Metabolic reprogramming enables hepatocarcinoma cells to efficiently adapt and survive to a nutrient-restricted microenvironment
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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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".