Downregulation of osteopontin contributes to metastasis suppression by breast cancer metastasis suppressor 1
Bibliographic record
Abstract
Breast cancer metastasis suppressor 1 (BRMS1) inhibits the ability of multiple human and murine cancer cell lines to metastasize to lymph nodes, bones and lungs. Comparison of mRNA expression in metastatic MDA-MB-435 human carcinoma cells (435) and metastasis-suppressed BRMS1 transfectants (435/BRMS1) showed a marked (>90%) reduction of osteopontin (OPN) mRNA and protein expression in BRMS1-overexpressing cells. OPN expression is associated with disease progression in patients, with higher levels of OPN produced by cancer cells associated with poorer patient survival. Furthermore, OPN has been suggested to promote survival of cancer cells in response to stress, although the mechanisms by which this may occur remain poorly understood. This study tested the hypothesis that re-expression of OPN in metastasis-suppressed 435/BRMS1 cells would reverse metastasis suppression and confer protection from stress-induced apoptosis. A stable pooled population of OPN overexpressing 435/BRMS1 cells was created (435/BRMS1/OPN). OPN re-expression did not affect in vitro cell growth rates; however, increased anchorage independent growth/survival and protection from hypoxia-induced apoptosis was observed (p < 0.05). In vivo, OPN re-expression in BRMS1 transfected cells did not affect in vivo primary tumor growth but did increase the incidence of spontaneous metastasis to lymph nodes and lungs in mice. These novel findings suggest that OPN downregulation by BRMS1 may be responsible, at least in part, for BRMS1-mediated metastasis suppression by sensitizing cancer cells to stress induced apoptosis. These studies clarify one mechanism by which BRMS1 can suppress metastasis.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".