The Multifaceted Role of Osteopontin in Modulating the Tumor Microenvironment
Bibliographic record
Abstract
Osteopontin (OPN), a key structural protein in the extracellular matrix, plays a pivotal role in regulating the local tumor microenvironment and systemic immunity during cancer progression. Recognizing that tumor cells do not exist in isolation but rather interact with a multitude of stromal components that significantly influence patient outcomes and therapy responses, this review focuses on the role of OPN in nontumor cells in the context of solid cancers and the associated phenotypic and mechanistic insights. We explore how OPN influences the behavior of various innate and adaptive immune cells, including NK cells, neutrophils, macrophages, dendritic cells, myeloid-derived suppressor cells, B cells, and T cells, as well as structural stromal cells such as fibroblasts, endothelial cells, and adipocytes. The review highlights the roles of OPN in modulating these stromal cells and their multiaxial influence on tumorigenesis and metastasis. These complex interplays offer insights into potential diagnostic and therapeutic strategies around OPN/stromal-mediated pathways in cancer progression and recurrence.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".