Cysteinyl leukotriene receptor antagonists induce apoptosis and inhibit proliferation of human glioblastoma cells by downregulating B-cell lymphoma 2 and inducing cell cycle arrest
Bibliographic record
Abstract
Glioblastoma is the most aggressive type of brain cancer with the highest proliferation, invasion, and migration. Montelukast and zafirlukast, 2 widely used leukotriene receptor antagonists (LTRAs) for asthma treatment, inhibited invasion and migration of glioblastoma cell lines. Montelukast induces apoptosis and inhibits cell proliferation of various cancer cells. Herein, apoptotic and antiproliferative effects of montelukast and zafirlukast were investigated in 2 glioblastoma cell lines, A172 and U-87 MG. Both LTRAs induced apoptosis and inhibited cell proliferation of glioblastoma cells in a concentration-dependent manner. Montelukast was more cytotoxic and induced higher levels of apoptosis than zafirlukast in A172 cells, but not in U-87 MG cells. Both drugs decreased expression of B-cell lymphoma 2 (Bcl-2) protein without affecting Bcl-2-associated X (Bax) levels. LTRAs also reduced the phosphorylation of extracellular signal-regulated kinase 1/2 (ERK1/2). In contrast, zafirlukast showed a greater antiproliferative effect than montelukast and induced G0/G1 cell cycle arrest by upregulating p53 and p21 expression. These results suggested the therapeutic potential of LTRAs in glioblastoma.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".