Generation of a mouse model for studying the role of upregulated RTEL1 activity in tumorigenesis
Bibliographic record
Abstract
Regulator of telomere length 1 (RTEL1) is a DNA helicase protein that has been demonstrated to be required for the maintenance of telomere length and genomic stability. It has also been found to be essential for DNA homologous recombination during DNA repairing. Human RTEL1 genomic locus (20q13.3) is frequently amplified in multiple types of human cancers, including hepatocellular carcinoma and gastrointestinal tract tumors, indicating that upregulated RTEL1 activity could be important for tumorigenesis. In this study, we have developed a conditional transgenic mouse model that overexpress mouse Rtel1 in a Cre-excision manner. By crossing with a ubiquitous Cre mouse line, we further demonstrated that these established Rtel1 conditional transgenic mice allow to efficiently and highly express a functional Rtel1 that is able to rescue the embryonic defects of Rtel1 null mouse allele. Furthermore, we demonstrated that more than 70% transgenic mice that widely overexpress Rtel1 developed liver tumors that recapitulate many malignant features of human hepatocellular carcinoma (HCC). Our work not only generated a valuable mouse model for determining the role of RTEL1 in the development of cancers, but also provided the first genetic evidence to support that amplification of RTEL1, as observed in several types of human cancers, is tumorigenic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".