Dual role of allele-specific DNA hypermethylation within the TERT promoter in cancer
Bibliographic record
Abstract
Aberrant activation of telomerase in human cancer is achieved by various alterations within the TERT promoter, including cancer-specific DNA hypermethylation of the TERT hypermethylated oncological region (THOR). However, the impact of allele-specific DNA methylation within the TERT promoter on gene transcription remains incompletely understood. Using allele-specific next-generation sequencing, we screened a large cohort of normal and tumor tissues (n = 652) from 10 cancer types and identified that differential allelic methylation (DAM) of THOR is restricted to cancerous tissue and commonly observed in major cancer types. THOR-DAM was more common in adult cancers, which develop through multiple stages over time, than in childhood brain tumors. Furthermore, THOR-DAM was especially enriched in tumors harboring the activating TERT promoter mutations (TPMs). Functional studies revealed that allele-specific gene expression of TERT requires hypomethylation of the core promoter, both in TPM and TERT WT cancers. However, the expressing allele with hypomethylated core TERT promoter universally exhibits hypermethylation of THOR, while the nonexpressing alleles are either hypermethylated or hypomethylated throughout the promoter. Together, our findings suggest a dual role for allele-specific DNA methylation within the TERT promoter in the regulation of TERT expression in cancer.
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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".