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
Bibliographic record
Abstract
Bessi Qorri,1 William Harless,2 Myron R Szewczuk1 1Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada; 2ENCYT Technologies Inc., Membertou, Nova Scotia, CanadaCorrespondence: Myron R SzewczukDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON K7L 3N6, CanadaTel +1 613 533 2457Fax +1 613 533 6796; Email szewczuk@queensu.caBackground: 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".