Cancer-Associated Fibroblasts in a 3D Engineered Tissue Model Induce Tumor-like Matrix Stiffening and EMT Transition
Bibliographic record
Abstract
A tumor microenvironment is characterized by its altered mechanical properties. However, most models remain unable to faithfully recreate the mechanical properties of a tumor. Engineered models based on the self-assembly method have the potential to better recapitulate the stroma architecture and composition. Here, we used the self-assembly method based on a bladder tissue model to engineer a tumor-like environment. The tissue-engineered tumor models were reconstituted from stroma-derived healthy primary fibroblasts (HFs) induced into cancer-associated fibroblast cells (iCAFs) along with an urothelium overlay. The iCAFs-derived extracellular matrix (ECM) composition was found to be stiffer, with increased ECM deposition and remodeling. The urothelial cells overlaid on the iCAFs-derived ECM were more contractile, as measured by quantitative polarization microscopy, and displayed increased YAP nuclear translocation. We further showed that the proliferation and expression of epithelial-to-mesenchymal transition (EMT) marker in the urothelial cells correlate with the increased stiffness of the iCAFs-derived ECM. Our data showed an increased expression of EMT markers within the urothelium on the iCAFs-derived ECM. Together, our results demonstrate that our tissue-engineered tumor model can achieve stiffness levels comparable to that of a bladder tumor, while triggering a tumor-like response from the urothelium.
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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".