miRNA-296-5p functions as a potential tumor suppressor in human osteosarcoma by targeting SND1
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of osteosarcoma (OS) is still unclear, and it is still necessary to find new targets and drugs for anti-OS. This study aimed to investigate the role and mechanism of the anti-OS effects of miR-296-5p. METHODS: We measured the expression of miR-296-5p in human OS cell lines and tissues. The effect of miR-296-5p and its target gene staphylococcal nuclease and tudor domain containing 1 on proliferation, migration, and invasion of human OS lines was examined. The Student's t test was used for statistical analysis. RESULTS: We found that microRNA (miR)-296-5p was significantly downregulated in OS cell lines and tissues (control vs. OS, 1.802 ± 0.313 vs. 0.618 ± 0.235, t = 6.402, P < 0.01). Overexpression of miR-296-5p suppressed proliferation, migration, and invasion of OA cells. SND1 was identified as a target of miR-296-5p by bioinformatic analysis and dual-luciferase reporter assay. Overexpression of SND1 abrogated the effects induced by miR-296-5p upregulation (miRNA-296-5p vs. miRNA-296-5p + SND1, 0.294 ± 0.159 vs. 2.300 ± 0.277, t = 12.68, P = 0.003). CONCLUSION: Our study indicates that miR-296-5p may function as a tumor suppressor by targeting SND1 in OS.
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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".