The Impact of Extracellular Vesicles on Inflammation in the Tumor Microenvironment of Sarcomas: A Literature Review
Bibliographic record
Abstract
Introduction: Sarcomas are a diverse group of tumours encompassing over 100 different subtypes occurring in bone, muscle, and cartilage. The sarcoma tumour microenvironment (TME) contains various immune modulators and is influenced by the specific sarcoma subtype. The sarcoma TME is modulated by secretion of cytokines, chemokines, and extracellular vesicles (EVs). EVs play a role in inflammatory and immune responses in the TME. EVs effects are attributed to their cargo, consisting of mRNAs, proteins, miRNAs, chemokines, and cytokines. Sarcoma cells release tumour-derived EVs, facilitating communication, tumour progression, and inflammation. Methods: We conducted a literature search for keywords and concepts associated with EVs and inflammatory responses in sarcomas on the PubMed database. Keyword combinations included relevance to EVs, sarcomas and inflammation. Only studies from peer reviewed journals, written in English, and published between 2013-2024 were considered. Sarcoma subtypes viewed included: rhabdomyosarcoma, Ewing sarcoma, chondrosarcoma, liposarcoma, and osteosarcoma. Results: Ewing sarcoma EVs carry miRNA and mRNAs including EWS/FLI-1 mRNA, and surface proteins causing upregulation of inflammatory pathways, and contributing to the release of pro-inflammatory cytokines. Rhabdomyosarcoma EVs carry miRNAs and PAX3/7-FOXO1 mRNA stimulating inflammatory pathways to release cytokines and chemokines. Chondrosarcoma’s hypoxic TME causes increased EV secretion, favouring M2 polarisation, and the production of immunosuppressive markers. Liposarcoma EVs enriched with miRNAs and MDM2 induced an inflammatory response in macrophages. Osteosarcoma EVs contained TGF-β and miRNA cargo which influenced macrophage polarisation. Discussion: Sarcoma-derived EVs contain a wide range of mRNAs and miRNAs, with Ewing sarcoma and Rhabdomyosarcoma also containing the fusion oncogenes EWS/FLI-1 and PAX3/7-FOXO1, respectively. Surface proteins on Ewing sarcoma and osteosarcoma EVs were better understood compared to other sarcomas. Various sarcoma-derived EV cargo demonstrated potential to influence inflammatory pathways within macrophages. Macrophage polarisation is fundamental to immune function, determining pro-inflammatory and anti-inflammatory responses. Conclusion: By understanding the role of EVs in the inflammatory process, a deeper insight in tumour progression and tumour cell-cell communication can be achieved. The uniqueness of EV cargo to certain sarcoma subtypes could potentially indicate a future as biomarkers for early cancer detection. Further research into inflammatory effects by EVs can provide potential as novel therapeutics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".