Allelic methylation bias of the <i>RARB2</i> tumor suppressor gene promoter in cancer
Bibliographic record
Abstract
Retinoic acid receptor B2 (RARB2) is frequently inactivated in cancer. Methylation in the 5'-untranslated region and first exon is known to play a role; however, few studies have analyzed the detailed methylation pattern of the promoter region. We show that hypo- and hypermethylated alleles coexist in 5/11 cell lines in which RARB2 is inactivated. We present evidence supporting the mitotic transmission of these divergent methylation patterns and find a correlation between methylation divergence and heterozygosity at the 3p24 loci, suggesting an allelic methylation bias in these lines. Using a newly devised strategy based on allelic identification via methylation-sensitive restriction enzyme digestion combined with the use of a single nucleotide polymorphism, rs755661, we demonstrate that such a bias exists in three cancer cell specimens heterozygous at rs755661 and therefore amenable to this study. This previously unreported phenomenon of allelic methylation bias suggests that a promoter methylation-independent mechanism may be responsible for inactivation at the hypomethylated allele and this inactivation is reminiscent of an aberrant form of de novo imprinting. Approaches to interpreting methylation data should incorporate the notion of allelic methylation bias.
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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 | 0.001 |
| 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) | 0.001 | 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".