Regulation of Autophagy Progress via Lysosomal Depletion by Fluvastatin Nanoparticle Treatment in Breast Cancer Cells
Bibliographic record
Abstract
Fluvastatin (FLV) is a statin family member that may play a role in modulating a variety of medical disorders such as atherosclerosis and breast cancer. The present study addresses the ability of FLV to modulate the cellular immune response and provides a new nanosized FLV formula (self-nanoemulsifying delivery system, SNED) potentially more effective for suppression of breast cancer development. We monitored autophagic machinery through the expression of microtubule-associated protein 1A/1B-light chain 3 (LC3I/II). Lysosomal activity upon treatment was evaluated by mRNA and protein expression of lysosomal-associated membrane protein 1 (LAMP-1). Mitogen-activated protein kinase (MAPK) signaling and its association with proinflammatory cytokine secretion were assessed in treated cells. Autophagosome formation was significantly increased in cells that were pretreated with FLV-SNED in comparison to FLV-treated cells. Activation of autophagy was accompanied with arrest of LAMP-1 expression, which correlates with lysosomal activity. Simultaneously, both FLV and FLV-SNED activated MAPK signaling and modified interleukin-6 and tumor necrosis factor-α levels in treated cells. These findings indicate that FLV reduces cell viability via depletion of lysosomal activities along with accumulation of autophagosomes leading to disturbance of autophagosome-lysosomal fusion in treated cells. Furthermore, our data reveal the effectiveness of both FLV agents in the modulation of proinflammatory cytokine secretion from treated cells via regulation of MAPK signaling cascades and indicate that FLV-SNED is more efficient than FLV. This study provides new insights into how FLV regulates breast cancer cell viability via modulation of AMPK-mTOR and ERK-mTOR signaling, and through autophagosome formation accompanied by lysosomal degradation.
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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".