Epigenetic reprogramming of liver cells in tamoxifen‐induced rat hepatocarcinogenesis
Bibliographic record
Abstract
Tamoxifen, a nonsteroidal anti-estrogen, is a potent genotoxic hepatocarcinogen in rats, with both tumor initiating and promoting properties. Recently it has been demonstrated that genotoxic carcinogens, in addition to exerting genotoxic effects, often cause epigenetic alterations and these induced epigenetic changes may play important mechanistic role in carcinogenesis. In the present study, we investigated the role of tamoxifen-induced epigenetic changes in hepatocarcinogenic process. The results of the study showed that exposure of female F344 rats to tamoxifen resulted in progressive loss of CpG methylation in regulatory sequences of long interspersed nucleotide elements (LINE-1) and prominent increase in expression of LINE-1 elements and c-myc proto-oncogene. The accumulation of tamoxifen-induced DNA lesions was accompanied by the decreased level of Rad51, Ku70, and DNA polymerase beta (Polbeta) proteins that play a crucial role in maintenance of genomic stability. Furthermore, feeding rats with tamoxifen-containing diet led to increased regenerative cell proliferation, as indicated by the increased level of Ki-67 and proliferating cell nuclear antigen (PCNA) proteins. These data indicate that exposure of animals to genotoxic hepatocarcinogen tamoxifen led to early phenotypical alterations in livers characterized by emergence of epigenetically reprogrammed cells with a specific cancer-related epigenetic phenotype prior to tumor formation.
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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".