Impact of Physiological Oxygen Levels on Cancer Cell Biology and Hypoxic Responses In Vitro
Bibliographic record
Abstract
Cancer cell culture has been historically done in incubators that do not regulate oxygen, exposing cells to near-atmospheric levels, around 18% O 2 . However, physiological oxygen levels in most human tissues range between 2–9% O 2 (physioxia). How supraphysiological oxygen levels in culture affects the behavior of cancer cells is poorly understood. In this study, we investigated how culturing various cancer cell lines in 5% (physioxia) or 18% O 2 affects different aspects of cancer biology in vitro. Using transcriptomics, we found that oxygen levels in culture had a robust effect on gene expression, but that this response was highly cell-line specific. A common feature among the cell lines used was increased expression of hypoxia-inducible factor (HIF) targets in physioxia. Further, we determined that PC-3 prostate cancer cells have higher proliferation and migration rates, elevated glucose consumption, and increased metabolic activity when cultured in physioxia than in 18% O 2 . We then evaluated how the baseline oxygen level affects the subsequent response to hypoxia in PC-3 cells, since O 2 levels in prostate tumors are below 1.5%. We found that the transcriptional response to hypoxia was highly dependent on the baseline oxygen condition. Cells cultured in 18% O 2 showed an enhanced induction of HIF targets when exposed to hypoxia, particularly genes involved in glucose metabolism. In comparison, hypoxia-mediated induction of HIF targets was milder in cells previously grown in physioxia. Consistent with this, hypoxia exposure increased glucose consumption and metabolic activity in cells previously grown in 18% O 2 but not in cells previously grown in physioxia. Collectively, our data suggest that many of the transcriptional and metabolic changes commonly associated with the response to hypoxia occur in the transition from 18% to 5% O 2 . We conclude that culture in physioxia modulates important cancer hallmarks in vitro, and that implementing physiological conditions in culture is critical for obtaining physiologically representative outcomes in cancer research. JAS: Natural Sciences and Engineering Research Council (NSERC) Discovery Grant. RA: Mitacs Globalink Graduate Fellowship. JEW: Ontario Graduate Scholarship. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".