S100A10 Has a Critical Regulatory Function in Mammary Tumor Growth and Metastasis: Insights Using MMTV-PyMT Oncomice and Clinical Patient Sample Analysis
Bibliographic record
Abstract
S100A10 (p11) is a plasminogen receptor that regulates cellular plasmin generation by cancer cells. In the current study, we used the MMTV-PyMT mouse breast cancer model, patient tumor microarray, and immunohistochemical (IHC) analysis to investigate the role of p11 in oncogenesis. The genetic deletion of p11 resulted in significantly decreased tumor onset, growth rate, and spontaneous pulmonary metastatic burden in the PyMT/p11-KO (knock-out) mice. This phenotype was accompanied by substantial reduction in Ki67 positivity, macrophage infiltration, decreased vascular density in the primary tumors, and decrease in invasive carcinoma and pulmonary metastasis. Surprisingly, IHC analysis of wild-type MMTV-PyMT mice failed to detect p11 expression in the tumors or metastatic tumor cells and loss of p11 did not decrease plasmin generation in the PyMT tumors and cells. Furthermore, tumor cells expressing p11 displayed dramatically reduced lung metastasis when injected into p11-depleted mice, further strengthening the stromal role of p11 in tumor growth and metastasis. Transcriptome analysis of the PyMT tumors from p11-KO mice showed marked reduction in genes such as Areg, Muc1, and S100a8 involved in breast cancer development, progression, and inflammation. The PyMT/p11-KO tumors displayed a remarkable increase in inflammatory cytokines such as interleukin (Il)-6, Il-10, and interferon (Ifn)-γ. Gene expression profiling and IHC of primary breast cancer samples showed that p11 mRNA and protein levels were significantly higher in tumor tissues compared to normal mammary tissue. P11 mRNA expression was significantly associated with poor patient prognosis and significantly elevated in high grade, triple negative (TN) tumors, and tumors with high proliferative index. This is the first study examining the crucial role of p11 in breast tumor development and metastasis, thus emphasizing its potential as a diagnostic and prognostic biomarker in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".