Targeting transglutaminase 2 mediated exostosin glycosyltransferase 1 signaling in liver cancer stem cells with acyclic retinoid
Bibliographic record
Abstract
Abstract Transglutaminase 2 (TG2) is a multifunctional protein that promotes or suppresses tumorigenesis, depending on intracellular location and conformational structure. Acyclic retinoid (ACR) is an orally administered vitamin A derivative that prevents hepatocellular carcinoma (HCC) recurrence by targeting liver cancer stem cells (CSCs). In this study, we examined the subcellular location-dependent effects of ACR on TG2 activity at a structural level and characterized the functional role of TG2 and its downstream molecular mechanism in the selective depletion of liver CSCs. A binding assay with high-performance magnetic nanobeads and structural dynamic analysis with native gel electrophoresis and size-exclusion chromatography-coupled multi-angle light scattering or small-angle X-ray scattering showed that ACR binds directly to TG2, induces oligomer formation of TG2, and inhibits the transamidase activity of cytoplasmic TG2 in HCC cells. The loss-of-function of TG2 suppressed the expression of stemness-related genes, spheroid proliferation and selectively induced cell death in an EpCAM+ liver CSC subpopulation in HCC cells. Proteome analysis revealed that TG2 inhibition suppressed the gene and protein expression of exostosin glycosyltransferase 1 (EXT1) and heparan sulfate biosynthesis in HCC cells. In contrast, high levels of ACR increased intracellular Ca2+ concentrations along with an increase in apoptotic cells, which probably contributed to the enhanced transamidase activity of nuclear TG2. This study demonstrates that ACR could act as a novel TG2 inhibitor; TG2-mediated EXT1 signaling is a promising therapeutic target in the prevention of HCC by disrupting liver CSCs.
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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".