Bibliographic record
Abstract
Since the authors first reviewed this subject in 2016 significant progress has been documented in the CCN field with advances made in the understanding of how members of the CCN family of proteins, CCN1-6, contribute to the pathogenesis and progression, positive and negative, of a larger variety of cancers. As termed matricellular proteins, and more recently the connective communication network, it has become clearer that members of the CCN family interact complexly with other proteins in the extracellular microenvironment, membrane signaling proteins, and can also operate intracellularly at the transcriptional level. In this review we expand on this earlier information providing new detailed information and insights that appropriate a much greater involvement and importance of their role in multiple aspects of cancer. Despite all the new information many more questions have been raised and intriguing results generated that warrant greater investigation. In order to permit the reader to smoothly integrate the new information we discuss all relevant CCN members in the context of cancer subtypes. We have harmonized the nomenclature with CCN numbering for easier comparisons. Finally, we summarize what new has been learned and provide a perspective on how our knowledge about CCN1-6 is being used to drive new initiatives on cancer therapeutics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".