Long-circulating siRNA nanoparticles for validating Prohibitin1-targeted non-small cell lung cancer treatment
Bibliographic record
Abstract
RNA interference (RNAi) represents a promising strategy for identification and validation of putative therapeutic targets and for treatment of a myriad of important human diseases including cancer. However, the effective systemic in vivo delivery of small interfering RNA (siRNA) to tumors remains a formidable challenge. Using a robust self-assembly strategy, we develop a unique nanoparticle (NP) platform composed of a solid polymer/cationic lipid hybrid core and a lipid-poly(ethylene glycol) (lipid-PEG) shell for systemic siRNA delivery. The new generation lipid-polymer hybrid NPs are small and uniform, and can efficiently encapsulate siRNA and control its sustained release. They exhibit long blood circulation (t1/2 ∼ 8 h), high tumor accumulation, effective gene silencing, and negligible in vivo side effects. With this RNAi NP, we delineate and validate the therapeutic role of Prohibitin1 (PHB1), a target protein that has not been systemically evaluated in vivo due to the lack of specific and effective inhibitors, in treating non-small cell lung cancer (NSCLC) as evidenced by the drastic inhibition of tumor growth upon PHB1 silencing. Human tissue microarray analysis also reveals that high PHB1 tumor expression is associated with poorer overall survival in patients with NSCLC, further suggesting PHB1 as a therapeutic target. We expect this long-circulating RNAi NP platform to be of high interest for validating potential cancer targets in vivo and for the development of new cancer therapies.
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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) | 0.001 | 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".