Lipoprotein‐Like Nanoparticle Carrying Small Interfering RNA Against Spalt‐Like Transcription Factor 4 Effectively Targets Hepatocellular Carcinoma Cells and Decreases Tumor Burden
Bibliographic record
Abstract
Patients with advanced hepatocellular carcinoma (HCC) are often unable to tolerate chemotherapy due to liver dysfunction in the setting of cirrhosis. We investigate high‐density lipoprotein (HDL)‐mimicking peptide phospholipid scaffold (HPPS), which are nanoparticles that capitalize on normal lipoprotein metabolism and transport, as a solution for directed delivery of small interfering RNA (siRNA) cargo into HCC cells. Spalt‐like transcription factor 4 (SALL4), a fetal oncoprotein expressed in aggressive HCCs, is specifically targeted as a case study to evaluate the efficacy of HPPS carrying siRNA cargo. HPPS containing different formulations of siRNA therapy against SALL4 were generated specifically for HCC cells. These were investigated both in vitro and in vivo using fluorescence imaging. HPPS‐SALL4 effectively bound to scavenger receptor, class B type 1 (SR‐BI) and delivered the siRNA cargo into HCC cells, as seen in vitro. HPPS‐SALL4 effectively inhibited HCC tumor growth (P < 0.05) and induced a 3‐fold increase in apoptosis of the cancer cells in vivo compared to HPPS‐scramble. Additionally, there was no immunogenicity associated with HPPS‐SALL4 as measured by cytokine production. Conclusion: We have developed unique HDL‐like nanoparticles that directly deliver RNA interference (RNAi) therapy against SALL4 into the cytosol of HCC cells, effectively inhibiting HCC tumor growth without any systemic immunogenicity. This therapeutic modality avoids the need for hepatic metabolism in this cancer, which develops in the setting of cirrhosis and liver dysfunction. These natural lipoprotein‐like nanoparticles with RNAi therapy are a promising therapeutic strategy for HCC.
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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".