Epigenetic regulation of cancer self-renewal differs between endocrine tumors.
Bibliographic record
Abstract
e15717 Background: Cancer cells achieve limitless self-renewal capacity mainly through telomerase reactivation. Methylation of a specific region in the h TERT promoter, termed TERT Hypermethylated Oncologic Region (THOR), has been associated with telomerase reactivation, increased telomerase activity and patient outcome in several cancers. Methods: In this study, we assessed the methylation status of THOR using The Cancer Genome Atlas (TCGA) data on cohorts of two endocrine cancers with distinct cell proliferation rates: the highly proliferative pancreatic adenocarcinoma (n = 194 patients) and the more indolent thyroid carcinoma (n = 571 patients). Results: THOR was significantly hypermethylated in malignant cancer when compared to benign adjacent tissue in pancreatic cancer (p < 0.0001), but not in thyroid cancer. In pancreatic cancer, THOR hypermethylation could also distinguish normal tissue from early stage I disease and it associated with worst patient prognosis. Conclusions: These preliminary findings indicate that THOR can discriminate aggressive tumors from non-aggressive ones, and evidenced the diagnostic and prognostic value of THOR in pancreatic 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 | 0.000 |
| 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".