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Record W2911619448 · doi:10.1038/s41598-018-36888-x

SP1 and RARα regulate AGAP2 expression in cancer

2019· article· en· W2911619448 on OpenAlexfundno aff
Yegor Doush, Arif Anwer Surani, Amaia Navarro‐Corcuera, Stéphanie McArdle, E. Ellen Billett, Cristina Montiel‐Duarte

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsnot available
FundersAsociación de Amigos de la Universidad de NavarraUniversidad de NavarraTrent UniversityBroad InstituteNottingham Trent University
KeywordsDU145Retinoic acidCancer researchBiologyTranscription factorGene silencingSp1 transcription factorPromoterCancerCancer cellCell biologyMolecular biologyCell cultureGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

AGAP2 (Arf GAP with GTP-binding protein-like domain, Ankyrin repeat and PH domain 2) isoform 2 is considered a proto-oncogene, but not much is known about AGAP2 gene expression regulation. To get some insight into this process, AGAP2 proximal promoter was cloned and characterised using reporter assays. We have identified SP1 as a transcription factor bound to AGAP2 promoter and required for AGAP2 expression in two different types of cancer cells (KU812, a chronic myeloid leukaemia cell line; and DU145, a prostate cancer cell line): silencing SP1 decreased AGAP2 protein levels. We have also found that all-trans retinoic acid (ATRA) treatment increased AGAP2 protein levels in both cell lines whilst curcumin treatment reduced ATRA-mediated AGAP2 increase. Furthermore, chromatin immunoprecipitation studies revealed the presence of RARα, RXRα and the lysine acetyl transferase PCAF in AGAP2 promoter. Our results provide a novel understanding of AGAP2 expression regulation that could be beneficial to those patients with cancers where AGAP2 is overexpressed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations16
Published2019
Admission routes1
Has abstractyes

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