Glutaminase Reprogramming in Hepatocellular Carcinoma: Implications for Diagnosis, Prognosis, and Potential as a Novel Therapeutic Target
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a poor prognosis due to late diagnosis, limited curative therapies, and underlying liver disease. Glutamine metabolism, a crucial pathway in cancer, remains poorly understood in HCC, which develops in an already metabolically dynamic organ. This study aimed to characterize glutamine metabolism in HCC. Glutamine metabolism in HCC was explored through in vitro analysis of neoplastic characteristics, experimental hepatocarcinogenesis in C57BL/6 mice, and examination of liver samples from patients with HCC, cirrhosis, and non-diseased liver. The evaluation included metabolite abundance and mRNA/protein expressions. In mice, tumors exhibited hyperactive glutaminolysis compared to adjacent tissue. Notably, glutaminase expression shifted from the liver isoform (GLS2) in normal and cirrhotic livers to the kidney isoform (GLS1) in HCC. In samples from patients, HCC tumors showed overexpression of glutamine synthetase and GLS1 along with a loss of GLS2 expression, providing excellent discrimination of HCC lesions from cirrhotic and normal liver samples. Inhibiting GLS1 with CB-839 significantly impacted glutamine metabolism in HCC cells while showing limited activity on normal hepatocytes. HCC tumors show reprogramming of GLS2 to GLS1, with a concomitant increase in glutamine synthetase. These characteristics can discriminate HCC from cirrhotic and normal liver tissues. Overexpressed GLS1 and loss of GLS2 within tumors convey an unfavorable prognosis in patients with HCC. Pharmacological inhibition of GLS1 in HCC cells successfully harnesses glutamine metabolism, representing an attractive target for novel therapeutic approaches.
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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 | 0.001 | 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".