MicroRNA-193b Enhances Tumor Progression via Down Regulation of Neurofibromin 1
Bibliographic record
Abstract
Despite improvements in therapeutic approaches for head and neck squamous cell carcinomas (HNSCC), clinical outcome has remained disappointing, with 5-year overall survival rates hovering around 40-50%, underscoring an urgent need to better understand the biological bases of this disease. We chose to address this challenge by studying the role of micro-RNAs (miRNAs) in HNSCC. MiR-193b was identified as an over-expressed miRNA from global miRNA profiling studies previously conducted in our lab, and confirmed in HNSCC cell lines. In vitro knockdown of miR-193b in FaDu cancer cells substantially reduced cell proliferation, migration and invasion, along with suppressed tumour formation in vivo. By integrating in silico prediction algorithms with in vitro experimental mRNA profilings, plus mRNA expression data of clinical specimens, neurofibromin 1 (NF1) was identified to be a target of miR-193b. Concordantly, miR-193b knockdown decreased NF1 transcript and protein levels significantly. Luciferase reporter assays confirmed the direct interaction of miR-193b with NF1. Moreover, p-ERK, a downstream target of NF1 was also suppressed after miR-193b knockdown. FaDu cells treated with a p-ERK inhibitor (U0126) phenocopied the reduced cell proliferation, migration and invasion observed with miR-193b knockdown. Finally, HNSCC patients whose tumours expressed high levels of miR-193b experienced a lower disease-free survival compared to patients with low miR-193b expression. Our findings identified miR-193b as a potentially novel prognostic marker in HNSCC that drives tumour progression via down-regulating NF1, in turn leading to activation of ERK, resulting in proliferation, migration, invasion, and tumour formation.
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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".