Linoleic‐acid‐substituted polyethylenimine to silence heat shock protein 90B1 (HSP90B1) to inhibit migration of breast cancer cells
Bibliographic record
Abstract
INTRODUCTION: Breast cancer continues to be one of the leading causes of death in women, and the lack of treatment options for distant metastasis warrants the need to identify and develop more effective approaches. The aim of this study was to identify and validate targets that are associated with the survival and migration of the breast cancer cells in vitro through RNA interference (RNAi) approach. METHODS: Linoleic-acid-modified polyethylenimine (PEI) polymer was used to screen a short interfering RNA (siRNA) library against numerous cell adhesion and cytoskeleton genes in MDA-MB-231 triple-negative breast cell line, and the functional outcome of silencing was determined by growth and migration inhibition with further target validation studies. RESULTS: Heat shock protein 90B1 (HSP90B1) was identified as a crucial gene that is known to be involved in various breast cancer machineries, including uncontrolled proliferation and brain metastasis. The success of this approach was also due to the use of hyaluronic acid (HA) additive in lipopolymer complexes that showed a profound impact in reducing the cell viability (~50%), migration (~40%), and mRNA transcript levels (~80%) with a physiologically relevant siRNA concentration of 60 nM. The use of Dicer-substrate siRNA proved to be beneficial in target silencing, and a combinational treatment of integrin-β1 (ITGB1) and HSP90B1 was effective in reducing the migration of the MDA-MB-231 and MDA-MB-436 breast cancer cells. CONCLUSION: This study demonstrates the potential to identify and silence targets using a lipid-modified PEI/siRNA system and highlights the importance of HSP90B1 in the growth and migration of breast cancer cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".