Hypoxia-inducible factors: A target of cancer treatment
Bibliographic record
Abstract
Hypoxia, a characteristic of the tumor microenvironment caused by abnormal blood vessels and rapid cellular growth, enhances tumor aggressiveness and leads to resistance against conventional therapies. Unlike normal cells, hypoxic tumor cells activate adaptive survival mechanisms, prominently mediated by hypoxia-inducible factors (HIFs). HIF-1α is the most studied member of the HIF family, and the stability of its alpha subunit (HIF-1α) is a crucial determinant of the overall activity of the HIF-1α complex. HIF-1α stabilization under low oxygen occurs via oxygen-dependent and oxygen-independent pathways: in the oxygen-dependent pathway, HIf-1α is normally degraded by the von Hippel–Lindau protein (pVHL) when oxygen is present. Under hypoxia, hydroxylation is inhibited, allowing HIF-1α to accumulate. In the oxygen-independent pathway, growth factor signals activate cascades like PI3K/Akt/mTOR and MAPK/ERK, stabilizing HIF-1α regardless of oxygen levels. Stabilized HIF-1α translocates to the nucleus, promoting transcription of proangiogenic genes such as vascular endothelial growth factor (VEGF), thereby facilitating angiogenesis, tumor invasion, and progression. Dysregulation of these signaling pathways underpins the pathogenesis of many cancers, making HIF and its associated cascades critical targets for innovative cancer therapies. This review focuses on the pivotal role of HIF in tumor angiogenesis and emphasizes the therapeutic potential of targeting HIF signaling in cancer treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".