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Research Advances in the Use of Histone Deacetylase Inhibitors for Epigenetic Targeting of Cancer

2019· review· en· W2912582226 on OpenAlexaff
Yu Bai, Daid Ahmad, Ting Wang, Guihua Cui, Wenliang Li

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsUniversity of Waterloo
FundersScientific Research Funds of Jilin Province of Health and Family Planning CommissionScience and Technology Innovative Research Team in Higher Educational Institutions of Hunan ProvinceDepartment of Science and Technology of Jilin ProvinceEducation Department of Jilin Province
KeywordsEpigeneticsHistone deacetylaseHistoneEpigenetic therapyCancerCancer epigeneticsChromatin remodelingCancer researchBioinformaticsChromatinVorinostatMedicineComputational biologyBiologyDNA methylationGene expressionGeneGeneticsHistone methyltransferase

Abstract

fetched live from OpenAlex

The causes and progression of cancer are controlled by epigenetic processes. The mechanisms involved in epigenetic regulation of cancer development, gene expression, and signaling pathways have been studied. Histone deacetylases (HDACs) have a major impact on chromatin remodeling and epigenetics, making their inhibitors a very interesting area of cancer research. This review comprehensively summarizes the literature regarding HDAC inhibitors (HDACis) as an anticancer treatment published in the past few years. In addition, we explain the mechanisms of their therapeutic effects on cancer. An analysis of the beneficial characteristics and drawbacks of HDACis also is presented, which will assist preclinical and clinical researchers in the design of future experiments to improve the therapeutic efficacy of these drugs and circumvent the challenges in the path of successful epigenetic therapy. Future therapeutic strategies may include a combination of HDACis and chemotherapy or other inhibitors to target multiple oncogenic signaling pathways.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.514
Teacher spread0.335 · 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; a candidate call from one teacher head, not a consensus.

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

Citations20
Published2019
Admission routes1
Has abstractyes

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