Hypoxia/reoxygenation: A dynamic regulator of lysyl oxidase‐facilitated breast cancer migration
Bibliographic record
Abstract
Fluctuating oxygen levels characterize the microenvironment of many cancers and tumor hypoxia is associated with increased invasion and metastatic potential concomitant with a poor prognosis. Similarly, the expression of lysyl oxidase (LOX) in breast cancer facilitates tumor cell migration and is associated with estrogen receptor negative status and reduced patient survival. Here we demonstrate that hypoxia/reoxygenation drives poorly invasive breast cancer cells toward a more aggressive phenotype by up-regulating LOX expression and catalytic activity. Specifically, hypoxia markedly increased LOX protein expression; however, catalytic activity (beta-aminopropionitrile inhibitable hydrogen peroxide production) was significantly reduced under hypoxic conditions. Moreover, poorly invasive breast cancer cells displayed a marked increase in LOX-dependent FAK/Src activation and cell migration following hypoxia/reoxygenation, but not in response to hypoxia alone. Furthermore, LOX expression is only partially dependent on hypoxia inducible factor-1 (HIF-1alpha) in poorly invasive breast cancer cells, as hypoxia mimetics and overexpression of HIF-1alpha could not up-regulate LOX expression to the levels observed under hypoxia. Clinically, LOX expression positively correlates with tumor progression and co-localization with hypoxic regions (defined by HIF-1alpha expression) in ductal carcinoma in situ and invasive ductal carcinoma primary tumors. However, positive correlation is lost in metastatic tumors, suggesting that LOX expression is independent of a hypoxic environment at later stages of tumor progression. This work demonstrates that both hypoxia and reoxygenation are necessary for LOX catalytic activity which facilitates breast cancer cell migration through a hydrogen peroxide-mediated mechanism; thereby illuminating a potentially novel mechanism by which poorly invasive cancer cells can obtain metastatic competency.
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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".