Intermittent hypoxia confers pro-metastatic gene expression selectively through NF-κB in inflammatory breast cancer cells
Bibliographic record
Abstract
Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer. Treatment options are limited and the mechanisms underlying its aggressiveness are poorly understood. Intermittent hypoxia (IH) causes oxidative stress and is emerging as important regulator of tumor metastasis. Vessels in IBC tumors have been shown to be immature, which is a primary cause of IH. We therefore investigated the relevance of IH for the modulation of gene expression in IBC cells in order to assess IH as potential regulator of IBC aggressiveness. Gene array analysis of IBC cells following chronic IH (45-60 days) demonstrated increased expression of pro-metastatic genes of the extracellular matrix, such as tenascin-C (TNC; an essential factor of the metastatic niche) and matrix metalloproteinase 9 (MMP9), and of pro-inflammatory processes, such as cyclooxygenase-2 (COX-2). Investigating the oxidative stress-dependent regulation of TNC, we found a gradual sensitivity on mRNA and protein levels. Oxidative stress activated NF-E2-related factor 2 (Nrf2), c-Jun N-terminal kinase (JNK), c-Jun and nuclear factor κB (NF-κB), but TNC upregulation was only dependent on NF-κB activation. Pharmacological inhibition of inhibitor of NF-κB α (IκBα) phosphorylation as well as overexpression of IκBα prevented TNC, MMP9 and COX-2 induction, whereas the pro-inflammatory cytokine interleukin-1β (IL-1β) increased their expression levels. Analysis of the gene array data showed NF-κB binding sites for 64% of all upregulated genes, linking NF-κB with IH-dependent regulation of pro-metastatic gene expression in IBC cells. Our results provide a first link between intermittent hypoxia and pro-metastatic gene expression in IBC cells, revealing a putative novel mechanism for the high metastatic potential of IBC.
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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) | 0.002 | 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".