MR-05 * GLIOMA STEM CELL SPECIFIC microRNA-mRNA INTERACTION NETWORK
Bibliographic record
Abstract
microRNAs have been shown to have oncogenic or tumor suppressor function in glioblastoma (GBM). It has been postulated that there exists an extensive microRNA-mediated RNA-RNA interaction network in GBMs utilizing systems biology approach supporting a competitive endogenous RNA (ce-RNA). MicroRNAs have functional relevance in the regulation of critical genes and pathways implicated in the maintenance of glioma stem cell (GSC) properties. To address this, we have applied biochemical methods to establish direct miRNA-mRNA interaction network relevant and specific to GSCs. To avoid inclusion of the inherent bias of miRNA-target prediction algorithms, we have generated an unbiased global miRNA mediated RNA-RNA interactome by performing RNA-sequencing all RNA species (small and large RNAs) isolated from AGO2-microRNA-induced silencing complex (miRISC) of GSCs and normal human neural stem cells (hNSCs). Additionally, we have also established this interactome after exposure of GSCs and normal hNSCs to hypoxia, a key tumor micro-environmental factor that is known to be pivotal in generating GBM heterogeneity. In all, three independent GSC lines and one NSC line were profiled, and results compared with each other. miRNA-mRNA interaction nodes were determined by RNA read counts from RNA-seq data and combinations of miRNA target prediction softwares. The rank order list of miRNA-mRNA interaction nodes generated from RNA sequence reads reveals that enrichment of specific RNAs in functional AGO2-miRISC is not a direct function of their relative abundance in cells, thus this biochemically generated interactome is distinct from that generated by bioinformatics tools. Our data shows that MYC as one of the key networks targetted by microRNAs specifically in GSCs under hypoxic conditions. We demonstrate that scope and influence of GSC specific miRNA-mRNA network and specific nodes of this interactome varies with hypoxia and tumor region in GBMs
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".