MétaCan
Menu
Back to cohort
Record W2130995548 · doi:10.4161/epi.5.8.13276

Strategies to re-express epigenetically silenced p15<i><sup>INK4b</sup></i>and p21<i><sup>WAF1</sup></i>genes in acute myeloid leukemia

2010· review· en· W2130995548 on OpenAlexaff
C. Ronald Geyer

Bibliographic record

VenueEpigenetics · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEpigeneticsBiologyDNA methylationHistoneGene silencingCancer epigeneticsMethyltransferaseGeneticsMyeloid leukemiaHistone methyltransferaseCancer researchGeneMethylationGene expression

Abstract

fetched live from OpenAlex

p15(INK4B) and p21(WAF1) are TGF-β targets that are silenced in leukemia by epigenetic mechanisms involving DNA methylation and/or histone modifications. Mechanisms for establishing and maintaining epigenetic silencing of p15(INK4B) and p21(WAF1) are not well established. The reversible nature of epigenetic modifications has lead to the development of drugs that target DNA methyltransferases, histone deacetylases, and histone methyltransferases, which have been used to re-express aberrantly silenced genes in leukemia. Recently, non-coding RNA, referred to as natural antisense transcripts (NATs), have been implicated in the regulation of epigenetic modifications. Here, we review epigenetic mechanisms for silencing p15(INK4B) and p21(WAF1) and the role of NATs in this process. We also review epigenetic drugs and drug combinations used to re-express p15(INK4B) and p21(WAF1). Lastly, we discuss the potential use of NATs to target the activity of epigenetic drugs to specific genes and to permanently re-express epigenetically silenced genes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.306
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEpigeneticsSame topicRNA modifications and cancerFrench-language works237,207