Long noncoding RNA H19, and not microRNA miR-326, is over-expressed and predicts survival in glioblastoma
Bibliographic record
Abstract
Glioblastoma (GB) represents the most common malignant brain tumor, which, despite extensive research, remains of poor prognosis. The focus of recent studies of GB pathogenesis has shifted to the study of the role of noncoding RNAs (ncRNAs). In this study, we examined the expression levels of the microRNA miR-326 and the long ncRNA H19 (on which a miR-326 putative binding site was found by in-silico analysis) in brain tumor tissue from GB patients as compared to cancer-free brain tissue. Relative expression levels of miR-326 were not found to be significantly altered in GB patients. By comparison, H19 was consistently over-expressed in all GB patients (p < 0.001), and correlated with poorer overall survival (OS) and progression-free survival (PFS) (p = 0.026 and p = 0.045, respectively). At a cutoff value of 5.27, H19 up-regulation could predict OS in GB patients, with a 71.4% sensitivity and 59.6% specificity (p = 0.026). The current GB patients were clustered by the multivariate analysis into 4 groups based on miR-326 and H19 expression profiles, age at diagnosis, and PFS. Our data suggest a role for H19 in the pathogenesis of GB and could be a potential prognostic biomarker for GB.
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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".