Prostate Epithelium-Specific Deletion of the Selenocysteine tRNA Gene Trsp Leads to Early Onset Intraepithelial Neoplasia
Bibliographic record
Abstract
Although various lines of evidence suggest that oxidative stress plays a role in human prostate cancer initiation and progression, there is a paucity of direct evidence for its role in tumor initiation. To begin to address this issue, we developed a novel tumorigenesis model by reducing the expression of multiple selenoproteins (SPs) in mouse prostatic epithelium. This was accomplished via the prostate-specific deletion of Trsp, a gene that encodes a transfer RNA (Sec tRNA) required for the insertion of selenocysteine residues into SPs during their translation. By 6 weeks of age, Trsp-deficient mice exhibited widespread prostatic intraepithelial neoplasia lesions in all prostatic lobes, which then progressed to high-grade dysplasia and microinvasive carcinoma by 24 weeks. In contrast to other murine prostate cancer models, Trsp-deficient mice required neither the deletion of a tumor suppressor nor the transgenic introduction of an oncogene for prostatic intraepithelial neoplasia lesion development. In keeping with the antioxidant functions of several SPs, we found increases in lipid peroxidation markers in Trsp-deficient epithelial cells. This novel model of prostate neoplasia provides evidence for the existence of a selenoprotein or selenoproteins capable of acting as a tumor suppressor in the murine prostate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".