Super-enhancers (SEs) are regions of the genome that play a crucial regulatory role in gene expression by promoting large-scale transcriptional responses in various cell types and tissues. Recent research suggests that alterations in super-enhancer activity can contribute to the development and progression of various disorders. The aim of this research is to explore the multifaceted roles of super-enhancers in gene regulation and their significant implications for understanding and treating complex diseases. Here, we study and summarise the classification of super-enhancer constituents, their possible modes of interaction, and cross-regulation, including super-enhancer RNAs (seRNAs). We try to investigate the opportunity of SE dynamics prediction based on the hierarchy of enhancer single elements (enhancers) and their aggregated action. To further our understanding, we conducted an in silico experiment to compare and differentiate between super-enhancers and locus-control regions (LCRs), shedding light on the enigmatic relationship between LCRs and SEs within the human genome. Particular attention is paid to the classification of specific mechanisms and their diversity, exemplified by various oncological, cardiovascular, and immunological diseases, as well as an overview of several anti-SE therapies. Overall, the work presents a comprehensive analysis of super-enhancers across different diseases, aiming to provide insights into their regulatory roles and may act as a rationale for future clinical interventions targeting these regulatory elements.

Super-enhancers (SEs) are large clusters of highly active enhancers that play key regulatory roles in cell identity, development, and disease. While conventional methods classify SEs in a binary fashion—super-enhancer or not—this threshold-based approach can overlook significant intermediate states of enhancer activity. Here, we present a dataset and accompanying framework that facilitate a more nuanced, non-binary examination of SE activation across mouse tissue types (mammary gland, lung tissue, and NMuMG cells) and various experimental conditions (normal, tumor, and drug-treated samples). By consolidating overlapping SE intervals and capturing continuous enhancer activity metrics (e.g., ChIP-seq signal intensities), our dataset reveals gradual transitions between moderate and high enhancer activity levels that are not captured by strictly binary classification. Additionally, the data include extensive functional annotations, linking SE loci to nearby genes and enabling immediate downstream analyses such as clustering and gene ontology enrichment. The flexible approach supports broader investigations of enhancer landscapes, offering a comprehensive platform for understanding how SE activation underpins disease mechanisms, therapeutic response, and developmental processes.

Long non-coding RNAs (lncRNAs) interact with chromatin and recruit epigenetic complexes to specific genomic loci, yet their relationship with super-enhancers (SEs), key regulatory elements frequently reprogrammed in cancer, remains unexplored. We developed an integrative pipeline that combines RNA–chromatin contact data (RNA-Chrom), histone modification–lncRNA expression correlation profiles (HiMoRNA peaks), and super-enhancer annotations (SEdb 3.0) to map lncRNA–SE regulatory axes. Applying this framework to SNHG1 in HCT116 colorectal cancer cells, we identified 21 SNHG1-reactive super-enhancers (Ψ-SEs) among 184 cancer-specific SEs, at which SNHG1 physical contacts co-occur with SNHG1-correlated histone modifications (HiMoRNA peaks), predominantly H3K4me1 (permutation p = 0.001, fold enrichment = 2.03). Comparison with 4145 lncRNAs demonstrated that epigenetic correlations alone do not distinguish SNHG1; instead, the addition of the contact layer is required to delineate the Ψ-SE set. Differential expression (DESeq2) and co-expression analyses in 471 TCGA-COAD tumor samples identified 12 Ψ-SE target genes (including CDC20, PDP1, and TOP1) consistently upregulated in both HCT116 cells and patient tumors and positively correlated with SNHG1, with the co-expression signal robust to tumor purity correction. The proposed Ψ/Ω classification provides a generalizable framework for prioritizing super-enhancers at which lncRNA–chromatin interactions may shape the local epigenetic environment across cancer types.