MicroRNA expression as predictor of local recurrence risk in oral squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Oral squamous cell carcinoma (OSCC) is the sixth most common cancer worldwide with a high rate of recurrence. MicroRNAs (miRNAs) are gene regulators playing an important role in oral carcinogenesis. The purpose of this study was for us to identify and functionally characterize miRNAs that predict recurrence in OSCC. METHODS: We collected 92 OSCC with their normal tissue counterparts and we performed miRNAs expression profiling on 74 OSCC and 38 normal tissues. The association between the expression of miRNAs and clinical outcome was evaluated in the follow-up of 69 patients. RESULTS: Four of the miRNAs deregulated between OSCC and normal tissues are prognostic for recurrence either when considered individually or as a group. Depletion of the expression of prognostic miRNAs inhibit the proliferation of OSCC cells CONCLUSION: MiRNAs are differentially expressed in OSCC versus normal samples. The expression of 4 prognostic miRNA signatures is able to predict recurrence risk independently from other clinical factors in OSCC. © 2015 Wiley Periodicals, Inc. Head Neck 38: E189-E197, 2016.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".