Bone Marrow-derived Myofibroblasts Are the Providers of Pro-invasive Matrix Metalloproteinase 13 in Primary Tumor
Bibliographic record
Abstract
Carcinoma-associated fibroblasts are key contributors of the tumor microenvironment that regulates carcinoma progression. They consist of a heterogeneous cell population with diverse origins, phenotypes, and functions. In the present report, we have explored the contribution of bone marrow (BM)-derived cells to generate different fibroblast subsets that putatively produce the matrix metalloproteinase 13 (MMP13) and affect cancer cell invasion. A murine model of skin carcinoma was applied to mice, irradiated, and engrafted with BM isolated from green fluorescent protein (GFP) transgenic mice. We provide evidence that one third of BM-derived GFP(+) cells infiltrating the tumor expressed the chondroitin sulfate proteoglycan NG2 (pericytic marker) or α-smooth muscle actin (α-SMA, myofibroblast marker), whereas almost 90% of Thy1(+) fibroblasts were originating from resident GFP-negative cells. MMP13producing cells were exclusively α-SMA(+) cells and derived from GFP(+) BM cells. To investigate their impact on tumor invasion, we isolated mesenchymal stem cells (MSCs) from the BM of wild-type and MMP13-deficient mice. Wild-type MSC promoted cancer cell invasion in a spheroid assay, whereas MSCs obtained from MMP13-deficient mice failed to. Our data support the concept of fibroblast subset specialization with BM-derived α-SMA(+) cells being the main source of MMP13, a stromal mediator of cancer cell invasion.
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 | 0.001 |
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".