Reprogramming of Glutamine Amino Acid Transporters Expression and Prognostic Significance in Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy and is a major cause of cancer-related mortality in the world. This study aimed to characterize glutamine amino acid transporter expression profiles in HCC compared to those of normal liver cells. In vitro and in vivo models of HCC were studied using qPCR, whereas the prognostic significance of glutamine transporter expression levels within patient tumors was analyzed through RNAseq. Solute carrier (SLC) 1A5 and SLC38A2 were targeted through siRNA or gamma-p-nitroanilide (GPNA). HCC cells depended on exogenous glutamine for optimal survival and growth. Murine HCC cells showed superior glutamine uptake rate than normal hepatocytes (p < 0.0001). HCC manifested a global reprogramming of glutamine transporters compared to normal liver: SLC38A3 levels decreased, whereas SLC38A1, SLC7A6, and SLC1A5 levels increased. Also, decreased SLC6A14 and SLC38A3 levels or increased SLC38A1, SLC7A6, and SLC1A5 levels predicted worse survival outcomes (all p < 0.05). Knockdown of SLC1A5 and/or SLC38A2 expression in human Huh7 and Hep3B HCC cells, as well as GPNA-mediated inhibition, significantly decreased the uptake of glutamine; combined SLC1A5 and SLC38A2 targeting had the most considerable impact (all p < 0.05). This study revealed glutamine transporter reprogramming as a novel hallmark of HCC and that such expression profiles are clinically significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".