Cancer-Associated Fibroblasts Drive the Progression of Metastasis through both Paracrine and Mechanical Pressure on Cancer Tissue
Bibliographic record
Abstract
Neoplastic cells recruit fibroblasts through various growth factors and cytokines. These "cancer-associated fibroblasts" (CAF) actively interact with neoplastic cells and form a myofibroblastic microenvironment that promotes cancer growth and survival and supports malignancy. Several products of their paracrine signaling repertoire have been recognized as tumor growth and metastasis regulators. However, tumor-promoting cell signaling is not the only reason that makes CAFs key components of the "tumor microenvironment," as CAFs affect both the architecture and growth mechanics of the developing tumor. CAFs participate in the remodeling of peritumoral stroma, which is a prerequisite of neoplastic cell invasion, expansion, and metastasis. CAFs are not present peritumorally as individual cells but they act orchestrated to fully deploy a desmoplastic program, characterized by "syncytial" (or collective) configuration and altered cell adhesion properties. Such myofibroblastic cohorts are reminiscent of those encountered in wound-healing processes. The view of "cancer as a wound that does not heal" led to useful comparisons between wound healing and tumorigenesis and expanded our knowledge of the role of CAF cohorts in cancer. In this integrative model of cancer invasion and metastasis, we propose that the CAF-supported microenvironment has a dual tumor-promoting role. Not only does it provide essential signals for cancer cell dedifferentiation, proliferation, and survival but it also facilitates cancer cell local invasion and metastatic phenomena.
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) | 0.003 | 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".