MR-04 * DICER REGULATES GLIOMA STEM CELL STATE
Bibliographic record
Abstract
The role of cancer stem cells in tumor formation and tumor heterogeneity is currently one of the most researched topics in cancer biology, and microRNAs likely have functional relevance in regulation of critical genes and parameters implicated in glioma stem cell (GSC) behavior and differentiation. To address this, we investigated global role of microRNA in GSCs, focusing on DICER, which regulates double-stranded RNA processing for microRNA biogenesis. Analysis of data from the Cancer Genome Atlas (TCGA) database suggests that high Dicer expression level is correlated with better prognosis of GBM patients. Gene signatures correlated with DICER expression levels suggest DICER/miRNA mediated mechanisms potentially regulate a multitude of cellular pathways in GBMs. Immunohistochemistry analysis of GBM tissue microarray reveals that of 54 tumor samples, 26 (48%) of GBM patients have low or undetectable levels of DICER1 protein, indicating frequent inactivation in high grade glioma. To characterize this functionally, we utilized various in vitro approaches, including exposure to hypoxia, to characterize the GSC properties after knockdown of DICER (GSCs did not have significant variation in endogenous DICER levels). Using three different GSC lines, we found in all cases with Dicer knockdown resulted in increased proliferation of three independent GSC lines with concomitant decrease in levels of stem cell markers (Sox2, Bmi1 etc.), a phenotype observed even upon exposure to hypoxia, a state that is known to preserve and promote the stem-like cell phenotype. Results from self-renewal assays as well as expression profiling show that GSCs with depleted levels of Dicer lose stem cell characteristics and acquire a progenitor-cell like state. Our results highlight the role of DICER as potential regulators of GSC stem-like versus progenitor-like state.
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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) | 0.003 | 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".