Potential Role of CCN Proteins in Breast Cancer: Therapeutic Advances and Perspectives
Bibliographic record
Abstract
CCNs are a specific type of matricellular protein, which are essential signaling molecules, and play multiple roles in multicellular eukaryotes. This family of proteins consists of six separate members, which exist only in vertebrates. The architecture of CCN proteins is multi-modular comprising four distinct modules. CCN Proteins achieve their primary functional activities by binding with several integrin7 receptors. The CCN family has been linked to cell adhesion, chemotaxis and migration, mitogenesis, cell survival, angiogenesis, differentiation, tumorigenesis, chondrogenesis, and wound healing, among other biological interactions. Breast cancer is the most commonly diagnosed cancer worldwide and CCN regulated breast cancer stands at the top. A favorable or unfavorable association between various CCNs has been reported in patients with breast carcinomas. The pro-tumorigenic CCN1, CCN2, CCN3, and CCN4 may lead to human breast cancer, although the anti-tumorigenic actions of CCN5 and CCN6 are also present. Several studies have been conducted on CCN proteins and cancer in recent years. CCN1 and CCN3 have been shown to exhibit a dual nature of tumor inhibition and tumor suppression to some extent in quiet recent time. Pharmacological advances in treating breast cancer by targeting CCN proteins are also reported. In our study, we intend to provide an overview of these research works while keeping breast cancer in focus. This information may facilitate early diagnosis, early prognosis and the development of new therapeutic strategies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".