AIF-regulated oxidative phosphorylation supports lung cancer development
Bibliographic record
Abstract
Cancer is a major and still increasing cause of death in humans. Most cancer cells have a fundamentally different metabolic profile from that of normal tissue. This shift away from mitochondrial ATP synthesis via oxidative phosphorylation towards a high rate of glycolysis, termed Warburg effect, has long been recognized as a paradigmatic hallmark of cancer, supporting the increased biosynthetic demands of tumor cells. Here we show that deletion of apoptosis-inducing factor (AIF) in a Kras G12D -driven mouse lung cancer model resulted in a marked survival advantage, with delayed tumor onset and decreased malignant progression. Mechanistically, Aif deletion leads to oxidative phosphorylation (OXPHOS) deficiency and a switch in cellular metabolism towards glycolysis in non-transformed pneumocytes and at early stages of tumor development. Paradoxically, although Aif -deficient cells exhibited a metabolic Warburg profile, this bioenergetic change resulted in a growth disadvantage of Kras G12D -driven as well as Kras wild-type lung cancer cells. Cell-autonomous re-expression of both wild-type and mutant AIF (displaying an intact mitochondrial, but abrogated apoptotic function) in Aif -knockout Kras G12D mice restored OXPHOS and reduced animal survival to the same level as AIF wild-type mice. In patients with non-small cell lung cancer, high AIF expression was associated with poor prognosis. These data show that AIF-regulated mitochondrial respiration and OXPHOS drive the progression of lung cancer.
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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 | 0.001 |
| 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".