Matricellular proteins in cancer: a focus on secreted Frizzled-related proteins
Bibliographic record
Abstract
Tumours are complex entities, wherein cancer cells interact with myriad soluble, insoluble and cell associated factors. These microenvironmental mediators regulate tumour growth, progression and metastasis, and are produced by cancer cells and by stromal components such as fibroblast, adipocytes and immune cells. Through their ability to bind to extracellular matrix proteins, cell surface receptors and growth factors, matricellular proteins enable a dynamic reciprocity between cancer cells and their microenvironment. Hence, matricellular proteins play a critical role in tumour progression by regulating where and when cancer cells are exposed to key growth factors and regulatory proteins. Recent studies suggest that, in addition to altering Wingless (Wnt) signalling, certain members of the Secreted Frizzled Related Protein (sFRP) family are matricellular in nature. In this review, we outline the importance of matricellular proteins in cancer, and discuss how sFRPs may function to both inhibit and promote cancer progression in a context-dependent manner. By considering the matricellular functionality of sFRPs, we may better understand their apparently paradoxical roles in cancers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".