Simvastatin inhibits the expression of stemness-related genes and the metastatic invasion of human cancer cells via destruction of the cytoskeleton
Bibliographic record
Abstract
Statins are a class of drugs that inhibit the rate-limiting steps in the cholesterol biosynthesis pathway. They act by inhibiting 3-hydroxy-3-methylglutaryl CoA (HMG-CoA) reductase, which catalyzes the conversion of HMG-CoA to mevalonate. Blocking of mevalonate synthesis leads to inhibition of the farnesylation and geranylgeranylation of several functional proteins, such as RhoA and other small guanosine triphosphate-binding proteins, that are important in maintaining the undifferentiated status of the cells. In the present study, we hypothesized that simvastatin, likely through the inhibition of farnesylation and geranylgeranylation of Rac1, Cd42 and RhoA, induces a destruction/restructuration of the cytoskeleton that decreases mechanical strain transfer to the nuclei, inducing the loss of transmission of regulatory signals from the cytoskeleton to the nucleoskeleton. Although this remains at present a hypothesis and is not easy to define if the de-structuration of the cytoskeleton is a secondary effect of simvastatin treatment or the inhibition of post-translational protein modification have a precise role in the structuration of actin cytoskeleton, we speculate that these signal variations could inhibit the expression of certain stemness genes, which could therefore be considered nucleoskeleton-associated and mechanically regulated genes. On the other hand, the restructuration of the cytoskeleton inhibits the formation of lamellipodia and filopodia, which likely decreases the capability of cancer cells to invade the extracellular matrix, thereby modulating the equilibrium between proliferation, differentiation and metastatic invasion in human cancer cells. On the basis of our results we think that simvastatin, alone or in combination with conventional drugs, may have a possible role in cancer therapy.
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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".