TRAIL Therapy for Breast Cancer Treatment by Employing Lipopolymer mRNA Delivery
Bibliographic record
Abstract
Promising improvements in synthesis of stable and nonimmunogenic messenger RNA (mRNA) have enhanced the potential for mRNA in in situ production of therapeutic proteins. In this study, we explored the delivery of an mRNA expressing the tumor necrosis factor-related apoptosis-inducing ligand protein (mTRAIL) into breast cancer cells and human bone marrow stromal cells (hBMSCs) by using aliphatic lipid-modified small molecular weight polyethylenimine (PEI) carriers. The duration and extent of TRAIL secretion after mRNA delivery were dependent on the cell type, but mRNA delivery consistently showed earlier and higher protein expression compared with plasmid DNA delivery. Higher TRAIL-induced cytotoxicity and apoptosis induction were evident in breast cancer cells after mTRAIL transfection. In addition, hBMSCs transfected with mTRAIL were able to effectively kill breast cancer cells after coculture. mTRAIL delivery with the lipopolymers resulted in significant inhibition of breast cancer tumor growth in a xenograft model in mice. We conclude that an mRNA-based approach to TRAIL expression is a highly promising approach to retard the growth of breast cancer cells.
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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".