MicroRNAs in an Oral Cancer Context – from Basic Biology to Clinical Utility
Bibliographic record
Abstract
Oral cancer is one of the most commonly diagnosed malignancies worldwide. Its dismal five-year survival rate of ~50% has barely changed for decades. A better understanding of the molecular basis of tumorigenesis - with particular emphasis on disease initiation and progression - is needed to improve clinical outcomes, since this will facilitate the development of drugs and management strategies based on the specific genetic changes underpinning disease behaviors. MicroRNAs (miRNAs), a class of short non-coding RNAs that down-regulate gene expression, have been demonstrated to play essential roles in human cancers. miRNA deregulation has been observed in many tumor types and is implicated in oncogenic cell processes, including proliferation, survival, apoptosis, metastasis, and chemoresistance. In addition, miRNA alterations have been associated with specific clinical phenotypes such as disease progression or recurrence, development of metastases, and post-operative survival. Recent studies have explored the utility of miRNAs as diagnostic and prognostic tools and as potential therapeutic targets. Herein, we discuss miRNA biology and provide a summary of the key findings on the role of miRNAs in oral malignancies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".