Exosomes Induce Fibroblast Differentiation into Cancer-Associated Fibroblasts through TGFβ Signaling
Bibliographic record
Abstract
Abstract A particularly important tumor microenvironment relationship exists between cancer cells and surrounding stromal cells. Fibroblasts, in response to cancer cells, become activated and exhibit myofibroblastic characteristics that favor invasive growth and metastasis. However, the mechanism by which cancer cells promote activation of healthy fibroblasts into cancer-associated fibroblasts (CAF) is still not well understood. Exosomes are nanometer-sized vesicles that shuttle proteins and nucleic acids between cells to establish intercellular communication. Here, bladder cancer–derived exosomes were investigated to determine their role in the activation of healthy primary vesical fibroblasts. Exosomes released by bladder cancer cells are internalized by fibroblasts and promoted the proliferation and expression of CAF markers. In addition, cancer cell–derived exosomes contain TGFβ and in exosome-induced CAFs SMAD-dependent signaling is activated. Furthermore, TGFβ inhibitors attenuated CAF marker expression in healthy fibroblasts. Therefore, these data demonstrate that bladder cancer cells trigger the differentiation of fibroblasts to CAFs by exosomes-mediated TGFβ transfer and SMAD pathway activation. Finally, exosomal TGFβ localized inside the vesicle and contributes 53.4% to 86.3% of the total TGFβ present in the cancer cell supernatant. This study highlights a new function for bladder cancer exosomes as novel modulators of stromal cell differentiation. Implication: This study identifies exosomal TGFβ as new molecular mechanism involved in cancer-associated fibroblast activation. Mol Cancer Res; 16(7); 1196–204. ©2018 AACR.
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".