MicroRNA-129-3p Suppresses Tumor Progression and Chemoradioresistance in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
(1) Background: MicroRNA-129 (miR-129) participates in tumor progression and chemoresistance in various cancer types. In this study, the role of miR-129-3p, the main mature form of miR-129, in tumor progression and chemoradiotherapy resistance in head and neck cancer was evaluated. (2) Methods: RT-PCR, western blotting, cell proliferation assays, cell apoptosis assays, and cell invasion and migration assays were used. (3) Results: In this study, the miR-129-3p overexpression suppressed the proliferation, invasion, and migration of SNU1041, SCC15, and SCC25 human HNSCC cell lines. Additionally, it induced apoptosis and enhanced radiation-/cisplatin-induced apoptosis of SNU1041, SCC15, and SCC25 cells. Therefore, miR-129-3p could suppress tumor progression and enhance chemoradiosensitivity in human HNSCC. Furthermore, miR-129-3p was downregulated in fresh tumor tissues from patients with HNSCC compared with that in the adjacent normal mucosa. In a nude mouse xenograft model with SNU15 cells, the miR-129-3p overexpression significantly decreased tumor growth, as measured by tumor weight and volume. (4) Conclusions: Our study provides evidence that miR-129-3p suppresses tumor progression and chemoradioresistance in HNSCC. This finding may serve as a basis for developing miR-129-3p-based therapeutic strategies.
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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".