Extracellular vesicles from thyroid cancer harbor a functional machinery involved in extracellular matrix remodeling
Bibliographic record
Abstract
Extracellular vesicles (EVs) participate in cell-stroma crosstalk within the tumor microenvironment and fibroblasts (Fb) contribute to tumor promotion in thyroid cancer. However, the role of tumor-stroma derived EVs still needs to be deciphered. We hypothesized that the interaction of thyroid tumor cells with Fb would liberate EVs with a specific proteomic profile, which would have an impact on EV-functionality in thyroid tumor progression-related events. Tumor (TPC-1, 8505c) and non-tumor (NThyOri) thyroid cells were co-cultured with human Fb. EVs, obtained by ultracentrifugation of conditioned media, were characterized by nanoparticle tracking analysis and western blotting. EV-proteomic analysis was performed by mass-spectrometry, and metalloproteinases (MMPs) were studied by zymography. EV-exchange was evaluated using immunofluorescence, confocal microscopy and FACS. EVs expressed classical exosome markers, with EVs from thyroid tumor cell-Fb co-cultures showing a proteomic profile related to extracellular matrix (ECM) remodeling. Bidirectional crosstalk between Fb and TPC-1 cells produced significantly more EVs than their isolated cells, and potentiated EV-functionality. In line with this, Fb-TPC-1 derived EVs induced MMP2 activation in NThyOri supernatants, and MMP2 activity could be evidenced in Fb and TPC-1 contact-independent co-cultures. Besides, MMP2 interactors allowed us to discriminate between EVs from thyroid tumoral and non-tumoral milieus. Interestingly, Fb internalized more EVs from TPC-1 than from NThyOri producing cells. Fb and thyroid tumor cell crosstalk produces specialized EVs with an ECM remodeling proteomic profile, enabling activation of MMP2 and possibly facilitating ECM-degradation, which is potentially linked with thyroid tumor progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".