microRNA-622 acts as a tumor suppressor in hepatocellular carcinoma
Bibliographic record
Abstract
microRNAs (miRNAs) are important regulators of tumor development and progression. In this study, we aimed to explore the expression and role of miR-622 in hepatocellular carcinoma (HCC). We found that miR-622 was significantly downregulated in human HCC specimens compared to adjacent noncancerous liver tissues. miR-622 downregulation was significantly associated with aggressive parameters and poor prognosis in HCC. Enforced expression of miR-622 significantly decreased the proliferation and colony formation and induced apoptosis of HCC cells. In vivo studies demonstrated that miR-622 overexpression retarded the growth of HCC xenograft tumors. Bioinformatic analysis and luciferase reporter assays revealed that miR-622 directly targeted the 3'-untranslated region (UTR) of mitogen-activated protein 4 kinase 4 (MAP4K4) mRNA. Ectopic expression of miR-622 led to a significant reduction of MAP4K4 expression in HCC cells and xenograft tumors. Overexpression of MAP4K4 partially restored cell proliferation and colony formation and reversed the induction of apoptosis in miR-622-overexpressing HCC cells. Inhibition of JNK and NF-κB signaling phenocopied the anticancer effects of miR-622 on HCC cells. Taken together, miR-622 acts as a tumor suppressor in HCC and restoration of miR-622 may provide therapeutic benefits in the treatment of 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".