Single and Combinational siRNA Therapy of Cancer Cells: Probing Changes in Targeted and Nontargeted Mediators after siRNA Treatment
Bibliographic record
Abstract
Cancer cells are known to be heterogeneous and plastic, which imparts innate and acquired abilities to resist molecular targeting by short interfering RNA (siRNA). Not all cancer cells in a population would show a similar responsiveness to targeting of genes critical for their survival and even the responders could quickly transform and switch to alternative mechanism(s) for their survival. This study was designed to look at this phenomenon by analyzing the effect of siRNA silencing of selected protein mRNAs involved in cell survival and proliferation on other protein mRNAs that could contribute to cell survival. We compared the gene expression profile of the initial population after siRNA silencing to the subpopulation that survived the siRNA silencing, to identify potential overexpressions that might explain the cell survival. Our studies show that silencing well-selected protein mRNAs simultaneously could offer advantages compared to individual siRNA silencing due to an additional impact on the expression level of certain protein mRNAs. We also demonstrate that overexpression of certain protein mRNAs could explain the innate unresponsiveness of a subpopulation of cells. These observations could be a stepping stone for further investigation of the possibility of significant synergistic effect for this combinational RNA interference strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".