The role of super-enhancer-driven lncRNAs in cancer
Bibliographic record
Abstract
In recent years, the global burden of cancer has grown substantially, yet available treatments and clinical outcomes remain inadequate. Accordingly, research into novel therapeutic targets for cancer has become a major focus. Super-enhancers (SEs), which are clusters of multiple enhancers, represent essential epigenetic oncogenic factors that are critical for maintaining cancer cell identity. Moreover, SE-driven long non-coding RNAs (lncRNAs) play a crucial regulatory role in tumor initiation and progression. Targeting cancer-specific SE-driven lncRNAs can slow tumor development and offer a novel strategy for cancer treatment. This review first outlines the characteristics of SEs, including their relevance to phase separation (PS) and the core transcriptional regulatory circuitry (CRC). It then describes the fundamental characteristics, intracellular localization, and functions of SE-driven lncRNAs, with emphasis on the analytical methods for these lncRNAs and their roles in tumors. Finally, the review highlights the clinical applications of existing SE inhibitors in oncology and their potential for targeting SE-driven lncRNAs, aiming to advance translational research in this field.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".