A Tibetan Allele of EPAS1 as a Model for Investigating the Role of HIF-2α in Ewing Sarcoma
Bibliographic record
Abstract
Hypoxia is a hallmark symptom of solid tumors, including Ewing Sarcoma (EwS), which promotes tumor progression through a variety of complex biochemical and genetic pathways. The development of hypoxic tissues in tumors activates hypoxia inducible factors (HIF) which are believed to increase therapy resistance and metastasis. Endothelial Per-Arnt-Sim (PAS) domain-containing protein 1 (EPAS1) is known to encode an understudied HIF subunit called HIF-2α which may be a potential target for future therapies. A Tibetan population living in the Himalayan belt at high altitude has been shown to possess a Denisovan variation of the EPAS1 gene with reduced expression that contributes to their population’s hypoxia resistance. The relationship between thriving under hypoxic conditions and reduced expression of EPAS1 suggests this gene may be significantly involved in progressing hypoxic cancers. The objective of this study is to investigate the function of the EPAS1 gene under hypoxic conditions and its role in promoting cancer using the Tibetan allele as a convenient model for reduced expression. Three phases of experiments investigating tumor growth, therapy resistance, and metastasis will be performed using spheroid models and scratch wound analysis on EwS cells transfected with the Tibetan allele of EPAS1 (EPAS1t). It is expected that reduced EPAS1 expression will correlate to increased tumor growth, therapy resistance, and cell migration. These results would highlight EPAS1, and consequentially HIF-2α, as a biomarker for patient prognosis and a potential target for future therapies. Future studies may wish to expand this experiment by repeating the study with alternative cell lines, using animal models, or investigating the impacts of upregulated EPAS1 in hypoxic tumors.
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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 | 0.001 | 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 | 0.001 |
| 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".