Osteopontin overexpression in breast cancer: Knowledge gained and possible implications for clinical management
Bibliographic record
Abstract
Osteopontin (OPN) is a secreted protein that is overexpressed in a number of human cancers, and has been associated with increased metastatic burden and poor prognosis in breast cancer patients. The OPN protein contains several conserved structural elements including heparin- and calcium-binding domains, a thrombin-cleavage site, a CD44 binding site, and two integrin-binding sites. Experimental studies have shown that the ability of OPN to interact with a diverse range of factors, including cell surface receptors (integrins, CD44), secreted proteases (matrix metalloproteinases, urokinase plasminogen activator), and growth factor/receptor pathways (TGFalpha/EGFR, HGF/Met) is central to its role in malignancy. These complex signaling interactions can result in changes in gene expression, which ultimately lead to alterations in cell properties involved in malignancy such as adhesion, migration, invasion, enhanced tumor cell survival, tumor angiogenesis, and metastasis. Therefore, OPN is not merely associated with cancer, but rather it plays a multi-faceted functional role via complex molecular cross-talk with other factors. This review will focus on the role of OPN in breast cancer, in particular on the malignancy-promoting aspects of OPN that may reveal opportunities for new approaches to the clinical management of breast cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".