miRNAs as important drivers of glioblastomas: A no-brainer?
Bibliographic record
Abstract
There is no debate on the relevance of miRNAs in the pathogenesis of cancer. Numerous miRNAs with oncogenic and tumor-suppressive properties have been identified in glioblastoma multiforme (GBM), an aggressive type of brain tumor with dismal prognosis. Differential expression of these biomolecules in several cancer models makes them attractive therapeutic targets for the development of miRNA-based cancer treatments despite the hurdles associated with such an approach. In addition, systemic release of miRNAs also positions them as attractive tools for non-invasive cancer diagnosis and prognosis. This review initially looks at differentially expressed miRNAs in GBMs. Our focus will next be directed towards circulating miRNAs and how these molecules could be leveraged for cancer diagnosis as well as for the assessment of patient response to chemotherapeutic treatments. Finally, we discuss the primary strategies utilized in the development of miRNA-focused therapeutics and summarize preclinical results gathered in GBMs to date.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".