Prognostic relevance of miR‐124‐3p and its target <i>TP53INP1</i> in pediatric ependymoma
Bibliographic record
Abstract
Ependymoma is a malignant pediatric brain tumor, often incurable under the current treatment regimen. We aimed to evaluate the expression of microRNAs (miRs) in pediatric ependymoma tumors in an attempt to identify prognostic molecular markers which would lead to potential therapeutic targets. Following miR-array expression analysis, we focused on 9 miRs that correlated with relapse which were further validated by quantitative real-time PCR (qRT-PCR) in a cohort of 67 patients. Western blotting and immunohistochemistry were used to measure target protein expression in 20 and 34 tumor samples, respectively. High expression of miR-124-3p significantly correlated with the lower progression-free survival (PFS) of 16% compared to 67% in those expressing low levels (P = .002). Interestingly, in the group of patients with local disease (n = 56) expression levels of this miR distinguished 2 subgroups with a significantly different outcome (P = .001). miR-124-3p was identified as an independent prognostic factor of relapse in the multivariate analysis performed in the whole cohort and in the group with localized disease. In the localized group, a patient expressing high levels of miR-124-3p had a 4.1-fold increased risk for relapse (P = .005). We demonstrated the direct binding of miR-124-3p to its target TP53INP1. Negative TP53INP1 protein levels correlated with a poor outcome (P = .034). We propose miR-124-3p and TP53INP1 as new biomarkers for prognostic stratification that may be possible therapeutic targets for ependymoma.
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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) | 0.000 | 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".