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Record W3092098441 · doi:10.2147/dddt.s264122

<p>Novel Molecular Mechanism of Aspirin and Celecoxib Targeting Mammalian Neuraminidase-1 Impedes Epidermal Growth Factor Receptor Signaling Axis and Induces Apoptosis in Pancreatic Cancer Cells</p>

2020· article· en· W3092098441 on OpenAlexafffund
Bessi Qorri, William Harless, Myron R. Szewczuk

Bibliographic record

VenueDrug Design Development and Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCancer researchEpidermal growth factor receptorApoptosisPancreatic cancerMechanism (biology)Signal transductionEpidermal growth factorGrowth factor receptorChemistryReceptorMedicinePharmacologyInternal medicineCancerCell biologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Background: Aspirin (acetylsalicylic acid) and celecoxib have been used as potential anti-cancer therapies. Aspirin exerts its therapeutic effect in both cyclooxygenase (COX)-dependent and -independent pathways to reduce tumor growth and disable tumorigenesis. Celecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, reduces factors that cause inflammation and pain. The question is whether aspirin and celecoxib have other molecular targets of equal or more therapeutic efficacy with significant anti-cancer preventive benefits. Aim: Here, we propose that aspirin and celecoxib exert their anti-cancer effects by targeting and inhibiting mammalian neuraminidase-1 (Neu-1). Neu-1 has been reported to regulate the activation of several receptor tyrosine kinases (RTKs) and TOLL-like receptors and their downstream signaling pathways. Neu-1 in complex with matrix metalloproteinase-9 (MMP-9) and G protein-coupled receptors (GPCRs) has been reported to be tethered to RTKs at the ectodomain. Materials and Methods: The WST-1 cell viability assay, Caspase 3/7 assay, and Annexin V assay were used to evaluate the cell viability and detect apoptotic and necrotic cells following treatment in MiaPaCa-2, PANC-1 and the gemcitabine-resistant PANC-1 variant (PANC-1 GemR) cells. Microscopic imaging, lectin cytochemistry, and flow cytometry were used to detect levels of α-2,3 sialic acid. Epidermal growth factor (EGF)-stimulated live cell sialidase assays and neuraminidase assays were used to detect Neu-1 activity. Immunocytochemistry was used to detect levels of EGFR and phosphorylated EGFR (pEGFR) following treatment. Results: For the first time, aspirin and celecoxib were shown to significantly inhibit Neu-1 sialidase activity in a dose- and time-dependent manner following stimulation with EGF. Aspirin blocked Neu-1 desialylation of α-2,3-sialic acid expression following 30 min stimulation with EGF. Aspirin and celecoxib significantly and dose-dependently inhibited isolated neuraminidase ( Clostridium perfringens ) activity on fluorogenic substrate 2ʹ-(4-methylumbelliferyl)-α-D-N-acetylneuraminic acid (4-MUNANA). Aspirin inhibited phosphorylation of the EGFR in EGF-stimulated cells. Aspirin dose- and time-dependently induced CellEvent caspase-3/7 + cells as well as apoptosis and necrosis on PANC-1 cells. Conclusion: These findings signify a novel multimodality mechanism(s) of action for aspirin and celecoxib, specifically targeting and inhibiting Neu-1 activity, regulating EGF-induced growth receptor activation and inducing apoptosis and necrosis in a dose- and time-dependent manner. Repurposing aspirin and celecoxib as anti-cancer agents may also upend other critical targets involved in multistage tumorigenesis regulated by mammalian neuraminidase-1. Significance: These findings may be the missing link connecting the anti-cancer efficacy of NSAIDs to the role of glycosylation in inflammation and tumorigenesis. Keywords: neuraminidase-1, inflammation, cancer, multistage tumorigenesis, tumor microenvironment

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.233
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2020
Admission routes2
Has abstractyes

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