DNA Methylation Of Repeated Elements And Cancer-related Genes In Normal Human Liver, and in Cancer and Non-cancer Liver Cell Lines, Treated With 5adc Or PCB126
Bibliographic record
Abstract
DNA methylation of repeated elements and cancer-related genes in normal human liver, and in cancer and non-cancer liver cell lines, treated with 5adC or PCB126 ABSTRACT DNA methylation is an important epigenetic mechanism contributing to the regulation of gene expression and to the stability of DNA repeated sequences.Abnormal DNA methylation is a characteristic of cancer cells and there are scientific concerns that exposure to environmental contaminants (EC) might contribute to carcinogenesis by altering DNA methylation mechanisms.This study compared DNA methylation of repeated elements and cancerrelated genes in normal human liver, and in cancer (HepG2) and non-cancer (HC-04) cell lines, and investigated the effects of 5-aza-2'-deoxycytidine (5adC; a demethylation control) and polychlorinated biphenyl-126 (PCB126), a non-genotoxic rodent hepatocarcinogen.The results revealed striking celltype differences in DNA methylation of repeated elements (AluYb8, LINE-1, Sat-alpha) and 7/9 cancerrelated genes (CCND2, DAB2IP, DLEC1, GSTp1, OPCML, RASSF1, RUNX3, but not SFRP2 and SOCS1).In 72-h dose-response experiments, 5adC induced "U" shape demethylation responses in the nine investigated genes, but associated with elevated mRNA expression only in 6/9 and 3/9 genes in HC-04 and HepG2 cells, respectively.DNA methylation was resistant to PCB126 in most genes in both cell lines, except for reduced promoter methylation and increased expression of GSTp1 in HepG2 cells, which further support a role for oxidative stress in PCB126induced oncogenesis/toxicity. Finally, the different DNA methylation patterns and gene expression responses between the non-cancer HC-04 and cancer HepG2 cell lines provide alternative models to further explore epigenetic divergence in gene expression regulation.
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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) | 0.002 | 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".