Extracellular vesicles promote activation of pro-inflammatory cancer-associated fibroblasts in oral cancer
Bibliographic record
Abstract
Introduction: Oral squamous cell carcinoma (OSCC) is the most common form of head and neck cancer and has a survival rate of ∼50% over 5 years. New treatment strategies are sorely needed to improve survival rates—and a better understanding of the mechanisms underlying tumorigenesis is needed to develop these strategies. The role of the tumor microenvironment (TME) has increasingly been identified as crucial in tumor progression and metastasis. One of the main constituents of the TME, cancer-associated fibroblasts (CAFs), plays a key role in influencing the biological behavior of tumors. Multiple mechanisms contribute to CAF activation, such as TGFβ signaling, but the role of extracellular vesicles (EVs) in CAF activation in OSCC is poorly understood. Assessing the impact of oral cancer-derived EVs on CAF activation will help to better illuminate OSCC pathophysiology and may drive development of novel treatments options. Methods: EVs were isolated from OSCC cell lines (Cal 27, SCC-9, SCC-25) using differential centrifugation. Nanoparticle tracking analysis was used for EV characterization, and Western blot to confirm the presence of EV protein markers. Oral fibroblasts were co-cultured with enriched EVs, TGFβ, or PBS over 72 h to assess activation. Flow cytometry was used to evaluate CAF markers. RNA collected from fibroblasts was extracted and the transcriptome was sequenced. Conditioned media from the co-cultures was evaluated with cytokine array profiling. Results: OSCC-derived EVs can activate oral fibroblasts into CAFs that are different from those activated by TGFβ, suggesting different mechanisms of activation and different functional properties. Gene set enrichment analysis showed several upregulated inflammatory pathways in those CAFs exposed to OSCC-derived EVs. Marker genes for inflammatory CAF subtypes were also upregulated, but not in CAFs activated by TGFβ. Finally, cytokine array analysis on secreted proteins revealed elevated levels of several pro-inflammatory cytokines from EV-activated CAFs, for instance IL-8 and CXCL5. Discussion: Our results reveal the ability of OSCC-derived EVs to activate fibroblasts into CAFs. These CAFs seem to have unique properties, differing from TGFβ-activated CAFs. Gaining an understanding of the interplay between EVs and stromal cells such as CAFs could lead to further insights into OSCC tumorigenesis and potential 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 | 0.000 | 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) | 0.003 | 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".