<p>siRNA-based breast cancer therapy by suppressing 17&beta;-hydroxysteroid dehydrogenase type 1 in an optimized xenograft cell and molecular biology model in vivo</p>
Bibliographic record
Abstract
PURPOSE: Hormone-dependent breast cancer is the most common form of breast cancer, and inhibiting 17β-HSD1 can play an attractive role in decreasing estrogen and cancer cell proliferation. However, the majority of existing inhibitors have been developed from estrogens and inevitably possess residual estrogenicity. siRNA knockdown provides a highly specific way to block a targeted enzyme, being especially useful to avoid estrogenicity. Application of 17β-HSD1-siRNA in vivo is limited by the establishment of an animal model, as well as the potential nuclease activity in vivo. We tried to reveal the in vivo potential of 17β-HSD1-siRNA-based breast cancer therapy. MATERIALS AND METHODS: To establish a competent animal model, daily subcutaneous injection of an estrone micellar aqueous solution was adopted to provide the substrate for estradiol biosynthesis. The effects of three different doses of estrone (0.1, 0.5, and 2.5 µg/kg/day) on tumor growth in T47D-17β-HSD1-inoculated group were investigated and compared with the animals inoculated with wild type T47D cells. To solve in vivo delivery problem of siRNA, "17β-HSD1-siRNA/LPD", a PEGylated and modified liposome-polycation-DNA nanoparticle containing 17β-HSD1-siRNA was prepared by the thin film hydration method and postinsertion technology. Finally, "17β-HSD1-siRNA/LPD" was tested in the optimized model. Tumor growth and 17β-HSD1 expression were assessed. RESULTS: Comparison with the untreated group revealed significant suppression of tumor growth in "17β-HSD1-siRNA/LPD"-treated group when HSD17B1 gene expression was knocked down. CONCLUSION: These findings showed promising in vivo assessments of 17β-HSD1-siRNA candidates. This is the first report of an in vivo application of siRNA for steroid-converting enzymes in a nude mouse model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".