Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme
Bibliographic record
Abstract
Proliferating embryonic and cancer cells preferentially use aerobic glycolysis to support growth, a metabolic alteration commonly referred to as the "Warburg effect." Here, we show that the glycolytic enzyme hexokinase 2 (HK2) is crucial for the Warburg effect in human glioblastoma multiforme (GBM), the most common malignant brain tumor. In contrast to normal brain and low-grade gliomas, which express predominantly HK1, GBMs show increased HK2 expression. HK2 expression correlates with worse overall survival of GBM patients. Depletion of HK2, but neither HK1 nor pyruvate kinase M2, in GBM cells restored oxidative glucose metabolism and increased sensitivity to cell death inducers such as radiation and temozolomide. Intracranial xenografts of HK2-depleted GBM cells showed decreased proliferation and angiogenesis, but increased invasion, as well as diminished expression of hypoxia inducible factor 1α and vascular endothelial growth factor. In contrast, exogenous HK2 expression in GBM cells led to increased proliferation, therapeutic resistance, and intracranial growth. Growth was dependent on both glucose phosphorylation and mitochondrial translocation mediated by AKT signaling, which is often aberrantly activated in GBMs. Collectively, these findings suggest that therapeutic strategies to modulate the Warburg effect, such as targeting of HK2, may interfere with growth and therapeutic sensitivity of some GBMs.
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 | 0.000 | 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".