CBIO-22DICER AND miRNAs REGULATE GLIOMA STEM CELL CHARACTERISTICS
Bibliographic record
Abstract
Deregulation of microRNA expression is common in a variety of malignancies, including glioma. Several studies have demonstrated the role of miRNAs in regulating glioma stem cell (GSC) properties. In addition, DICER, which regulates the processing of precursor miRNAs to the mature double-stranded form, is down-regulated in multiple forms of cancer. To determine the role of DICER and miRNA regulation in the development and progression of glioblastoma, we investigated the link between miRNA deregulation and GSC characteristics. Our in vitro studies using GSC 7-2 and U251 glioma cell line demonstrated that decreased DICER expression by shRNA treatment results in a global decrease in expression of miRNAs, the majority of which are tumor suppressor miRNAs such as the let-7 family of miRNAs. DICER knockdown increased proliferation of GSCs, while their “stemness” properties, such as their ability to form neurospheres and the levels of stem cell markers such as Sox2 and Bmi1, decreased upon shDicer treatment. Intracranial injection of GSC 7-2 and U251 cells treated with shDicer resulted in decreased overall survival in xenografted mice. Further analysis of the tumors isolated from these mice demonstrated that DICER knockdown results in a relative increase in tumor cell proliferation, as shown by increased ki-67 labeling (30% Ki67 positive nuclei in shDicer versus 15% in shControl tumors), a decrease in expression of Sox2 (stemness marker; 13% in shDicer versus 31% in shControl tumors), and increase in expression of GFAP (astrocytic differentiation marker). Analysis of data from the Cancer Genome Atlas (TCGA) database suggests that high Dicer expression level is correlated with better prognosis of GBM patients and this supports our in vivo data. Our results highlight the role of DICER and miRNAs as potential regulators of GSC proliferation, stem- versus progenitor-like state, and their potential link to development or progression of glioma tumors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".