Targeting Cell Cycle Proteins in Breast Cancer Cells with siRNA by Using Lipid-Substituted Polyethylenimines
Bibliographic record
Abstract
The cell cycle proteins are key regulators of cell cycle progression whose deregulation is one of the causes of breast cancer. RNA interference (RNAi) is an endogenous mechanism to regulate gene expression and it could serve as the basis of regulating aberrant proteins including cell cycle proteins. Since the delivery of small interfering RNA (siRNA) is a main barrier for implementation of RNAi therapy, we explored the potential of a non-viral delivery system, 2.0 kDa polyethylenimines substituted with linoleic acid and caprylic acid, for this purpose. Using a library of siRNAs against cell cycle proteins, we identified cell division cycle protein 20 (CDC20), a recombinase RAD51, and serine-threonine protein kinase CHEK1 as effective targets for breast cancer therapy, and demonstrated their therapeutic potential in breast cancer MDA-MB-435, MDA-MB-231, and MCF7 cells with respect to another well-studied cell cycle protein, kinesin spindle protein. We also explored the efficacy of dicer-substrate siRNA (DsiRNA) against CDC20, RAD51, and CHEK1, where a particular DsiRNA against CDC20 showed an exceptionally high inhibition of cell growth in vitro. There was no apparent effect of silencing selected cell cycle proteins on the potency of the chemotherapy drug doxorubicin. The efficacy of DsiRNA against CDC20 was subsequently assessed in a xenograft model, which indicated a reduced tumor growth as a result of CDC20 DsiRNA therapy. The presented study highlighted specific cell cycle protein targets critical for breast cancer therapy, and provided a polymeric delivery system for their effective down-regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".