STEM-15LONG NONCODING RNA REGULATION OF THE CANCER STEM CELL PHENOTYPE IN GLIOBLASTOMA MULTIFORME
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults, with a two-year survival rate of less than 25%. The recurrence of GBM has been attributed to the presence of glioma stem cells (GSC), which are thought to play a central role in tumor development and progression. Long noncoding RNAs (lncRNAs) have been suggested to play a role in maintaining pluripotency, self-renewal, and differentiation in embryonic stem cells (ESCs). We hypothesized that lncRNAs functionally contribute to GBM development and tumor propagation by maintaining the cancer stem cell phenotype in glioblastoma. Initially, an in silico “nearest-neighbor” approach was employed to identify 112 lncRNA candidates that were close to the transcription factors that have been implicated in regulating self-renewal and pluripotency of ESCs or iPSCs, as well as factors that have been used to reprogram GSCs. Based on further in silico analyses and in vitro studies, we have identified three novel long noncoding RNAs, lincSox2, lincPOU5F1, and lincCTNNB1 that show differential expression in stem vs. differentiated normal human fetal neural stem cells (NSCs) and GSCs. The expression of lincSox2 and lincPOU5F1 significantly decreased in differentiated human NSCs compared to controls, and were significantly increased in differentiated GSCs compared to control GSCs. In comparison, the expression of lincCTNNB1 was increased in all differentiated cells, compared to their corresponding stem cell controls. Further knockdown experiments followed by in vivo studies will provide insight into functional relevance of these candidates in maintaining “stemness” in GSCs. Based on in silico and in vitro studies, we have identified two novel long noncoding RNAs that show differential expression in stem vs. differentiated NSCs and GSCs, and may functionally contribute to glioma biology by regulating the cancer stem cell phenotype. However, further characterization is needed to fully understand the role of lncRNAs in glioblastoma multiforme.
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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) | 0.001 | 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".