PINK1 Is a Negative Regulator of Growth and the Warburg Effect in Glioblastoma
Bibliographic record
Abstract
Proliferating cancer cells are characterized by high rates of glycolysis, lactate production, and altered mitochondrial metabolism. This metabolic reprogramming provides important metabolites for proliferation of tumor cells, including glioblastoma. These biological processes, however, generate oxidative stress that must be balanced through detoxification of reactive oxygen species (ROS). Using an unbiased retroviral loss-of-function screen in nontransformed human astrocytes, we demonstrate that mitochondrial PTEN-induced kinase 1 (PINK1) is a regulator of the Warburg effect and negative regulator of glioblastoma growth. We report that loss of PINK1 contributes to the Warburg effect through ROS-dependent stabilization of hypoxia-inducible factor-1A and reduced pyruvate kinase muscle isozyme 2 activity, both key regulators of aerobic glycolysis. Mechanistically, PINK1 suppresses ROS and tumor growth through FOXO3a, a master regulator of oxidative stress and superoxide dismutase 2. These findings highlight the importance of PINK1 and ROS balance in normal and tumor cells. PINK1 loss was observed in a significant number of human brain tumors including glioblastoma (n > 900) and correlated with poor patient survival. PINK1 overexpression attenuates in vivo glioblastoma growth in orthotopic mouse xenograft models and a transgenic glioblastoma model in Drosophila Cancer Res; 76(16); 4708-19. ©2016 AACR.
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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".