Small Extracellular Vesicles Derived from NF2-Associated Schwannoma Cells Modulate Tumor Progression and Immunity via HSP90
Bibliographic record
Abstract
In-depth exploration of tumor immune suppression mechanisms may provide new therapeutic options for NF2-associated tumors. In this study, we found that sEVs secreted by NF2-associated schwannomas (NF2-EVs) facilitate the conversion of CD14+ monocytes into an MDSC-like phenotype, showcasing MDSC-like inhibitory functions. Moreover, these NF2-EVs are capable of enhancing tumor cell proliferation. Through proteomic analysis and subsequent validation of the NF2-EVs, we identified elevated levels of HSP90. When we knocked down HSP90 expression in tumor cells, the sEVs secreted showed diminished capacity to convert monocytes into MDSCs and a reduced ability to promote tumor cell proliferation. Conversely, sEVs secreted by tumor cells that overexpress HSP90 displayed the opposite effects. Further mechanistic studies revealed that HSP90 could influence the expression of AKT/p-AKT and ERK/p-ERK. Our results suggest that NF2 tumor cells could regulate the AKT/p-AKT and ERK/p-ERK pathways to promote tumor cell proliferation and the formation of an immunosuppressive microenvironment by secreting sEVs’ HSP90, offering valuable insights into the involvement of HSP90 in exosome-mediated communication within the context of NF2-related schwannomatosis (NF2-SWN). This information has the potential to inform the design of effective immunotherapeutic protocols and offer new treatment options for NF2-SWN patients.
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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".