Pro-Inflammatory Cytokines Transactivate Glycosylated Cytokine Receptors on Cancer Cells to Induce Epithelial–Mesenchymal Transition to the Metastatic Phenotype
Bibliographic record
Abstract
Background/Objectives: The significance of cytokine signaling on cancer progression and metastasis has raised interest in cancer research over the last few decades. Here, we analyzed the effects of three cytokines that we previously reported are significantly upregulated rapidly after the surgical removal of primary breast, colorectal, and prostate cancer. We also investigated the regulation of their cognate receptors. Methods: All experiments were conducted using the PANC-1, SW620, and MCF-7 cell lines, treated with three different cytokines (TGF-β1, HGF, and IL-6). The effect of these cytokines on the expression of epithelial–mesenchymal transition (EMT) cell surface markers and neuraminidase-1 activity was measured via fluorescent microscopy and image analysis software. Results: The findings show that these cytokines increase the expression of mesenchymal markers while reducing epithelial markers, corresponding to the EMT process. A strong link between cytokine receptor signaling and the Neu-1-MMP-9-GPCR crosstalk was identified, suggesting that cytokine receptor binding leads to increased Neu-1 activity and subsequent signaling pathway activation. Oseltamivir phosphate (OP) prevented sialic acid hydrolysis by neuraminidase-1 (Neu-1), leading to the downregulation of these signaling cascades. Conclusions: In concert with the previous work revealing the role of Neu-1 in regulating other glycosylated receptors implicated in cancer cell proliferation and EMT, targeting Neu-1 may provide effective treatment against a variety of malignancies. Most significantly, the treatment of patients with specific inhibitors of Neu-1 soon after primary cancer surgery may improve our ability to cure early-stage cancer by inhibiting the EMT process and disrupting the ability of any residual cancer cell population to metastasize.
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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 | 0.000 |
| 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".