Characterization of a glycine N‐methyltransferase gene knockout mouse model for hepatocellular carcinoma: Implications of the gender disparity in liver cancer susceptibility
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the fifth common cancer in the world and it mainly occurs in men. Glycine N-methyltransferase (GNMT) participates in one-carbon metabolism and affects DNA methylation by regulating the ratio of S-adenosylmethionine to S-adenosylhomocystine. Previously, we described that the expression of GNMT was diminished in human HCC. Here, we showed that 50% (3/6) male and 100% (7/7) female Gnmt-/- mice developed HCC, and their mean ages of HCC development were 17 and 16.5 months, respectively. In addition, 42.9% (3/7) of female Gnmt-/- mice had hemangioma. Wnt reporter assay demonstrated that Gnmt is a negative regulator for canonical Wnt signaling pathway. Beta-catenin, cyclin D1 and c-Myc, genes related to Wnt pathway, were upregulated in the liver tissues from both 11 weeks and HCC stage of Gnmt-/- mice. Furthermore, global DNA hypomethylation and aberrant expression of DNA methyltransferases 1 and 3b were found in the early and late stages of HCC development. Hierarchical cluster analysis of 6,023 transcripts from microarray data found that gene expression patterns of HCC tumors from male and female Gnmt-/- mice were distinctively different. Real-time PCR confirmed that Gadd45a, Pak1, Mapk3 and Dsup3 genes of mitogen-activated protein kinase (MAPK) pathway were activated in Gnmt-/- mice, especially in the female mice. Therefore, GNMT is a tumor suppressor gene for liver cancer, and it is associated with gender disparity in liver cancer susceptibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".