Abstract 2620 Culture in Physiological Oxygen and Plasma-Like Media Modulate Breast Cancer Cell Hallmarks
Bibliographic record
Abstract
Standard cell culture conditions often fail to replicate the in vivo microenvironment, limiting the reproducibility and physiological relevance of experimental findings.For example, commonly used media such as DMEM contain supraphysiological levels of key metabolites like glucose while lacking others entirely.Additionally, standard cell culture incubators maintain near-atmospheric oxygen levels (18% O2), far exceeding the physiological oxygen levels (2-9% O2) typical of most tissues.Despite their significance, these factors are often studied independently.Here, we investigated the effects of both media composition and oxygen levels on breast cancer cell physiology.Using MCF7 cells cultured in 18% O2 or 5% O2 (physioxia) and in either DMEM or Plasmax-a physiological medium modeled after the human plasma metabolome-we performed transcriptomic, proteomic, and functional analyses.RNA sequencing revealed oxygen-and media-dependent changes in gene expression, particularly in pathways related to cell cycle regulation, metabolism, and redox homeostasis.While the overlap between differentially expressed genes and proteins was low, pathways related to proliferation and energy metabolism where also enriched in the proteomic analysis.Consequently, functional assays revealed differences in proliferation, migration, metabolic activity, glucose consumption, and reactive oxygen species production driven by different culture conditions.Generally, physiological culture conditions promoted proliferation, migration, and metabolic activity in MFC7 cells.Collectively, our results demonstrate that both oxygen levels and media composition profoundly influence cancer cell behavior, underscoring the necessity of implementing physiologically relevant culture conditions in in vitro cancer research.
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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) | 0.003 | 0.001 |
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".