The effect of mesenchymal stem cells-derived exosomes on the prostate, bladder, and renal cancer cell lines
Bibliographic record
Abstract
We aimed to explain the role of mesenchymal stem cells (MSC-exosomes) on gene expressions of epithelial to mesenchymal transition (EMT), angiogenesis, and apoptosis. Four different cell lines were employed, including ACHN, 5637, LNCaP, and PC3, as well-known representatives for renal, bladder, hormone-sensitive, and hormone-refractory prostate cancers, respectively. Cell lines were exposed to diverse concentrations of mesenchymal stem cells-derived exosomes to find IC50 values. Percentages of apoptotic cells were evaluated by Annexin/P.I. staining. Micro Culture Tetrazolium Test assessed proliferative inhibitory effect; and prostate biomarker (KLK2), EMT (E-cadherin and Snail), angiogenesis genes (VEGF-A/VEGF-C), apoptosis genes (BAX/BCL2, P53) and Osteopontin variants (OPNa/b, and c) mRNA levels were studied by realtime PCR method. All 5637, LNCaP, and PC3 following treatment with exosomes illustrated specific responses with changes in expression of different genes. The increased TP53 and decreased BCL2 expressions were seen in 5637, LNCaP, and PC3. In PC3, OPNb and OPNc have raised more than P53; in LNCap, the increase was in VEGF-c. In 5637 cells, more than TP53 and BCL2 changes, two other genes, VEGFa and B.A.X., have decreased, suggesting exosomes' anti-apoptotic and anti-angiogenic effects. The kidney tumor cell line saw no significant gene expression change in ten targeted genes. MSC-exosomes therapy has augmented some interesting antitumor effects on prostate, bladder, and kidney cancer cell lines. This effect which originates from exosomes' potency to persuade apoptosis and prevent the proliferation of cancer cells simultaneously, was more substantial in bladder cancer, moderate in prostate cancer, and mild in renal cancer.
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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".