Tumor microenvironment-based feed-forward regulation of NOS2 in breast cancer progression
Bibliographic record
Abstract
Inflammation is widely recognized as an inducer of cancer progression. The inflammation-associated enzyme, inducible nitric oxide synthase (NOS2), has emerged as a candidate oncogene in estrogen receptor (ER)-negative breast cancer, and its increased expression is associated with disease aggressiveness and poor survival. Although these observations implicate NOS2 as an attractive therapeutic target, the mechanisms of both NOS2 induction in tumors and nitric oxide (NO)-driven cancer progression are not fully understood. To enhance our mechanistic understanding of NOS2 induction in tumors and its role in tumor biology, we used stimulants of NOS2 expression in ER(-) and ER(+) breast cancer cells and examined downstream NO-dependent effects. Herein, we show that up-regulation of NOS2 occurs in response to hypoxia, serum withdrawal, IFN-γ, and exogenous NO, consistent with a feed-forward regulation of NO production by the tumor microenvironment in breast cancer biology. Moreover, we found that key indicators of an aggressive cancer phenotype including increased S100 calcium binding protein A8, IL-6, IL-8, and tissue inhibitor matrix metalloproteinase-1 are up-regulated by these NOS2 stimulants, whereas inhibition of NOS2 in MDA-MB-231 breast cancer cells suppressed these markers. Moreover, NO altered cellular migration and chemoresistance of MDA-MB-231 cells to Taxol. Most notably, MDA-MB-231 tumor xenographs and cell metastases from the fat pad to the brain were significantly suppressed by NOS2 inhibition in nude mice. In summary, these results link elevated NOS2 to signals from the tumor microenvironment that arise with cancer progression and show that NO production regulates chemoresistance and metastasis of breast cancer cells.
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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 | 0.000 | 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 | 0.000 |
| 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".