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Record W3009152633 · doi:10.1139/cjpp-2019-0668

Upregulation of PACE4 in prostate cancer is not dependent on E2F transcription factors

2020· article· en· W3009152633 on OpenAlexafffundvenue
Anita K. Bakrania, Mélanie Aubé, Roxane Desjardins, Robert Sabbagh, Robert Day

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsGene silencingProstate cancerGene isoformTranscription factorCancer researchBiologyTranscription (linguistics)ProstateCancerCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Recent studies in prostate cancer have identified PACE4, a proprotein convertase enzyme, as an emerging therapeutic target. Inhibition of PACE4-altCT, an oncogenic isoform of PACE4, using molecular or pharmacological approaches results in decreased cell proliferation and tumor progression in xenograft models. Although several validations have confirmed PACE4-altCT as a novel therapeutic target, the transcriptional regulation of PACE4 isoforms and mechanism of action remain a challenge. Previously, it has been reported that the human PACE4 promoter possesses potential binding sites for the E2F family of transcription factors, all of which are involved in cell cycle regulation and synthesis of DNA in mammalian cells. Therefore, we attempted to conduct in-depth evaluation of E2Fs on PACE4 and PACE4 isoform expression in prostate cancer. We conducted in vitro molecular silencing studies in various prostate cancer cell lines and determined the change in PACE4 expression levels. The results clearly show that the E2Fs alone do not alter PACE4 expression.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.049
GPT teacher head0.340
Teacher spread0.290 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and Pharmacology→Same topicProstate Cancer Treatment and Research→French-language works237,207→