HIF-1α Mediates Tumor Hypoxia to Confer a Perpetual Mesenchymal Phenotype for Malignant ProgressionA presentation from the Keystone Symposium on Epithelial Plasticity and Epithelial-to-Mesenchymal Transition, Vancouver, British Columbia, Canada, 21 to 26 January 2011.
Bibliographic record
Abstract
Although tumor progression involves genetic and epigenetic alterations to normal cellular biology, the underlying mechanisms of these changes remain obscure. Numerous studies have shown that hypoxia-inducible factor 1α (HIF-1α) is overexpressed in many human cancers and up-regulates a host of hypoxia-responsive genes for cancer growth and survival. We recently identified an alternative mechanism of HIF-1α function that induces genetic alterations by suppressing DNA repair. Here, we show that long-term hypoxia, which mimics the tumor microenvironment, drives a perpetual epithelial-mesenchymal transition (EMT) through up-regulation of the zinc finger E-box binding homeobox protein ZEB2, whereas short-term hypoxia induces a reversible EMT that requires the transcription factor Twist1. Moreover, we show that the perpetual EMT driven by chronic hypoxia depends on HIF-1α induction of genetic alterations rather than its canonical transcriptional activator function. These mesenchymal tumor cells not only acquire tumorigenicity but also display characteristics of advanced cancers, including necrosis, aggressive invasion, and metastasis. Hence, these results reveal a mechanism by which HIF-1α promotes a perpetual mesenchymal phenotype, thereby advancing tumor progression.
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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) | 0.004 | 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".