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Record W1987130968 · doi:10.1158/1538-7445.am2011-5561

Abstract 5561: 20(S)-Protopanaxadiol-aglycone downregulates full-length and ligand-independent splice variants of androgen receptor

2011· article· en· W1987130968 on OpenAlexaff
Bo Cao, Xichun Liu, Jing Li, Zhenggang Xiong, Thomas Wiese, Helen Cheng, Paul S. Rennie, Lijuan Zhao, Haitao Zhang, Yan Dong

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsPrevention of Organ Failure
Fundersnot available
KeywordsAndrogen receptorProstate cancerDihydrotestosteroneAndrogenCancer researchDutasterideLNCaPEndocrinologyProstateFinasterideInternal medicineCancerProtopanaxadiolspliceMedicineBiologyPharmacologyGinsengHormoneBiochemistryGinsenoside

Abstract

fetched live from OpenAlex

Abstract Prostate carcinogenesis is characterized by a long latency of 20 to 40 years. Chemoprevention to manage the disease at an early stage to prevent it from becoming clinical relevant is increasingly being recognized as an important aspect of prostate cancer control. Androgen signaling plays a vital role in the development and progression of prostate cancer. Finasteride and dutasteride, which inhibit the formation of dihydrotestosterone, are the only chemopreventive agents that have been shown definitively to decrease prostate cancer incidence. However, their effectiveness appears to be limited to Gleason 6 cancers. In addition, cancer cells expressing high level of constitutively-active, ligand-independent splice variants of androgen receptor may not be responsive to treatment with finasteride or dutasteride. Therefore, there is an urgent need to develop new chemopreventive agents that could block androgen signaling through both the full-length and splice variants of androgen receptor. Ginsenosides are the main ingredients responsible for the pharmaceutical functions of ginseng, a commonly used medicinal herb among cancer patients. Several ginsenosides have been implicated to inhibit prostate cancer cell growth. However, the underlying mechanism is largely unknown. Here we provide the first evidence that, in prostate cancer cells, ginsenoside 20(S)-protopanaxadiol-aglycone (PPD) effectively downregulates the expression and activity of androgen receptor, including both the full-length and the constitutively-active, ligand-independent splice variants. The effect of PPD on androgen receptor is manifested by an immediate drop in protein, followed by a reduction in mRNA. The initial decrease in androgen receptor protein could be attributed to PPD induction of proteasome-mediated degradation, possibly as a result of disrupted androgen receptor N-C interaction. Depressing the inhibitory effect of PPD on androgen receptor by knocking down androgen receptor before PPD treatment weakens significantly the growth-suppressive activity of PPD, indicating the important contribution of androgen receptor downregulation to PPD action in prostate cancer cells. This report is also the first to establish the in vivo preclinical efficacy of PPD against the growth of androgen receptor-expressing prostate cancer cells, which constitute the majority of clinical prostate carcinomas. In addition to tumor growth inhibition, PPD supplementation also leads to in vivo downregulation of androgen receptor and its target gene, prostate-specific antigen. Considering the critical role of androgen receptor signaling in prostate cancer development and progression and the role of the ligand-independent androgen receptor splice variants in disease recurrence, our findings provide strong justification for further development of PPD for prostate cancer prevention and treatment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5561. doi:10.1158/1538-7445.AM2011-5561

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 score0.038
Threshold uncertainty score0.395

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 studies0.0000.000
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 head0.059
GPT teacher head0.344
Teacher spread0.285 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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