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Record W2129146027 · doi:10.1139/o08-001

Opioid-induced regulation of µ-opioid receptor gene expression in the MCF-7 breast cancer cell line

2008· article· en· W2129146027 on OpenAlexvenueno aff
Katarzyna Gach, Mariola Piestrzeniewicz, Jakub Fichna, Barbara Stefañska, Janusz Szemraj, Anna Janecka

Bibliographic record

VenueBiochemistry and Cell Biology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsnot available
FundersFundacja na rzecz Nauki Polskiej
KeywordsOpioid receptorOpioidDownregulation and upregulationChemistry(+)-NaloxoneAgonistMCF-7Opioid antagonistμ-opioid receptorReceptorGene expressionPharmacologyEndocrinologyInternal medicineBiologyCancer cellMedicineGeneBiochemistryCancerHuman breast

Abstract

fetched live from OpenAlex

The aim of the study was to investigate the presence of opioid receptor types in human breast adenocarcinoma MCF-7 cells and to characterize the changes in MOR expression induced by opioid agonist and antagonist treatment. We have shown that all three types of opioid receptors, but predominantly MOR, are expressed in MCF-7 cells. Selective MOR agonists, morphine, endomorphin-1, and endomorphin-2 downregulated MOR mRNA levels in a concentration- and time-dependent manner, but the effect produced by endomorphins was much stronger. Downregulation was blocked by the opioid antagonist naloxone. Naloxone alone produced a slight increase in MOR gene expression. Immunoblotting with antiserum against MOR-1 confirmed these results at the protein level. The results of our study indicate that, in MCF-7 cells, MOR gene expression is downregulated by opioid agonists and upregulated by opioid antagonists. We propose that the opioid-induced regulation of MOR mRNA expression is mediated by reduced binding of the transcription factors NFkappaB and AP-1 to the promoter region on the MOR gene.

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 score0.003
Threshold uncertainty score0.334

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 head0.023
GPT teacher head0.245
Teacher spread0.221 · 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

Citations35
Published2008
Admission routes1
Has abstractyes

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