Differential expression of mi<scp>RNAs</scp> in the serum of patients with high‐risk oral lesions
Bibliographic record
Abstract
Oral cancer is one of the most commonly diagnosed cancers worldwide. Disease is often diagnosed at later stages, which is associated with a poor 5-year survival rate and a high rate of local recurrence. MicroRNAs (miRNAs), a group of small, noncoding RNAs, can be isolated from blood serum samples and have demonstrated utility as biomarkers in multiple cancer types. The aim of this study was to examine the expression profiles of circulating miRNAs in the serum of patients with high-risk oral lesions (HRLs; oral cancer or carcinoma in situ) and to explore their utility as potential oral cancer biomarkers. Global serum miRNA profiles were generated using quantitative PCR method from 1) patients diagnosed with HRLs and undergoing intent-to-cure surgical treatment (N = 30) and 2) a demographically matched, noncancer control group (N = 26). We next honed our list of serum miRNAs associated with disease by reducing the effects of interpatient variability; we compared serum miRNA profiles from samples taken both before and after tumor resections (N = 10). Based on these analyses, fifteen miRNAs were significantly upregulated and five were significantly downregulated based on presence of disease (minimum fold-change >2 in at least 50% of samples, P < 0.05, permutation). Five of these miRNAs (miR-16, let-7b, miR-338-3p, miR-223, and miR-29a) yielded an area under the ROC curve (AUC) >0.8, suggesting utility as noninvasive biomarkers for detection of oral cancer or high-grade lesions. Combining these serum miRNA profiles with other screening techniques could greatly improve the sensitivity in oral cancer detection.
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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 | 0.000 | 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".