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Record W2204304636

Identification of a novel fusion transcript between human relaxin-1 (RLN1) and human relaxin-2 (RLN2) in prostate cancer

2016· article· en· W2204304636 on OpenAlexaff
Gregor Tevž, Sean McGrath, Ryan Demeter, Vincent Magrini, Varinder Jeet, Anja Rockstroh, Stephen J. McPherson, John Lai, Nenad Bartoniček, Jiyuan An, Jyotsna Batra, Marcel E. Dinger, Melanie Lehman, Elizabeth D. Williams, Colleen C. Nelson

Bibliographic record

VenueDigital Commons@Becker (Washington University School of Medicine) · 2016
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsUniversity of British Columbia
FundersProstate Cancer Foundation of AustraliaMovember FoundationAustralian GovernmentProstate Cancer Foundation
KeywordsLNCaPProstate cancerFusion geneBiologyGeneFusion transcriptRelaxinGene isoformFusion proteinComputational biologyCancer researchCancerGeneticsRecombinant DNAReceptor
DOInot available

Abstract

fetched live from OpenAlex

Simultaneous expression of highly homologous RLN1 and RLN2 genes in prostate impairs their accurate delineation. We used PacBio SMRT sequencing and RNA-Seq in LNCaP cells in order to dissect the expression of RLN1 and RLN2 variants. We identified a novel fusion transcript comprising the RLN1 and RLN2 genes and found evidence of its expression in the normal and prostate cancer tissues. The RLN1-RLN2 fusion putatively encodes RLN2 isoform with the deleted secretory signal peptide. The identification of the fusion transcript provided information to determine unique RLN1-RLN2 fusion and RLN1 regions. The RLN1-RLN2 fusion was co-expressed with RLN1 in LNCaP cells, but the two gene products were inversely regulated by androgens. We showed that RLN1 is underrepresented in common PCa cell lines in comparison to normal and PCa tissue. The current study brings a highly relevant update to the relaxin field, and will encourage further studies of RLN1 and RLN2 in PCa and broader.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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