DNA hypermethylation within TERT promoter upregulates TERT expression in cancer
Bibliographic record
Abstract
Replicative immortality is a hallmark of cancer cells governed by telomere maintenance. Approximately 90% of human cancers maintain their telomeres by activating telomerase, driven by the transcriptional upregulation of telomerase reverse transcriptase (TERT). Although TERT promoter mutations (TPMs) are a major cancer-associated genetic mechanism of TERT upregulation, many cancers exhibit TERT upregulation without TPMs. In this study, we describe the TERT hypermethylated oncological region (THOR), a 433-bp genomic region encompassing 52 CpG sites located immediately upstream of the TERT core promoter, as a cancer-associated epigenetic mechanism of TERT upregulation. Unmethylated THOR repressed TERT promoter activity regardless of TPM status, and hypermethylation of THOR counteracted this repressive function. THOR methylation analysis in 1,352 human tumors revealed frequent (>45%) cancer-associated DNA hypermethylation in 9 of 11 (82%) tumor types screened. Additionally, THOR hypermethylation, either independently or along with TPMs, accounted for how approximately 90% of human cancers can aberrantly activate telomerase. Thus, we propose that THOR hypermethylation is a prevalent telomerase-activating mechanism in cancer that can act independently of or in conjunction with TPMs, further supporting the utility of THOR hypermethylation as a prognostic biomarker.
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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 | 0.000 |
| 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) | 0.001 | 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".