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Record W2241247270 · doi:10.1093/carcin/bgv148

The retinoic acid derivative, ABPN, inhibits pancreatic cancer through induction of Nrdp1

2015· article· en· W2241247270 on OpenAlexaff
Sanguine Byun, Seung Ho Shin, Eunjung Lee, Jihoon Lee, Sung‐Young Lee, Lee Farrand, Sung Keun Jung, Yong‐Yeon Cho, Soo‐Jong Um, Hong‐Sig Sin, Youn‐Ja Kwon, Chengjuan Zhang, Benjamin K. Tsang, Ann M. Bode, Hyong Joo Lee, Ki Won Lee, Zigang Dong

Bibliographic record

VenueCarcinogenesis · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Cancer Institute
KeywordsRetinoic acidMedicineCancer researchPancreatic cancerTretinoinInternal medicineOncologyCancerPharmacologyChemistryBiochemistryGene

Abstract

fetched live from OpenAlex

Combination chemotherapy for the treatment of pancreatic cancer commonly employs gemcitabine with an EGFR inhibitor such as erlotinib. Here, we show that the retinoic acid derivative, ABPN, exhibits more potent anticancer effects than erlotinib, while exhibiting less toxicity toward noncancerous human control cells. Low micromolar concentrations of ABPN induced apoptosis in BxPC3 and HPAC pancreatic cancer cell lines, concomitant with a reduction in phosphorylated EGFR as well as decreased ErbB3, Met and BRUCE protein levels. The degradation of ErbB3 is a result of proteasomal degradation, possibly due to the ABPN-dependent upregulation of Nrdp1. Administration of ABPN showed significant reductions in tumor size when tested using a mouse xenograft model, with higher potency than erlotinib at the same concentration. Analysis of the tumors demonstrated that ABPN treatment suppressed ErbB3 and Met and induced Nrdp1 in vivo. The data suggest that ABPN may be more suitable in combination chemotherapy with gemcitabine than the more widely used EGFR inhibitor, erlotinib.

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)0.000
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 score0.020
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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