagy). For phenotypic analysis, DR cells were co-cultured with laryngeal fibroblasts in a 2-channel microfluidic chip that mimics a hypoxic tumor core in vivo. A drug sensitivity test with a chemosensitizer, metformin (MTF), was performed on the laryngeal tumor-on-a-chip. Compared to non-treated controls, MTF-primed cancer cells exhibit higher sensitivity to docetaxel (DTX), that is, cell death. Collectively, this resistance-acquired cell model displayed presumed genotypic and phenotypic profiles of chemoresistance providing a viable option for testing new therapeutic strategies for restoring tumor sensitivity to DTX.
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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".