siRNA-Mediated Integrin-Linked Kinase Suppression: Nonspecific Effects of siRNA/Cationic Liposome Complexes Trigger Changes in the Expression of Phosphorylated-AKT and mTOR Independently of ILK Silencing
Bibliographic record
Abstract
Short interfering RNA targeting ILK (ILK siRNA) could be used to treat patients with cancers where constitutive activation of the AKT/PI3K pathway is prominent (e.g., those cancers lack functional PTEN). It is generally believed that siRNA therapeutics will require the use of delivery systems and lipid-based formulations containing cationic lipids (CLs) are a viable option. However, CLs are known to be toxic and exposure to CLs can influence cell survival pathways. This study characterized how CLs combine with ILK siRNA to influence the AKT/PI3K pathway. Using PTEN-negative cell lines (PC3 castration-insensitive prostate cancer cells and U251 glioma cancer cells), the influence of CLs on the downstream consequences of ILK silencing was determined. When comparing nucleofection (an electroporation method that does not require the use of CLs) and CLs as means to deliver ILK siRNA, a 12- to 30-fold increase in siRNA delivery was achieved when using a CL formulation, yet ILK suppression was less efficient. Importantly, time-dependent signaling consequences associated with ILK silencing, including suppression of phosphorylated (serine 473)-AKT and changes in mTOR expression, were observed independently of ILK suppression when the target cells were exposed to cationic lipids following nucleofection-based delivery of ILK siRNA.
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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) | 0.001 | 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 | 0.001 |
| 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".