ReviewBriefings in bioinformatics2025
Decoding cancer heterogeneity through super-enhancer landscapes: from subtype discovery to therapeutic opportunity.
Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- MiRNAs in hyperglycemia-induced metabolic memory: established mechanisms and emerging nuclear activation concepts.Diabetology & metabolic syndrome · 2026Review
- 3D chromatin architecture in cancer: mechanisms of dysregulation and emerging therapeutic strategies.Experimental & molecular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Super-enhancers (SEs) are clusters of enhancers with potent regulatory capabilities. They play a crucial role in shaping cellular identity and driving the progression of various diseases, including cancer. SEs exhibit significant heterogeneity across different cell types and cancer subtypes. Analysis of SE landscapes can recapitulate existing classification systems and unveil novel SE-driven epigenetic subtypes. In this review, we summarized the latest advancements in cancer subtype identification based on SE-related characteristics, outlined the typical analytical workflows adopted in such studies, and explored the biological and clinical significance of SE-driven subtypes. Furthermore, we discussed the field's key challenges and emerging technologies to highlight future research directions. SE analysis provides a robust framework for dissecting cancer heterogeneity. This approach offers novel epigenetic perspectives and support for the realization of personalized medicine.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.