Genome‐wide screening of aberrant DNA methylation which associated with gene expression in mouse skin cancers
Bibliographic record
Abstract
Epigenetic alteration of genomic DNA is a common and key process in carcinogenesis. There is considerable evidence indicating that some of the somatic alterations occurring during carcinogenesis in humans also involve the same processes as those observed in mice. Therefore, we analyzed mouse skin cancer tissues induced by the 2-stage carcinogenesis model to identify skin tumor-specific differentially methylated regions (ST-DMRs) during the multistep carcinogenesis process. We have previously identified ST-DMRs using the restriction landmark genomic scanning (RLGS) technique and reported that some of the mouse ST-DMRs were also epigenetically modified in human cancers, such as melanoma, neuroblastoma, and brain tumor. These results encouraged us to pursue global methylation screening in mouse skin carcinogenesis. Using the methylated DNA immunoprecipitation (MeDIP) method combined with the NimbleGen promoter plus CpG island (CpGi) array, we identified 615 ST-DMRs. In combination with global gene expression analysis, 91 of these ST-DMRs were shown to be located on or around the genes differentially expressed between normal skin and tumor tissues, including a candidate human tumor suppressor gene Tfap2e. As observed in human colorectal cancers, Tfap2e was methylated at a CpGi located in intron 3 and downregulated in skin tumors. Our results identified aberrant methylated regions that were associated with gene expression regulation during carcinogenesis, which may indicate critical genetic regions also involved in human carcinogenesis. © 2013 Wiley Periodicals, Inc.
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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 | 0.001 | 0.001 |
| 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".