Gallein, G protein βγ subunits inhibitor, suppresses the TGF-α-induced migration of hepatocellular carcinoma cells via inhibition of the c-Jun N-terminal kinase
Bibliographic record
Abstract
G protein-coupled receptor (GPCR) signaling regulates a wide range of pathophysiological cell functions via G protein α and βγ subunits. Small molecules targeting the subunits of Gα and Gβγ have been developed as cancer therapeutics. We have previously reported that transforming growth factor-α (TGF-α) induces the migration of human hepatocellular carcinoma (HCC) HuH7 cells through the activation of AKT, p38 mitogen-activated protein kinase (MAPK), Rho-kinase and c-Jun N-terminal kinase (JNK). This study aims to determine whether Gβγ subunits regulate the TGF-α-induced migration of HCC HuH7 cells using gallein, a Gβγ subunits inhibitor. The Janus family of tyrosine kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) signaling pathway was also involved in the regulation of the migration. Gallein significantly reduced the TGF-α-induced cell migration. In contrast, fluorescein, a gallein-related compound that has no effect on Gβγ subunits, failed to affect the cell migration. Gallein suppressed the TGF-α-stimulated phosphorylation of JNK without affecting the phosphorylation of epidermal growth factor receptor, AKT, p38 MAPK, target protein of Rho-kinase and STAT3. Conversely, fluorescein did not attenuate the phosphorylation of JNK. These results strongly suggest that Gβγ subunits act as positive regulators in TGF-α-induced migration of HCC cells via the JNK signalling pathway.
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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 | 0.001 |
| 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".