STAT3 Knockdown in B16 Melanoma by siRNA Lipopolyplexes Induces Bystander Immune Response In Vitro and In Vivo
Bibliographic record
Abstract
Persistent activation of STAT3 plays a major role in cancer progression and immune escape. Therefore, targeting STAT3 in tumors is essential to enhance/reactivate antitumor immune response. In our previous studies, we demonstrated the efficacy of stearic acid-modified polyethylenimine (PEI-StA) in promoting small interfering RNA (siRNA) silencing of STAT3 in B16.F10 melanoma in vitro and in vivo. In the current study, we examine the immunologic impact of this intervention. Toward this goal, the infiltration and activation of lymphocytes and dendritic cells (DCs) in the tumor mass were assessed using flow cytometry. Moreover, the levels of IFN-γ, IL-12, and TNF-α in homogenized tumor supernatants were determined. Moreover, mixed lymphocytes reaction using splenocytes of tumor-bearing mice was used to assess DC functionality on siRNA/lipopolyplexes intervention. Our results demonstrated up to an approximately fivefold induction in the infiltration of CD3(+) cells in tumor mass on STAT3 knockdown with high levels of CD4(+), CD8(+), and NKT cells. Consistently, DC infiltration in tumor milieu increased up to approximately fourfold. Those DCs were activated, in an otherwise suppressive microenvironment, as evidenced by a high expression of costimulatory molecules CD86 and CD40. ELISA analysis revealed a significant increase in IFN-γ, IL-12, and TNF-α. Moreover, mixed lymphocytes reaction demonstrated alloreactivity of these DCs as assessed by high T-cell proliferation and IL-2 production. Our results suggest a bystander immune response after local STAT3 silencing by siRNA. This strategy could be beneficial as an adjuvant therapy along with current cancer vaccine formulations.
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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 | 0.001 |
| 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".