Loss of the tumor suppressor LKB1 promotes metabolic reprogramming of cancer cells via HIF-1α
Bibliographic record
Abstract
One of the major metabolic changes associated with cellular transformation is enhanced nutrient utilization, which supports tumor progression by fueling both energy production and providing biosynthetic intermediates for growth. The liver kinase B1 (LKB1) is a serine/threonine kinase and tumor suppressor that couples bioenergetics to cell-growth control through regulation of mammalian target of rapamycin (mTOR) activity; however, the influence of LKB1 on tumor metabolism is not well defined. Here, we show that loss of LKB1 induces a progrowth metabolic program in proliferating cells. Cells lacking LKB1 display increased glucose and glutamine uptake and utilization, which support both cellular ATP levels and increased macromolecular biosynthesis. This LKB1-dependent reprogramming of cell metabolism is dependent on the hypoxia-inducible factor-1α (HIF-1α), which accumulates under normoxia in LKB1-deficient cells and is antagonized by inhibition of mTOR complex I signaling. Silencing HIF-1α reverses the metabolic advantages conferred by reduced LKB1 signaling and impairs the growth and survival of LKB1-deficient tumor cells under low-nutrient conditions. Together, our data implicate the tumor suppressor LKB1 as a central regulator of tumor metabolism and growth control through the regulation of HIF-1α-dependent metabolic reprogramming.
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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".