Low density lipoprotein receptor mediates anti-VEGF effect of lymphocyte T-derived microparticles in Lewis lung carcinoma cells
Bibliographic record
Abstract
Nonstop proliferation and vigorous neovascularization are two prominent characteristics of cancer. Antiangiogenic therapy has emerged as an important modality in treatment of solid tumors. Our previous work demonstrated that microparticles derived from apoptotic T-lymphocytes (LMPs) not only reduced the viabilities of high-proliferating cells, but also exhibited potent antiangiogenic effects through inhibition of the vascular endothelial growth factor (VEGF)/VEGF receptor 2 signalling pathway. In the present study, we extended these studies to explore the anticancer potential of LMPs using a murine model of Lewis lung carcinoma (LLC). Results show that intratumoral injection of LMPs (2.5 mg/kg) decreased tumor size by more than 50% relative to control. Tumor microvessel density and VEGF-A levels were also markedly reduced upon LMPs treatment. To elucidate the underlying mechanisms of LMPs-mediated antitumor activity, LLC cells were utilized in in vitro experiments. LMPs suppressed VEGF-A protein levels in LLC cells and led to inhibition of LLC cell viability and proliferation. In addition, knockdown of the low-density lipoprotein receptor (LDLR) expression reduced the uptake of LMPs into LLC cells and attenuated the inhibitory effects of LMPs on cell growth and VEGF-A expression. Our findings demonstrate that LMPs exert antiangiogenic and proapoptotic effects that lead to inhibition of lung carcinoma by reducing VEGF-A levels and LDLR mediates the anti-VEGF effect of LMPs through translocating LMPs into LLC cells. These results suggest that LMPs are promising antiangiogenic therapeutic agent and represent a new therapeutic strategy for treating lung carcinomas.
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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 | 0.000 | 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 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".