Induction of Apoptosis by Survivin Silencing through siRNA Delivery in a Human Breast Cancer Cell Line
Bibliographic record
Abstract
Post-transcriptional silencing of antiapoptotic genes is a promising strategy for cancer therapy, but delivering short interfering RNA (siRNA) molecules against such targets is challenging due to inability of anionic siRNA to cross cellular membranes. Lipid substitution on small molecular weight, nontoxic polyethylenimine (PEI) has been investigated as a promising approach for effective siRNA delivery. In this study, we report on the ability of low molecular weight, lipid-substituted PEI to deliver siRNA against the antiapoptotic protein survivin. Toxicity of a library of lipid-substituted PEIs, as well as their siRNA delivery and survivin silencing efficiency, was evaluated in MDA-MB-231 human breast cancer cells. A significant increase in cellular delivery of siRNA was observed as a result of lipid substitution. Most significant downregulation of survivin was established by caprylic acid-substituted polymers, which resulted in significant levels of apoptosis induction and resultant loss of cell viability. Survivin downregulation prior to anticancer drug treatment decreased the IC(50) of several drugs by 50- to 120-fold. Our experiments indicated an effective downregulation of survivin, a cell protective protein upregulated in tumor cells, by delivering siRNA with hydrophobically modified PEI. This study introduces a promising delivery system for safe and effective siRNA delivery that will be suitable for further investigation in preclinical animal models.
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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) | 0.001 | 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 | 0.001 |
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".