Identification of a microRNA signature associated with progression of leukoplakia to oral carcinoma
Bibliographic record
Abstract
MicroRNAs (miRs) are non-coding RNA molecules involved in cancer initiation and progression. Deregulated miR expression has been implicated in cancer; however, there are no studies implicating an miR signature associated with progression in oral squamous cell carcinoma (OSCC). Although OSCC may develop from oral leukoplakia, clinical and histological assessments have limited prognostic value in predicting which leukoplakic lesions will progress. Our aim was to quantify miR expression changes in leukoplakia and same-site OSCC and to identify an miR signature associated with progression. We examined miR expression changes in 43 sequential progressive samples from 12 patients and four non-progressive leukoplakias from four different patients, using TaqMan Low Density Arrays. The findings were validated using quantitative RT-PCR in an independent cohort of 52 progressive dysplasias and OSCCs, and five non-progressive dysplasias. Global miR expression profiles distinguished progressive leukoplakia/OSCC from non-progressive leukoplakias/normal tissues. One hundred and nine miRs were highly expressed exclusively in progressive leukoplakia and invasive OSCC. miR-21, miR-181b and miR-345 expressions were consistently increased and associated with increases in lesion severity during progression. Over-expression of miR-21, miR-181b and miR-345 may play an important role in malignant transformation. Our study provides the first evidence of an miR signature potentially useful for identifying leukoplakias at risk of malignant transformation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".