Hypoxia Promotes Stem Cell Phenotypes, Resistance to Therapy and Poor Prognosis through Epigenetic Regulation of DICER
Bibliographic record
Abstract
Hypoxia is a common characteristic of solid tumors, associated with aggressive disease and poor prognosis in multiple sites, including breast and cervix cancer. These properties are attributed to the influence of hypoxia on treatment resistance to both radiotherapy and chemotherapy as well as biological responses that promote altered tumor biology. Extensive research has led to a good understanding of the mechanisms of cellular adaptation to hypoxia, and regulation of metabolic and angiogenic phenotypes. However, considerably less is known regarding mechanisms responsible for the influence of hypoxia on metastasis and stemness. In a search to identify novel genes that contribute to the cellular response during hypoxia, we discovered that hypoxia represses DICER, a key enzyme required for generation of mature miRNA. DICER is a known haplo-insufficient tumor suppressor that can stimulate cancer development through a reduction in miRNA generation. We demonstrate that tumor hypoxia is a major regulator of DICER expression in large cohorts of breast cancer patients and that DICER expression is suppressed by hypoxia to levels similar to that in tumors with monoallelic DICER loss. We also demonstrate that DICER repression occurs through a novel epigenetic mechanism requiring inhibition of the oxygen-dependent H3K27me3 demethylases KDM6A/B. Hypoxic suppression of DICER creates a miRNA processing defect and results in selective reduction of mature levels of the miR-200 family. Consequently, hypoxia leads to derepression of the miR-200 target ZEB1, stimulates the epithelial to mesenchymal transition and results in acquisition of stem cell phenotypes in human mammary epithelial cells. Finally, we demonstrate the importance of reduced DICER in mediating therapeutic response to fractionated radiation treatment in vivo. Our work suggests that in breast cancer the acquisition of EMT and stem cell properties, through epigenetic repression of DICER and loss of miR-200, may drive the known association of hypoxia and poor clinical outcome.
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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) | 0.001 | 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".