5-Azacytidine Downregulates the Proliferation and Migration of Hepatocellular Carcinoma Cells In Vitro and In Vivo by Targeting miR-139-5p/ROCK2 Pathway
Bibliographic record
Abstract
Background: For hepatocellular carcinoma (HCC), effective therapeutic approaches are lacking. As aberrant gene methylation is a major contributor to HCC development, demethylating drugs such as 5-azacytidine (5-Aza) have been proposed. As most 5-Aza mechanisms of action are unknown, we investigated its phenotypic/molecular effects. Methods: 5-Aza effects were examined in the human HCC cell lines JHH-6/HuH-7 and in the rat cell-line N1-S1. We also employed a xenograft mouse model (HuH-7), a zebrafish model (JHH-6), and an orthotopic syngeneic rat model (N1-S1) of HCC. Results: 5-Aza downregulated cell viability/growth/migration/adhesion by upregulating miR-139-5p, which in turn downregulated ROCK2/cyclin D1/E2F1 and increased p27kip1, resulting in G1/G0 cell accumulation. Moreover, a decrease in cyclin B1 and an increase in p27kip1 led to G2/M accumulation. Finally, we observed a decrease in MMP-2 levels, a stimulator of HCC cell migration. Aza effects were confirmed in the mouse model; in the zebrafish model, we also demonstrated the downregulation of tumor neo-angiogenesis, and in the orthotopic rat model, we observed impaired N1-S1 grafting in a healthy liver. Conclusion: We demonstrate for the first time that 5-Aza can impair HCC development via upregulation of miR-139-5p, which in turn impairs the ROCK2/cyclin D1/E2F1/cyclin B1 pro-proliferative pathway and the ROCK2/MMP-2 pro-migratory pathway. Thus, we provide novel information about 5-Aza mechanisms of action and deepen the knowledge about the crosstalk among ROCK2/cyclin D1/E2F1/cyclin B1/p27kip1/MMP-2 in HCC.
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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) | 0.002 | 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".