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Record W2762379106 · doi:10.1158/0008-5472.can-17-1397

PACE4 Undergoes an Oncogenic Alternative Splicing Switch in Cancer

2017· article· en· W2762379106 on OpenAlexafffund
Frédéric Couture, Robert Sabbagh, Anna Kwiatkowska, Roxane Desjardins, Simon‐Pierre Guay, Luigi Bouchard, Robert Day

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchProstate Cancer CanadaCanadian Cancer SocietyMovember Foundation
KeywordsCancerAlternative splicingCancer researchRNA splicingMedicineBiologyGeneticsGeneExonRNA

Abstract

fetched live from OpenAlex

Abstract Inhibition of PACE4, a proprotein convertase that is overexpressed in prostate cancer, has been shown to block cancer progression in an androgen-independent manner. However, the basis for its overexpression and its growth-inhibitory effects are mitigated and uncertain. Here, we report that PACE4 pre-mRNA undergoes DNA methylation–sensitive alternative splicing of its terminal exon 3′ untranslated region, generating an oncogenic, C-terminally modified isoform (PACE4-altCT). We found this isoform to be strongly expressed in prostate cancer cells, where it displayed an enhanced autoactivating process and a distinct intracellular routing that prevented its extracellular secretion. Together, these events led to a dramatic increase in processing of the progrowth differentiation factor pro-GDF15 as the first PACE4 substrate to be identified in prostate cancer. We detected robust expression of PACE4-altCT in other cancer types, suggesting that an oncogenic switch for this proenzyme may offer a therapeutic target not only in advanced prostate cancer but perhaps also more broadly in human cancer. Cancer Res; 77(24); 6863–79. ©2017 AACR.

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 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 score0.018
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.121
GPT teacher head0.484
Teacher spread0.363 · 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.

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

Citations39
Published2017
Admission routes2
Has abstractyes

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