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Record W4416275831 · doi:10.1002/cnr2.70387

Nicotinic Acetylcholine Receptor Pathways in Cancer: From Psychiatric Clues to Therapeutic Opportunities

2025· article· en· W4416275831 on OpenAlexaff
M Azadi, Pouya Pazooki, Soheila Ajdary, Hamed Shafaroodi

Bibliographic record

VenueCancer Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNicotinic agonistAcetylcholine receptorNicotinic acetylcholine receptorSignal transductionAcetylcholineReceptorSignalling

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of cancer poses significant challenges to treatment, largely because of drug resistance along with other side effects. Current studies have been investigating the growth factors more than other biologic tumor features, such as neurobiologic features. Here in this review, we highlight the role of nicotinic acetylcholine receptors (nAChRs) in cancer development with their context-dependent activation and downstream effectors. RECENT FINDINGS: Some nAChR subtypes stimulate tumorigenic pathways, EGFR/ERK1/2, PI3K/AKT, and MAPK, with varying responses based on the receptor subtype and tissue type. Notably, the Src kinase and MAPK pathways are common downstream effectors in lung, breast, and prostate cancers despite the variations in the predominant nAChR subunits in each cancer: α7 in lung, α9 in breast, and likely α5 and α7 in prostate tumors. CONCLUSION: These findings underscore the importance of targeting nAChRs in a context-specific manner to modulate shared signaling pathways, particularly the acetylcholine-stimulated Src/MAPK pathway. This review also calls for more investigation on other neurotransmitters and potential common pathways, as implicated by psychological reports, to advance the understanding of cancer biology and therapies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score1.000

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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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.

Study designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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