Integrated microRNA Analysis Identifies miR-512-3p as a Potential Biomarker of Poor Outcome in Pediatric Medulloblastoma
Bibliographic record
Abstract
Abstract Background: Medulloblastoma, a genetically heterogeneous tumor, is the most frequent malignant brain tumor in children. Although several studies have been carried out, the molecular mechanism underlying medulloblastoma tumorigenesis is not completely known. microRNA (miRNA) expression profiles have been associated with development, progression, and prognosis of human cancers, including medulloblastoma. However, the role of miRNAs in pediatric medulloblastoma has been poorly explored.Methods: Global miRNA expression in 24 microdissected medulloblastoma specimens (19 pediatric and 5 adult specimens) was evaluated by microarray assay. miR-512-3p, the most differentially expressed miRNA in these two groups, was analyzed by qRT-PCR in a cohort of 51 consecutive pediatric medulloblastoma samples and 7 pediatric non-neoplastic cerebellum control samples, and its clinical significance was assessed. Further in silico miRNA prediction of target genes was performed with bioinformatics tools.Results: Compared to the controls, miR-512-3p was significantly downregulated in the pediatric medulloblastoma samples. Higher miR-512-3p was associated with incomplete degree of resection, high risk group classification, and poor overall survival. In silico analysis in an independent cohort of medulloblastoma identified that some of the miR-512-3p target genes (SMAD9, SSX2IP, MAPK10, PTCH1, CCDC6, and BMPR2) were statistically correlated with overall survival, metastasis, and death.Conclusions: For the first time, our results have shown that miR-512-3p is significantly associated with poor clinical outcome in pediatric medulloblastoma, suggesting that miR-512-3p is a potential biomarker of prognosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".