Epigenetic aberrations of gene expression in a rat model of hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is mostly triggered by environmental and life-style factors and may involve epigenetic aberrations. However, a comprehensive documentation of the link between the dysregulated epigenome, transcriptome, and liver carcinogenesis is lacking. In the present study, Fischer-344 rats were fed a choline-deficient (CDAA, cancer group) or choline-sufficient (CSAA, healthy group) L-amino acid-defined diet. At the end of 52 weeks, transcriptomic alterations in livers of rats with HCC tumours and healthy livers were investigated by RNA sequencing. DNA methylation and gene expression were assessed by pyrosequencing and quantitative reverse-transcription PCR (qRT-PCR), respectively. We discovered 1,848 genes that were significantly differentially expressed in livers of rats with HCC tumours (CDAA) as compared with healthy livers (CSAA). Upregulated genes in the CDAA group were associated with cancer-related functions, whereas macronutrient metabolic processes were enriched by downregulated genes. Changes of highest magnitude were detected in numerous upregulated genes that govern key oncogenic signalling pathways, including Notch, Wnt, Hedgehog, and extracellular matrix degradation. We further detected perturbations in DNA methylating and demethylating enzymes, which was reflected in decreased global DNA methylation and increased global DNA hydroxymethylation. Four selected upregulated candidates, Mmp12, Jag1, Wnt4, and Smo, demonstrated promoter hypomethylation with the most profound decrease in Mmp12. MMP12 was also strongly overexpressed and hypomethylated in human HCC HepG2 cells as compared with primary hepatocytes, which coincided with binding of Ten-eleven translocation 1 (TET1). Our findings provide comprehensive evidence for gene expression changes and dysregulated epigenome in HCC pathogenesis, potentially revealing novel targets for HCC prevention/treatment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".