Integrin α11β1 in tumor fibrosis: more than just another cancer-associated fibroblast biomarker?
Bibliographic record
Abstract
There is currently an increased interest in understanding the role of the tumor microenvironment (TME) in tumor growth and progression. In this context the role of integrins in cancer-associated fibroblasts (CAFs) will need to be carefully re-evaluated. Fibroblast-derived cells are not only in the focus in tumors, but also in tissue fibrosis as well as in inflammatory conditions. The recent transcriptional profiling of what has been called "the pan-fibroblast cell lineage" in mouse and human tissues has identified novel transcriptional biomarker mRNAs encoding the secreted ECM proteins dermatopontin and collagen XV as well as the phosphatidylinositol-anchored membrane protein Pi16. Some of the genes identified in these fibroblasts scRNA-seq datasets will be useful for rigorous comparative characterizations of fibroblast-derived cell subpopulations. At the same time, it will be a challenge in the coming years to validate these transcriptional mRNA datasets at the protein-(expression) and at tissue-(distribution) levels and to find useful protein biomarker reagents that will facilitate fibroblast profiling at the cell level. In the current review we will focus on the role of the collagen-binding integrin α11β1 in CAFs, summarizing our own work as well as published datasets with information on α11 mRNA expression in selected tumors. Our experimental data suggest that α11β1 is more than just another biomarker and that it as a functional collagen receptor in the TME is playing a central role in regulating collagen assembly and matrix remodeling, which in turn impact tumor growth and metastasis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".