Epigenetic silencing of miRNA-9 is associated with HES1 oncogenic activity and poor prognosis of medulloblastoma
Bibliographic record
Abstract
BACKGROUND: microRNA-9 is a key regulator of neuronal development aberrantly expressed in brain malignancies, including medulloblastoma. The mechanisms by which microRNA-9 contributes to medulloblastoma pathogenesis remain unclear, and factors that regulate this process have not been delineated. METHODS: Expression and methylation status of microRNA-9 in medulloblastoma cell lines and primary samples were analysed. The association of microRNA-9 expression with medulloblastoma patients' clinical outcome was assessed, and the impact of microRNA-9 restoration was functionally validated in medulloblastoma cells. RESULTS: microRNA-9 expression is repressed in a large subset of MB samples compared with normal fetal cerebellum. Low microRNA-9 expression correlates significantly with the diagnosis of unfavourable histopathological variants and with poor clinical outcome. microRNA-9 silencing occurs via cancer-specific CpG island hypermethylation. HES1 was identified as a direct target of microRNA-9 in medulloblastoma, and restoration of microRNA-9 was shown to trigger cell cycle arrest, to inhibit clonal growth and to promote medulloblastoma cell differentiation. CONCLUSIONS: microRNA-9 is a methylation-silenced tumour suppressor that could be a potential candidate predictive marker for poor prognosis of medulloblastoma. Loss of microRNA-9 may confer a proliferative advantage to tumour cells, and it could possibly contribute to disease pathogenesis. Thus, re-expression of microRNA-9 may constitute a novel epigenetic regulation strategy against medulloblastoma.
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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 | 0.000 | 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 | 0.000 |
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".