Abstract PO-047: A novel saliva miRNA panel of promising diagnostic biomarkers for oral cancer: The association of miR-21 with smoking history
Bibliographic record
Abstract
Abstract Introduction: Tobacco use is implicated in the carcinogenesis of oral squamous cell carcinoma (OSCC), which is associated with poor survival if not diagnosed early. It is urgent to develop a novel non-invasive and highly sensitive risk assessment and diagnostic method to screen OSCC. Here, we explored salivary miRNAs as a screening method for OSCC in a high-risk group of patients, such as smokers. Materials and methods: Saliva was collected from 44 individuals (23 HPV-negative OSCC; 21 controls; an equal number of smokers and non-smokers). Twenty head and neck cancer-related miRNA markers were analyzed by qPCR, using dual-labeled probes (miR-20A, miR-21, miR-27B, miR-29A, miR-29B, miR-29C, miR-31, miR-34a, miR-99a, miR-125a, miR-136, miR-139, miR-155, miR-192, miR-200A, miR-375, miR-425A, miR-451a, miR-504, miR-3928; RNU6 control), and by Welch’s t-test and ROC (receiver operating characteristic) curve; GraphPad Prism 7.0. Results: A panel of 4 miRNA markers (miR-21, miR-136, miR-3928, miR-29B) was found to be significantly overexpressed in the saliva of OSCC versus healthy controls with a diagnostic ability (p<0.05 by Welch’s t-test; AUC (area under the ROC curve): 64-85%, sensitivity: 50-67%, specificity: 32-38%; 95% confidence interval; by ROC curve analysis). “Oncomir” miR-21 levels (miR-21/RNU6) were found to be significantly higher in the saliva of OSCC patients with a smoking history (mean ± SD: 0.17 ± 0.19) versus never-smokers (mean ± SD: 0.0056 ± 0.0075) (p<0.05; by t-test) with a diagnostic ability (AUC: 90%, sensitivity: 68%, specificity: 30%; 95% confidence interval). Conclusions: We provide a novel panel of non-invasive, easy-to-apply, and sensitive biomarkers, for the diagnosis of oral cancer, including miR-21 in individuals with a smoking history, encouraging their validation in a large group of head and neck cancer patients. Citation Format: Dimitra Vageli, Panagiotis G. Doukas, Benjamin L. Judson. A novel saliva miRNA panel of promising diagnostic biomarkers for oral cancer: The association of miR-21 with smoking history [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-047.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".