Fingolimod Inhibits <scp>C6</scp> Rat Glioma Proliferation and Migration, Induces Sub‐<scp>G1</scp> Cell Cycle Arrest, Mitochondrial and Extrinsic Apoptosis In Vitro and Reduces Tumour Growth In Vivo
Bibliographic record
Abstract
Glioblastoma multiforme (GBM), the most prevalent brain tumour, is universally fatal. GBM cells exhibit cell cycle disruption and treatment resistance, remarking an urgent need for newer treatments. Fingolimod, a sphingosine-1-phosphate receptor modulator, has been reported to have anti-cancer effects. This study investigated the therapeutic potentials of fingolimod in rat C6 cells and pursued the involved mechanism(s). Cell survival, proliferation, migration, and morphology of fingolimod-treated C6 cells were evaluated using MTT, soft-agar colony formation, wound-healing, and Giemsa staining assays. Apoptosis was investigated through acridine orange/ethidium bromide (AO/EB) and annexin V staining, and flow cytometry analysed the cell cycle. Quantitative reverse transcription PCR and western blotting were used to evaluate gene and protein expressions. An intracranial C6 rat model validated the anti-tumour effect of fingolimod. Fingolimod significantly reduced the survival and colonies of the C6 cells and delayed their gap closure. Cell shrinkage coupled with AO/EB and PI staining of the fingolimod-treated cells indicated apoptosis, subsequently confirmed by measuring the expression levels of the candidate genes involved in apoptosis and cell cycle, such as Bax/Bcl2, P53, Cytochrome C and Caspases 9/3, Fas, Fadd, Tnfrsf1a, Cdkn1a, and Ccnd1, at RNA and protein levels, indicating both extrinsic and mitochondrial apoptosis and cell cycle arrest at sub-G1 phase in fingolimod-treated cells. Furthermore, treating rats bearing intracranial C6 tumours with fingolimod led to significant suppression of intracranial tumour growth. Based on our findings, cell cycle arrest and apoptosis contribute to fingolimod antitumor effects.
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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) | 0.001 | 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) | 0.001 | 0.001 |
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.
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".