TERT promoter alterations could provide a solution for Peto’s paradox in rodents
Bibliographic record
Abstract
Cancer is a genetic disease caused by changes in gene expression resulting from somatic mutations and epigenetic changes. Although the probability of mutations is proportional with cell number and replication cycles, large bodied species do not develop cancer more frequently than smaller ones. This notion is known as Peto's paradox, and assumes stronger tumor suppression in larger animals. One of the possible tumor suppressor mechanisms involved could be replicative senescence caused by telomere shortening in the absence of telomerase activity. We analysed telomerase promoter activity and transcription factor binding in mammals to identify the key element of telomerase gene inactivation. We found that the GABPA transcription factor plays a key role in TERT regulation in somatic cells of small rodents, but its binding site is absent in larger beavers. Protein binding and reporter gene assays verify different use of this site in different species. The presence or absence of the GABPA TF site in TERT promoters of rodents correlates with TERT promoter activity; thus it could determine whether replicative senescence plays a tumor suppressor role in these species, which could be in direct relation with body mass. The GABPA TF binding sites that contribute to TERT activity in somatic cells of rodents are analogous to those mutated in human tumors, which activate telomerase by a non-ALT mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".