An enhancer is a DNA sequence in the genome that can regulate the expression of target genes spatially and precisely during cellular development and differentiation (Simakov et al., 2013). The ENCODE Project Consortium has speculated that there are approximately 400,000 putative enhancers in the human genome based on different regulatory elements of the genome, which can be classified according to seven different chromosome states (The ENCODE Project Consortium, 2012). In addition, as more cell types are analyzed and studied, the number of human enhancers has increased to more than one million (Jia et al., 2020; Levine et al., 2014).

Cells store genetic information in DNA, synthesize mRNA through transcription, and translate mRNA into proteins with specific biological functions. Since 1985, this process has been known as the “central dogma” (Moldave, 1985), confirming the transcription process is divided into multiple stages, including initiation, elongation and termination (Gayon, 2016). RNA polymerase II is regarded as the core factor regulating the process of gene transcription. Gene expression is mediated by common transcription factors, promoters, enhancers, mediators, cohesion, insulators, and silencers (Juven-Gershon and Kadonaga, 2010). Normally, common transcription factors can bind to the promoters of genes and thereby stimulate gene expression. However, unlike promoters, enhancers can regulate gene expression in a nondirectional manner, and the distance from target genes may be highly variable, they can be located upstream or downstream of genes, or within introns, or even within genes that have different chromatin profiles (Li et al., 2023; Lomvardas et al., 2006; Spilianakis et al., 2005). In some cases, a single enhancer can even regulate the expression of multiple genes (Plank and Dean, 2014). In cells, the activity of specific enhancers can be limited by a variety of factors and only elevates target gene expression within specific tissue and cell types, specific time points, or special physiological, pathological, and environmental conditions. Therefore, this dynamic regulation of enhancer activity has been shown to be critical in cellular differentiation (Ong and Corces, 2011). At that time, Richard A. Young’s lab proposed the concept of SEs on the basis of the mean density of Mediator coactivator (Med1) compared to the typical enhancers by using ChIP-Seq (Whyte et al., 2013). The information generated by ChIP-seq has greatly facilitated the understanding of the mechanisms by which enhancers, transcription factors, co-factors and histone modifications regulate gene expression. ChIP-seq fold the difference for enhancer features, such as Mediator, H3K27ac, H3K4me1, and DNaseI hypersensitivity between SEs vs. typical enhancers. SE region span is typically 8 to 20 Kb, which is much higher than typical enhancer of 200–300 bp region span. In Figure 1, Y-axis represents the overall signal value of SEs and typical enhancers (Figure 1).

Based on the large difference in the mean density between SEs and typical enhancers, SEs have a strong transcriptional activation ability, and the expression levels of the genes associated with them are also relatively high. The transcription factors bound by SEs and the chromosome markers associated with transcriptional activity are much higher than those of typical enhancers. As a result, SEs may strongly promote the transcription of their target genes (Whyte et al., 2013) (Figure 2).

In tumor cells, oncogenes are transcribed and activated, mediating cell proliferation and immortalization (Hanahan and Weinberg, 2011). Therefore, the inhibition of oncogene transcription is a potential therapeutic target. However, this target faces a major challenge: transcription is the most basic function of cells, and the transcriptional inhibition of oncogenes may lead to a broad-spectrum inhibition of cell gene transcription (Sengupta and George, 2017; Sur and Taipale, 2016). Nowadays, clinically used transcriptional inhibitors should specifically inhibit oncogenes and have little effect on normal cell transcription (Didiasova et al., 2018). Researchers have found that there are a large number of “tumor-specific transcripts” in triple-negative breast cancer cells, and the world’s first “tumor-specific transcript” map of triple-negative breast cancer has been successfully drawn. Further research has discovered that SEs can activate the expression of “tumor-specific transcript” MARCO-TST in triple-negative breast cancer, and for the first time confirmed that BET inhibitors can be a potential treatment option for patients with triple-negative breast cancer (Yang et al., 2022). Compared with individual oncogenes or molecules, the maintenance of tumor cell characteristics is more dependent on the activity of SEs. This implies that SEs are more ideal targets for anti-cancer treatment. Therefore, targeting and inhibiting the activity of SEs or knocking out SEs fragments may become a new approach for tumor treatment. However, as transcription is a biological process that occurs universally in the body, it cannot be inhibited as a whole. Highly specific inhibition is required for anti-tumor treatment. Therefore, it is necessary to search for a possible intervention target among the small molecules involved in the SEs mechanism.

Transcriptional initiation, suspension, elongation, and other processes are regulated by transcription factors. Studies have shown evidence that the SE regulation of transcription relies on BRD4, mediator complexes, cell-cycle-dependent kinase 7 (CDK7) complexes, and the CDK9 transcription complex (Adelman and Lis, 2012). In addition, BRD4 promotes the assembly of SEs by recruiting the mediator complex, thereby promoting the release of suspended RNA Pol II (Di Micco et al., 2014). CDK12/13 can accelerate RNA Pol II transcription. Therefore, it is generally believed that the mediator complex, BRD4, and CDK, the key regulatory factors of the SE regulation of transcription, are conducive to the development of new tumor therapeutic targets. Based on the key nodes of transcriptional inhibition mentioned above, the main drug types currently available for the targeting of SEs are ① inhibitors or decomposers of BRD family proteins; ② CDK7 inhibitor; and ③ other types of inhibitors (Bartkowiak et al., 2010; Liang et al., 2015).

JQ1 inhibits the interaction between BRD4 and acetylated proteins by binding to a domain of BRD4 (Delmore et al., 2011; Filippakopoulos et al., 2010), which limits the binding of BRD4 to the H3K27ac site of the SE and inhibits the interaction between the SE and the promoter, thereby affecting oncogenes’ transcription (Lovén et al., 2013). Since the transcription regulated by SEs is particularly sensitive to changes in the concentration of transcription factors, JQ1 treatment can preferentially prevent the binding of BRD4 to the acetylation modification sites on SEs, thus specifically inhibiting the transcriptional activation mediated by SEs (Lovén et al., 2013; Shin, 2018). In addition, the BRD inhibitors iBET762, OTX015, CPI0610, and iBET151 were also found, and the first three have entered clinical trials (Amorim et al., 2016). dBET series compounds are more specific BRD4 inhibitors developed based on the chemical structure of JQ1, which can specifically mediate the degradation of BRD family proteins, thereby preventing these proteins from recognizing the acetylation sites of SEs, affecting the activity of the SEs, and inhibiting transcription (Qian et al., 2023; Tasdemir et al., 2016). Studies have shown that BETd-246 can target the specific degradation of BRD family proteins, and it also shows a better therapeutic effect in triple-negative breast cancer than iBET-211 (Bai et al., 2017).

THZ1 is a CDK7-specific inhibitor to which some SE-mediated tumor cells are highly sensitive (Kwiatkowski et al., 2014). THZ1 can covalently bind to CDK7 cysteine 312, inhibiting CDK7 kinase activity and the phosphorylation of CDK7 for Pol II CTD’s fifth serine, thereby blocking transcription initiation and reducing the amount of promoter-proximal paused RNA Pol II. The SE of the main control suspension RNA Pol II is released. Then THZ1 processes RNA Pol at the bottom of the promoter II, reduces the enhancer of the RNA Pol II combination, and finally suppresses transcription (Zhang et al., 2020). After THZ1 treatment, the SE activity decreased, which leads to the transcriptional inhibition of a variety of oncogenes, thus inhibiting the growth and proliferation of a variety of tumor cells. Syros developed SY-1365 as a specific inhibitor of CDK7 that selectively inhibits a variety of solid tumors (breast, ovarian, and small-cell lung cancers) and blood cancers (acute myeloid leukemia and acute lymphoblastic leukemia).

CDK12 is a kinase that regulates transcription elongation. In T cell leukemia, THZ531 can specifically inhibit CDK12/13 and effectively inhibit super-enhancer-mediated gene expression (Zhang et al., 2016). Inhibiting the activity of mediator kinase (CDK8/19) in acute myeloid leukemia can upregulate the SE activity related to a tumor suppressor and activate the expression of the tumor suppressor gene, thus achieving anti-leukemia activity (Pelish et al., 2015). Similarly, the CDK 4/6 inhibitor LEE011 selectively inhibits CDK4, downregulates cyclin D1-related SE activity, and effectively promotes the apoptosis of Ewing’s sarcoma cells (Kennedy et al., 2015).

GZ17-6.02 can affect gene acetylation, reduce the transcription of master transcription factors, proteins in the sonic hedgehog protein pathway, and stem cell markers (Ghosh et al., 2019). In pancreatic ductal adenocarcinoma, GZ17-6.02 can decrease the occupancy of master transcription factor Oct4 in SE regions, reduce the activity of SEs, and thereby inhibit the growth of pancreatic ductal adenocarcinoma cells (Ghosh et al., 2019). GZ17-6.02 can also inhibit the growth of malignant glioblastoma by down regulating the expression of SE genes (Choi et al., 2022). Additionally, Syros Pharmaceuticals has utilized its gene control platform to identify acute myeloid leukemia patients and subgroups of myelodysplastic syndrome with SEs of RARA or IRF8 genes and discovered biomarkers that can recognize these SEs. These SEs are believed to drive the overexpression of RARA or IRF8 genes, leading to tumor development by targeting immature, undifferentiated, and proliferating cells. The retinoic acid receptor-α agonist SY-1425 can promote the differentiation of acute myeloid leukemia cells with high expression of RARA or IRF8 genes, thereby inhibiting tumor growth (McKeown et al., 2019).

SEs have become a highly controversial topic in both clinical and basic research, with their functions and potential clinical therapeutic prospects drawing significant attention. Currently, a large number of BET family protein inhibitors or degraders and CDKs inhibitors have been included in clinical studies targeting SEs, and their value in anti-tumor treatment is gradually being discovered. However, it is worth noting that targeting SEs for cancer treatment may cause significant adverse reactions, as some normal genes will also be inhibited when SEs are blocked. For instance, studies have shown that THZ1 can inhibit myogenic differentiation, indicating that THZ1 may cause adverse reactions to muscle function during the treatment process (Dutta et al., 2023). Additionally, interfering with the generation of oncogenic SEs through the CRISPR/Cas9 gene editing system can block SEs, regulate SEs-driven oncogenes, and prevent tumor occurrence. It is also possible to exert anti-tumor effects by knocking out oncogenic SEs through the CRISPR/Cas9 gene editing system. However, due to the off-target effects of the CRISPR/Cas9 gene editing system, any non-target site gene editing is associated with clinical risks (Zheng et al., 2020). ChIP-seq is one of the main techniques for identifying SEs, but its application in SE research still has several limitations. For instance, the ChIP-seq experiment involves multiple steps, including cross-linking, chromatin fragmentation, immunoprecipitation, library construction, and sequencing, and each step may introduce errors (Anzawa et al., 2020). The uniformity of chromatin fragmentation, the quality of antibodies (such as the specificity and affinity of H3K27ac antibodies), and the efficiency of immunoprecipitation all affect data quality (Anzawa et al., 2020). Moreover, traditional SE identification methods (such as the ROSE algorithm) mainly rely on the signal intensity of H3K27ac ChIP-seq, but this method cannot directly verify whether SEs truly regulate gene expression, resulting in a high false positive rate (Mack et al., 2018). Research has found that in colorectal cancer, only 16.1% of ROSE-predicted SEs are significantly correlated with gene expression, indicating that most predicted SEs may not be functional. Currently, emerging technologies such as CUT&RUN and CUT&Tag, which have lower cell requirements, higher throughput, and simpler operation procedures, are gradually replacing ChIP-seq in chromatin mapping studies (Shinkai et al., 2025). These technologies can more efficiently capture the binding sites of transcription factors and histone modifications, but they have not yet been fully standardized for SE research. In summary, although ChIP-seq still holds an important position in SE research, its limitations have prompted scientists to explore more efficient and accurate experimental and computational methods.

It is currently known that there are over 80 types of autoimmune diseases, which affect 3%–5% of the total population in the United States (Jacobson et al., 1997; Marrack et al., 2001). Scientists have found that human leukocyte antigens (HLAs) can be divided into two classes: class I and class II. HLA class I is expressed in almost all cell types and presents peptide antigens on the cell surface. In contrast, HLA class II is significantly expressed only on dendritic cells and B cells and presents antigens on the surface of both types of cells. Single-nucleotide polymorphisms (SNPs) in the HLA class I and class II genes can encode on chromosome 6p21.3 and are associated with various autoimmune diseases (Okada et al., 2014; Vahedi et al., 2015). Moreover, disease-related mutations can be enriched in SEs. The results suggest that the distribution of this variation is disproportionate and that for most disease types, disease-related SNPs are enriched in the SEs found in disease-related cell types (López-Isac et al., 2019). This correlation also explains the role of SE-associated genes in defining cellular identity, and changes in the expression of identity genes may lead to the occurrence of related diseases. For example, of the 27 SNPs known to be associated with Alzheimer’s disease, approximately 19% (5/27) occur in SEs in brain tissue (Chapuis et al., 2013; Dunn et al., 2025). A similar situation has been observed for a variety of immune-related diseases including type I diabetes (Cooper et al., 2008), systemic lupus erythematosus (Almlöf et al., 2017; Zhang Y. et al., 2022), rheumatoid arthritis (Jadhav et al., 2022; Okada et al., 2014; Tsuchiya et al., 2021), multiple sclerosis (Sawcer et al., 2011), primary biliary cirrhosis (Cavalli et al., 2016), Crohn’s disease (Franke et al., 2010), Asthma (Ferreira et al., 2011), and atrial fibrillation (Smith et al., 2012), which exhibit an enrichment of SNP loci in a T-cell-specific SE region (Vahedi et al., 2015). Through studying the lead expression-modulating SNP, scientists also uncovered an NF-κB-driven regulatory circuit which constrains T-cell activation via the dynamic formation of a SE that upregulates TNFAIP3 (Bourges et al., 2020). Therefore, SEs can be used to study the pathogenesis of these diseases, while drugs that affect the genes associated with the specific SEs may be more effective in clinical applications. At the same time, the combination of cell-type-specific SEs with human genetic information also provides a scientific basis for the development of targeted drugs.

As we previously mentioned, rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic synovial inflammation and progressive joint destruction (Odegård et al., 2005; Yelin, 2007). Proinflammatory cytokines such as tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β), and proteases (e.g., MMP9 and MMP3) increase significantly in the synovial fluid (McInnes and Schett, 2011; Redlich and Smolen, 2012). Thus, TNF inhibitors, interleukin receptor inhibitors, and other small-molecular-weight compounds such as Janus kinase (JAK) inhibitors have been frequently and actively applied in clinical practice (Kubo et al., 2014; Singh et al., 2019; Tanaka, 2019). However, some patients relapsed or showed no remission, indicating that the existing drugs had little effect on them. As discussed previously, some SNPs are known to be associated with high susceptibility to RA. In addition, the results of GWAS analysis have revealed that approximately 101 SNPs are connected to RA and cover almost 50% of the genomic variants underlying the susceptibility to RA (Peeters et al., 2015; Viatte et al., 2013). Compared with typical enhancers, SEs harbor 3.2 times more SNPs that are associated with susceptibility to RA, indicating that the SNPs related to RA can strongly take part in SE-mediated transcriptional regulation (Teumer et al., 2018) and may serve as a new avenue for RA therapy.

The basic leucine zipper transcription factor 2 (BACH2) protein is an important transcription factor for the maintenance of immune homeostasis by Treg cells (Vahedi et al., 2015). In T cells, BACH2 inhibits the expression of genes encoding various cytokines, including IFN-gamma, and cytokine receptors. Gene mutations at the BACH2 locus are associated with RA. The knockdown of the BACH2 gene induces the expression of various cytokines and their receptors (Vahedi et al., 2015). Although the BACH2 protein negatively regulates the expression of eRNAs, the gene itself is uniquely regulated by SEs (Vahedi et al., 2015). However, it remains unclear which eRNA induces the expression of the BACH2 gene and how it achieves this goal. Tofacitinib, can inhibit JAK1/3, thereby reducing the expression of several genes related to the susceptibility of RA. Studies have confirmed that, compared with genes not regulated by SEs, its inhibitory effect on the expression of genes regulated by SEs is more significant. (Vahedi et al., 2015). This suggests that JAK/STAT signaling factors can regulate the expression of RA susceptibility-related genes through SEs.

Systemic lupus erythematosus (SLE) is an autoimmune disease that follows a chronic course or repeated cycles of remission and relapse and predominantly affects women. According to GWAS, approximately 60 disease-susceptibility SNPs have been identified in European SLE patients (Teruel and Alarcón-Riquelme, 2016), while nine new disease-susceptibility loci were identified in Chinese patients (Han et al., 2009). Researchers have discovered that A Disintegrin And Metalloproteinase (ADAM)-like decysin-1 (ADAMDEC1) are important for proteolytic cleavage (Shi et al., 2018). ADAMDEC1 is closely related to ADAM28, which plays a key role in maintaining the acute inflammatory process. What’s more important is ADAMDEC1 is overexpressed in monocytes of SLE patients, and its expression can be induced by pro-inflammatory cytokine stimulation. (Guo et al., 2022). Moreover, inflammatory stimulation leads to the recruitment of NF-κB and P300 upstream of the ADAMDEC1 gene. In the absence of such combination, the induction of ADAMDEC1 expression was inhibited (Shi et al., 2018). After enhancing the activity of P300 marked with H3K27ac, eRNA-157 promoted the induction of ADAMDEC1 gene expression by looping between the promoter and SE. eRNA-157 is a short noncoding RNA in a non-polyadenylated state produced through bidirectional transcription, while ADAMDEC1 mRNA is a long-coding RNA in a polyadenylated state generated via unidirectional transcription. Although eRNA-157 involves the induction of ADAMDEC1 mRNA, whether the latter regulates the former is still unclear (Yamagata et al., 2020).

The programmed cell death 1 gene, PDCD1, encodes a programmed death 1 (PD-1) protein, which is an important immune checkpoint. Mice with PDCD1 knockout exhibit SLE-like pathology (Nishimura et al., 1999). What’s more, SNPs in the PDCD1 gene are also associated with SLE (Prokunina et al., 2002). Importantly, the PDCD1 gene has a SE structure in CD4-naive T cells and may be regulated by SEs (Khan and Zhang, 2016). Due to the different types of simulation, SE function may be impaired, thus potentially promoting the pathogenesis of SLE. As mentioned above, the eRNAs and SEs that may be involved in the control of the pathological conditions of SLE patients have been identified. In the future, their functions will be elucidated at an individual level using mice and other disease models.

Multiple sclerosis (MS) is also a complex autoimmune disease that is caused by a combination of many risk factors, including genetic mutations and vitamin D deficiency. SNPs associated with the susceptibility to MS are observed at and around vitamin D receptor (VDR)-binding sites (Sospedra and Martin, 2005). Lu et al. classified SEs bound to VDR (termed VDR SE, VSE) into three types VSE1, VSE2 and VSE3. Several SNPs associated with MS susceptibility were detected in the VSE domain, particularly in the VSE3 subdomain (Lu et al., 2019). Based on these findings, disease-susceptibility SNPs within SEs are assumed to regulate SEs. However, it remains unclear how the presence of SNPs associated with MS susceptibility affects the expression of eRNA through the interaction of VDR that binds to vitamin D and chromatin, which seems to be an issue for further investigation.

In the case of inflammatory bowel disease (IBD), it has been found that approximately half of the risk SNPs are located in the SE regions of CD4 T cell activation (Vahedi et al., 2015). Graves’ disease (GD) is associated with excessive humoral immunity, due to the production of autoantibodies against the thyroid-stimulating hormone (TSH) receptor 1 (Klecha et al., 2008). GWAS has identified 101 SNPs associated with susceptibility to GD (Teumer et al., 2018). At the same time, atopic dermatitis (AD) causes chronic and recurrent inflammatory allergic reactions, such as skin itching and peeling. In the future, it will be of great significance to clarify the role of SE in various autoimmune diseases, including the aforementioned ones (Jin et al., 2016). Whyte et al. reported that SEs are cis-regulatory elements and form loops with promoters (Whyte et al., 2013). In addition, individual SEs form loops with many promoters, thereby regulating the expression of many target gene clusters (Cong et al., 2019). Therefore, an important theme for future research might be to use GWAS analysis to identify disease-specific SNPs for each autoimmune disease, to identify SNPs important for chromatin interactions through linkage analysis, and to measure actual interactions using chromosome conformation capture analysis.

Although SEs serve as key elements in gene regulation, the SNPs related to them have a significant association with disease mechanisms, there still existed the potential contradictions about the roles of SEs in diseases. Studies have verified the same SE-SNP may have opposite effects in different tissues. For instance, rs12740374 (which regulates SORT1 expression) reduces blood lipids in the liver but may promote atherosclerosis in the vascular wall (Al-Eitan et al., 2020). SE often regulates multiple genes, while SNPs may only affect one of the targets. For instance, rs4791078 is located in a SE involved in heart development, but the gene network it regulates through SMAD3 remains incompletely defined (Nasser et al., 2021). In cancers, SE can both activate tumor suppressor genes (such as TP53) and drive oncogenes (such as MYC), depending on the cellular context (Chen et al., 2024; Kubota et al., 2019). Current functional prediction tools (such as DeepSEA) have limited accuracy in annotating non-coding variants, and experimental validation (such as MPRA) is costly, which may lead to false positives/negatives. In conclusion, SE-related SNPs directly participate in disease occurrence by regulating the expression of key genes. However, tissue specificity, functional redundancy, and technical limitations lead to contradictions in their roles. In the future, it is necessary to combine high-throughput functional experiments with clinical data to elucidate the mechanism of SE more accurately in diseases.

A SE is defined as a large cluster of transcription enhancers that can drive cell-identity-defining genes expression. SEs exhibit unique structural and functional properties compared with typical enhancers; however, at present, there is still a lack of clear rules to define SEs. The mathematical method to distinguish SEs from typical enhancers is mainly based on the difference in the signal strength of active enhancer markers. Whether SEs can be defined as distinct entities still needs further research and verification (Pott and Lieb, 2015; Whyte et al., 2013). In addition, recent research has allowed for an increasing recognition of the many functional similarities between promoters and enhancers. Enhancers have shown the characteristic of driving gene expression, and some promoters have also displayed the function of strengthening gene expression, making it necessary to reevaluate the traditional definitions of enhancers and promoters (Andersson, 2015; Kim and Shiekhattar, 2015). Nevertheless, SEs have shown their value in areas such as cell identity determination and disease pathology. To further explore SEs and their roles in defining cell identity and participating in disease processes, relatively systematic SE database platforms, such as SEA and dbSUPER, have been established recently, and relevant analysis tools have been integrated that will promote the research and understanding of SEs (Khan and Zhang, 2016; Wei et al., 2016). SEs are enriched in key genes that control cell identity, and the expression of many key cancer tumor cell genes is driven by SEs. The characteristics of common diseases related to the significant variation in SE enrichment can be used to identify the key cell-type-specific transcription factors and determine key oncogenes, and variations in associated loci have shown great application potential. With further research developments, SEs may provide new ideas for the development of treatments for common diseases such as cancer and other conditions.