Methylation Status of the Telomerase Reverse Transcriptase Promoter in Parotid Tumours and Adjacent Parotid Gland Tissue: A Pilot Study on the Implications for Recurrence and Development of Malignancy
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The methylation of the hypermethylated oncological region (THOR) of human telomerase reverse transcriptase (hTERT) may forecast tumour aggressiveness. This pilot study aimed to evaluate THOR methylation as a potential biomarker for recurrence/malignant transformation in salivary gland pleomorphic adenomas (PA). METHODS: THOR methylation was assessed by quantitative pyrosequencing in 96 parotid tissue samples (benign and malignant), including non-neoplastic parotid tissue, PA, recurrent PA (rPA), and carcinomas, along with their adjacent tissues. TERT promoter mutations (TPMs) were analysed by Sanger sequencing. RESULTS: THOR methylation significantly differed across the seven groups. Malignant tissues showed higher THOR methylation than non-neoplastic tissues, whereas benign tumours showed no significant difference from non-neoplastic tissue. THOR methylation in rPA was closer to carcinoma than to normal tissue, similar in rPA and tissues adjacent to rPA, and higher in tissues adjacent to carcinomas than in non-neoplastic tissues. A subset of PA-adjacent tissues showed epigenetic alterations, suggesting an increased risk of recurrence or malignant transformation (5-15%). No TPMs were detected. CONCLUSIONS: THOR methylation may add information to differentiate normal from carcinogenic tissues and, as such, may be included in a biomarkers panel. Epigenetic alterations in PA-adjacent tissues with normal histology highlight the need for improved diagnostic markers.
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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".