The paradox of the retinoic acid receptor beta 2 in cancer /
Bibliographic record
Abstract
Introduction. Recent lines of evidence suggest that retinoids1,2 and RARbeta23 may have both beneficial and harmful effects in cancer. Little is known regarding the specifics of RARbeta2 promoter silencing in cancer, or about the role that RARbeta2 plays in RARbeta2-expressing cancer cells. Objectives. To analyze the patterns and heritability of RARbeta2 promoter methylation in cancer and determine whether it is subject to allelic bias; and to analyze the effects of RARbeta2 knockdown on growth and mRNA expression profiles in cancer. Methods. RARbeta2 promoter methylation was analyzed in 20 parental cancer cell lines and 5 subcloned lines using bisulfate genomic sequencing (>150 sequencings); the proportion of methylated alleles was estimated using methylation-sensitive restriction enzyme digestion followed by PCR and single nucleotide polymorphism identification; 18 antisense oligonucleotides against RARbeta2 were tested in various cancer cell lines using RT-PCR, cell counting and annexin V staining; and gene expression profiles were compared following knockdown versus all trans retinoic acid (ATRA) stimulation using cDNA microarray technology (>14,000 genes), SOURCE and GOMINER databases. Results. Hypo- and hypermethylated alleles frequently co-exist in lines in which RARbeta2 is inactivated (5111, 45%); divergent methylation is heritable in the majority of subclones analyzed (618, 75%); and methylation is subject to allelic bias at a ratio of ~2:1 (3l3 CpG sites, 100%). Cellular proliferation is correlated with RARbeta2 expression levels (p=0.0003); the most effective oligo reduces cellular proliferation by up to 80% in cancer cell lines in which RARbeta2 expression has been retained (313, 100%), but has no apparent effects in lines in which it has been lost (313, 100%); reduction in cell growth following oligo treatment is at least partially due to activation of programmed cell death; and over a dozen pro-carcinogenesis genes are downr
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".