MiR-130b-3p Suppress the Migration, Proliferation and Chemosensitization of Hepatocellular Carcinoma Cells
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers globally, yet its pathogenesis remains incompletely understood. Among the various mechanisms contributing to HCC development, small RNAs, such as microRNAs (miRNAs), play a significant role. miRNAs are non-coding RNAs, typically 20-30 nucleotides long, that regulate gene transcription by binding to RNAs, affecting downstream signaling pathways. One such miRNA, hsa-miR-130b-3p, has been associated with cancer development, including HCC, although the full extent of its involvement remains unclear. This study aimed to explore the link between hsa-miR-130b-3p and HCC using bioinformatics analyses and in vitro assays. Publicly available databases were utilized for expression profiling, mRNA and lncRNA target prediction, pathway enrichment, and methylation analysis. In vitro experiments were conducted using a hsa-miR-130b-3p inhibitor in HepG2 cells to assess its effects on proliferation, migration, and oxaliplatin sensitivity. Our findings show that hsa-miR-130b-3p is upregulated in multiple cancers, including HCC, targeting cancer-related genes and interacting with various lncRNAs. Inhibition of hsa-miR-130b-3p reduced cancer cell proliferation and migration, while enhancing drug sensitivity to oxaliplatin. These results suggest that hsa-miR-130b-3p may play a role in HCC pathogenesis, but further studies are required to fully understand its mechanisms.
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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) | 0.002 | 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".