Potential Effect of Simvastatin as an Anti-Cancer Agent on SOX7 and SOX9 Expression in Prostate Cancer Cell Lines
Bibliographic record
Abstract
Background: Prostate cancer (PCa) is the second leading cause of cancer death in men, worldwide. Geranylgeranylation and farnesylation have a main role in the carcinogenic process, which can be prevented via statins as HMGCOA reductase enzyme inhibitors in cholesterol biosynthesis. These effects might be controlled by several transcription factors such as SOX7 and SOX9, which have been involved in PCa initiation and progression. To the best of our knowledge, no study has demonstrated the association of simvastatin and SOX status in PCa. Therefore, this study is an attempt to evaluate whether simvastatin induces anti-neoplastic effects via the SOX9 and SOX7 transcription factors. Methods: Prostate cancer cell lines LNCaP and PC3 were used to evaluate the expression of SOX7 and SOX9 using quantitative RT-PCR. Results: Our data was analyzed by applying one-way ANOVA and Tukey's test and determined that 0.07 µM of simvastatin after 24 h was sufficient to upregulate SOX7 mRNA expression ratio by 3.58 fold in LNCaP. In addition, the level of SOX9 mRNA expression was increased by 12.18 fold at 0.07 µM after 24 h, 8.67 fold at 0.001 µM after 24 h, and 6.33 fold at 0.07 µM after 12 h in LNCaP and in PC3 cell line. The level of SOX9 mRNA expression was increased by 2.64 fold at 0.5 µM after 24 h and 2.78 fold at 0.1 µM after 12 h, however, it decreased by 0.67 fold at 0.1 µM after 24 h. Conclusions: Our findings suggest that simvastatin can induce the anti-cancer properties via manipulating the expression of SOX7 in LNCaP, as the androgen-dependent cell.
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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) | 0.001 | 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".