Understanding How Hypoxia Alters the Breast Cancer Proteome in the Context of Molecular Subtypes and Metastatic Organotropism
Bibliographic record
Abstract
Breast cancer is a leading cause of cancer diagnosis and death in Canadian women, with >90% of deaths caused by metastasis. The current study explores how hypoxia affects cancer aggressiveness and metastatic potential across breast cancer cell lines representing different molecular subtypes and those that metastasize to different organs. Using liquid chromatography with tandem mass spectrometry (LC-MS/MS), comparison of normoxic and hypoxic proteomes of different breast cancer cell lines revealed changes to pro-survival and metastatic mechanisms contributing to subtype-associated aggressiveness. We also identified that extracellular exosomes and associated integrins are significantly upregulated components of the hypoxia response, suggesting their role in metastasis, especially to bone. Additionally, 8 clinically significant hypoxia-enriched proteins were identified specific to triple negative disease outcomes. Overall, hypoxia mediated subtype-specific aggressiveness and metastatic behavior, potentially via extracellular exosomes. This research offers insights into subtype-specific differences and identifies potential therapeutic opportunities to mitigate breast cancer metastasis in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".