Discovery of miRNA–mRNA regulatory networks in glioblastoma reveals novel insights into tumor microenvironment remodeling
Bibliographic record
Abstract
Adult glioblastoma (GBM) is a highly aggressive primary brain tumor, accounting for nearly half of all malignant brain tumors, with a median survival rate of only 8 months. Treatment for GBM is largely ineffective due to the highly invasive nature and complex tumor composition of this malignancy. MicroRNAs (miRNA) are short, non-coding RNAs that regulate gene expression by binding to messenger RNAs (mRNA). While specific miRNA have been associated with GBM, their precise roles in tumor development and progression remain unclear. In this study, the analysis of miRNA expression data from 743 adult GBM cases and 59 normal brain samples identified 94 downregulated miRNA and 115 upregulated miRNA. Many of these miRNA were previously linked to GBM pathology, confirming the robustness of our approach, while we also identified novel miRNA that may act as potential regulators in GBM. By integrating miRNA predictions with gene expression data, we were able to associate downregulated miRNA with tumor microenvironment factors, including extracellular matrix remodeling and signaling pathways involved in tumor initiation, while upregulated miRNA were found to be associated with essential neuronal processes. This analysis highlights the significance of miRNA in GBM and serves as a foundation for further investigation.
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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) | 0.000 | 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".