Cancer‐Targeting Peptides Functionalized With Polyarginine Enables GRP78‐Dependent Cell Uptake and siRNA Delivery Within the DU145 Prostate Cancer Cells
Bibliographic record
Abstract
This study investigated a peptide-based GRP78-targeting strategy for short-interfering (si) RNA delivery in cancer cells. Synthetic fluorescein-labeled amphiphilic peptides composed of the hydrophobic cell surface (cs) GRP78-targeting and hydrophilic, polycationic arginine-rich cell penetrating peptides demonstrated GRP78-dependent cell uptake in the DU145 prostate cancer cells, and to a lesser extent in the non-cancerous human lung fibroblast WI-38 cell line. Mechanistic studies revealed energy-dependent GRP78 receptor-mediated endocytosis of the GRP78-targeting peptide with polyarginine (W1-R9). The cytosolic accumulation of this peptide underscored its potential utility in siRNA delivery. Peptide:siRNA complexes formed stably condensed nanoparticles, with calcium functioning as an ionic stabilizer and additive promoting endosomal siRNA escape for RNA interference (RNAi) activity. Preliminary peptide-based siRNA transfections in the DU145 cells demonstrated that GRP78 knockdown led to an interplay in between pro-survival and cell death outcomes under ER stress induction. Thus, the GRP78-targeting polyarginine peptides enables efficient cell uptake for specific siRNA delivery in the DU145 cells. This class of bio-active synthetic peptides is important for the investigation of cancer biology, leading to the innovation of cancer-targeted gene delivery and therapy approaches.
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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) | 0.000 | 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 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".