<i>CASCADES</i> , a novel <i>SOX2</i> super-enhancer associated long noncoding RNA, regulates cancer stem cell specification and differentiation in glioblastoma multiforme
Bibliographic record
Abstract
ABSTRACT Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults, with a median survival of just over one year. The failure of available treatments to achieve remission in patients with GBM has been attributed to the presence of cancer stem cells (CSCs), which are thought to play a central role in tumor development and progression and serve as a treatment-resistant cell repository capable of driving tumor recurrence; in fact, the property of “stemness” itself may be responsible for treatment resistance. In this study, we identify a novel lncRNA, Cancer stem cell associated distal enhancer of SOX2 ( CASCADES ) that functions as an epigenetic regulator in glioma CSCs (GSCs). CASCADES is expressed in IDH-wild type GBM and significantly enriched in GSCs. Knockdown of CASCADES in GSCs results in differentiation towards a neuronal lineage in a cell- and cancer-specific manner. Bioinformatics analysis reveals that CASCADES functions as a super-enhancer associated lncRNA epigenetic regulator of SOX2 . Our findings identify CASCADES as a critical regulator of stemness in GSCs and represent a novel epigenetic and therapeutic target for disrupting the cancer stem cell compartment in GBM.
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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) | 0.002 | 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".