<i>CASCADES</i>, a novel <i>SOX2</i> super‐enhancer‐associated long noncoding <scp>RNA</scp>, regulates cancer stem cell specification and differentiation in glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most common primary malignant brain tumor in adults, with a median survival of just over 1 year. The failure of available treatments to achieve remission in patients with glioblastoma (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 long noncoding RNA (lncRNA), cancer stem cell-associated distal enhancer of SOX2 (CASCADES), that functions as an epigenetic regulator in glioma CSCs (GSCs). CASCADES is expressed in isocitrate dehydrogenase (IDH)-wild-type GBM and is 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 that represents a novel epigenetic and therapeutic target for disrupting the CSC compartment in glioblastoma.
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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".