<p>The role of miR-382-5p in glioma cell proliferation, migration and invasion</p>
Bibliographic record
Abstract
Methods: Quantitative real time-PCR (qRT-PCR) was used to measure miR-382-5p levels in glioma tissues. The miR-382-5p mimics and inhibitors were employed to upregulate and downregulate miR-382-5p expression respectively in glioma cells. EdU assay was used to assess cell proliferation. Wound healing and Transwell assays were employed to evaluate cell migration and invasion. Western blot was used to measure the changes of epithelial-to-mesenchymal transition (EMT) markers and the potential miR-382-5p target genes. Results: We found that miR-382-5p levels were low in glioma tissues as determined by qRT-PCR. EdU assay showed that upregulation of miR-382-5p significantly decreased cell proliferation in both U87 and U251 cells. Wound healing rate was significantly decreased in response to miR-382-5p mimics and significantly increased in response to miR-382-5p inhibitors. Transwell migration assays further confirmed the inhibitory effects of miR-382-5p on the migration in both U251 and U87 cells. Transwell invasion assays showed that upregulation of miR-382-5p resulted in a remarkable decrease in the number of invading cells, whereas downregulation of miR-382-5p led to a significant increase in the numbers of invading U87 and U251 cells. In addition, overexpression of miR-382-5p decreased the protein levels of N-cadherin, Snail and Slug, and increased E-cadherin levels, in glioma cells. Furthermore, miR-382-5p levels negatively correlated with Y box-binding protein 1 (YBX1) in lower grade glioma tissues, and negatively regulated the expression of YBX1 in glioma cells. Conclusion: In summary, miR-382-5p inhibited proliferation, migration, invasion, and the EMT in glioma cells, possibly through targeting the oncogene YBX1. Keywords: miR-382, glioma, proliferation, migration, invasion
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".