STAT3 Silencing in Dendritic Cells by siRNA Polyplexes Encapsulated in PLGA Nanoparticles for the Modulation of Anticancer Immune Response
Bibliographic record
Abstract
In dendritic cells (DCs), the induction of signal transducer and activator of transcription 3 (STAT3) by tumor-derived factors (TDFs) renders DCs tolerogenic and suppresses their antitumor activity. Therefore, silencing STAT3 in DCs is beneficial for cancer immunotherapy. We have shown that STAT3 knockdown in B16 murine melanoma by siRNA polyplexes of polyethylenimine (PEI) or its stearic acid derivative (PEI-StA) induces B16 cell death in vitro and in vivo. Here, we investigated the physical encapsulation of siRNA/PEI and PEI-StA polyplexes in poly(d,l-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) for STAT3 knockdown in DCs. PLGA NPs containing siRNA polyplexes of PEI (PLGA-P) and PEI-StA (PLGA-PS) had an average diameter of ~350 to 390 nm and a zeta potential of ∼-13 to -19 mV, respectively. The encapsulation efficiency (E.E.) of siRNA in PLGA-P and PLGA-PS was 26% and 43%, respectively. In both NP types, siRNA release followed a triphasic pattern, but it was faster in PLGA-PS. Our uptake study by fluorescence microscopy confirmed DC uptake and endosomal localization of both NP types. After exposure to B16.F10 conditioned medium, DCs showed high STAT3 and low CD86 expression indicating impaired function. STAT3 silencing by PLGA-P and PLGA-PS of STAT3 siRNA restored DC maturation and functionality as evidenced by the upregulation of CD86 expression, high secretion of TNF-α and significant allogenic T cell proliferation. Moreover, encapsulation in PLGA NPs significantly reduced PEI-associated toxicity on DCs. We propose this formulation as a strategy for targeted siRNA delivery to DCs. The potential of this approach is not limited to STAT3 downregulation in DCs but can be used to target the expression of other proteins as well. Moreover, it can be combined with other means for cancer immunotherapy like cancer vaccine strategies.
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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".