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Record W2184232482 · doi:10.4103/1008-682x.127825

The role of mRNA splicing in prostate cancer

2014· review· en· W2184232482 on OpenAlexafffund
AnnaV Lapuk, StanislavV Volik, Yuzhuo Wang, ColinC Collins

Bibliographic record

VenueAsian Journal of Andrology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsBC Cancer AgencyPrevention of Organ FailureUniversity of British Columbia
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsAlternative splicingRNA splicingProstate cancerBiologyCancer researchMetastasisPI3K/AKT/mTOR pathwayAndrogen receptorCancermicroRNABioinformaticsGeneComputational biologySignal transductionGene isoformGeneticsRNA

Abstract

fetched live from OpenAlex

Alternative splicing (AS) is a crucial step in gene expression. It is subject to intricate regulation, and its deregulation in cancer can lead to a wide array of neoplastic phenotypes. A large body of evidence implicates splice isoforms in most if not all hallmarks of cancer, including growth, apoptosis, invasion and metastasis, angiogenesis, and metabolism. AS has important clinical implications since it can be manipulated therapeutically to treat cancer and represents a mechanism of resistance to therapy. In prostate cancer (PCa) AS also plays a prominent role and this review will summarize the current knowledge of alternatively spliced genes with important functional consequences. We will highlight accumulating evidence on AS of the components of the two critical pathways in PCa: androgen receptor (AR) and phosphoinositide 3-kinase (PI3K). These observations together with data on dysregulation of splice factors in PCa suggest that AR and PI3K pathways may be interconnected with previously unappreciated splicing regulatory networks. In addition, we will discuss several lines of evidence implicating splicing regulation in the development of the castration resistance.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.320
Teacher spread0.310 · 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 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

Citations23
Published2014
Admission routes2
Has abstractyes

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