Absence of Malat1 does not prevent DEN-induced hepatocarcinoma in mice
Bibliographic record
Abstract
Long non-coding RNAs (lncRNA) are key regulators of gene expression both at the transcriptional and post-transcriptional levels. The lncRNA metastasis associated lung adenocarcinoma transcript 1 (Malat1) is overexpressed in many types of cancer, including hepatocarcinoma, and induces cell proliferation in several cell lines in vitro. However, the direct causal effects of Malat1 on hepatocyte proliferation and liver carcinogenesis in vivo are not fully understood. To better determine the contribution of Malat1 to hepatocarcinoma oncogenesis, this study was aimed at testing the hypothesis that its absence confers resistance to the development of liver tumors. Male Malat1-/- mice and their wild-type (WT) littermates were studied one year after treatment with the genotoxic agent diethylnitrosamine (DEN), a potent inducer of liver cancer. As expected, in WT mice, Malat1 expression was significantly higher in hepatic tumors than in healthy liver regions. Altered hepatic mRNA levels of Ki67, HDAC3, NFκB and p27 were observed in DEN-treated Malat1-/- mice. Despite this, these mice were characterized by similar liver weight, prevalence of tumors, and histological features compared to those of their WT littermates. In parallel, plasma lipids and glucose homeostasis did not significantly differ between DEN-treated groups. These findings support a role for Malat1 as a marker of liver carcinogenesis, but also suggest that its role in the regulation of hepatocyte hyperproliferation in mice is either minimal or masked by redundant and/or overwhelming mechanisms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".