Metabolic reprogramming enables hepatocarcinoma cells to efficiently adapt and survive to a nutrient-restricted microenvironment
Why this work is in the frame
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Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a metabolically heterogeneous cancer and the use of glucose by HCC cells could impact their tumorigenicity. Dt81Hepa1-6 cells display enhanced tumorigenicity compared to parental Hepa1-6 cells. This increased tumorigenicity could be explained by a metabolic adaptation to more restrictive microenvironments. When cultured at high glucose concentrations, Dt81Hepa1-6 displayed an increased ability to uptake glucose (P<0.001), increased expression of 9 glycolytic genes, greater GTP and ATP (P<0.001), increased expression of 7 fatty acid synthesis-related genes (P<0.01) and higher levels of Acetyl-CoA, Citrate and Malonyl-CoA (P<0.05). Under glucose-restricted conditions, Dt81Hepa1-6 used their stored fatty acids with increased expression of fatty acid oxidation-related genes (P<0.01), decreased triglyceride content (P<0.05) and higher levels of GTP and ATP (P<0.01) leading to improved proliferation (P<0.05). Inhibition of lactate dehydrogenase and aerobic glycolysis with sodium oxamate led to decreased expression of glycolytic genes, reduced lactate, GTP and ATP levels (P<0.01), increased cell doubling time (P<0.001) and reduced fatty acid synthesis. When combined with cisplatin, this inhibition led to lower cell viability and proliferation (P<0.05). This metabolic-induced tumorigenicity was also reflected in human Huh7 cells by a higher glucose uptake and proliferative capacity compared to HepG2 cells (P<0.05). In HCC patients, increased tumoral expression of Glut-1, Hexokinase II and Lactate dehydrogenase correlated with poor survival (P = 2.47E−5, P = 0.016 and P = 6.58E−5). In conclusion, HCC tumorigenicity can stem from a metabolic plasticity allowing them to thrive in a broader range of glucose concentrations. In HCC, combining glycolytic inhibitors with conventional chemotherapy could lead to improved treatment efficacy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it