In the 1980s, a 72 bp sequence, later called “enhancer,” was identified in the genome of the SV40/hemoglobin β1 recombinant gene, accompanied by the overexpression of the rabbit β globin gene (Banerji et al., 1981). Enhancers are cis-regulatory elements that interact with promoters from different positions and distances to increase the expression of controlled genes (Blackwood and Kadonaga, 1998). Richard A. Young identified larger clusters of enhancers, known as SEs (or stretch enhancers), which drove high transcription levels of key genes and defined cell identity in pluripotent embryonic stem cells (ESCs) (Whyte et al., 2013). Researchers have created some online repositories such as dbSUPER (Khan and Zhang, 2016) (https://asntech.org/dbsuper/), SEdb (Jiang et al., 2019; Wang Y. et al., 2023) (https://bio.liclab.net/sedb/), SEanalysis (Qian et al., 2019) (https://bio.liclab.net/SEanalysis/), and SEA (Wei et al., 2016; Chen et al., 2020) (http://sea.edbc.org/) to share SEs (and related genes) and their genetic and epigenetic annotations. The constituent enhancers of SEs are with multiple overlapping enrichments of H3K4me1 and H3K27ac of active enhancers (AEs) marks (Yoo et al., 2019; Barral and Déjardin, 2023) (Figure 1). The Rank Ordering of Super Enhancers (ROSE) algorithm is the most commonly used method to identify SEs (Lovén et al., 2013). The algorithm ranks H3K27ac chromatin immunoprecipitation sequencing (ChIP-Seq) signals and defines enhancers above the inflection point (slope >1) as SEs. However, there is still no consensus on an arbitrary cutoff of enrichment level within certain proximity for considering whether an active enhancer is a super-enhancer. As ChIP-seq has advanced, more and more SEs have been discovered to be involved in carcinogenesis, tumor immune evasion (Xu et al., 2019), and various autoimmune disorders (Parker et al., 2013; Vahedi et al., 2015; Yamagata et al., 2022). It is worth mentioning that other techniques, including 4C-seq (Jiang et al., 2020), Hi-C (Huang J. et al., 2018), ChIA-PET (Dowen et al., 2014), ChIP-STARR-seq (Barakat et al., 2018), GRO-seq (Hah et al., 2015), DNase-seq (Kang et al., 2021), and ATAC-seq (Adam et al., 2020), have also been employed to detect SEs in recent years. Lately, Jialiang Huang’s team refined the hierarchical structure of SEs into hub enhancers and non-hub enhancers by hierarchical scoring (Huang J. et al., 2018). Hub enhancers outperform non-hub enhancers in the interaction with regulatory factors and effects on activating target gene transcription.

As a member of the bromodomain and extra-terminal domain (BET) family and closely associated with SE-related oncogenic transcription, bromodomain-containing protein 4 (BRD4) binds to acetylated modified histones and recruits additional proteins (such as TEFb and Mediator) to indirectly participate in the initiation of transcription and the control of transcriptional elongation (Yang et al., 2005). BET inhibitors (BETi) separate BET proteins from chromatin by competitively binding with histone-acetylated lysines of the BET bromodomain. SEs are highly sensitive to BETi (Fontanals-Cirera et al., 2017). JQ1 is the first reported BETi (Filippakopoulos et al., 2010) and has been widely used for SE identification (Das et al., 2023; Zheng et al., 2024). JQ1 disrupts the transcriptional activity of the master regulators ETS2, HNF4A, and JUNB, suppresses the TGF-induced EMT status, and decreases tumor cell migration and invasion in NSCLC cells (Chang et al., 2016). JQ1, in combination with NSD2 knockdown, inhibits the activation of RAS-driven transcriptional programs in lung cancer cells (García-Carpizo et al., 2016). JQ1 downregulates the expression of HK2, whose promoter is specifically SEs hijacked in a dose-dependent manner in LUAD (Song et al., 2022). In addition, JQ1 exhibits this dose-dependent pharmacological profile in acute myeloid leukemia (Jang et al., 2017), uterine carcinoma (Bonazzoli et al., 2018), and diffuse large B-cell lymphoma (Trabucco et al., 2015).

Chromatin accessibility allows physical contact between cis-acting elements and trans-acting factors to regulate transcription processes (Klemm et al., 2019). Breast cancer cells undergoing SE reprogramming produced the accessible chromatin structure and had a higher propensity for lung metastasis (Li K. et al., 2019). TFs are proteins that bind to specific DNA sequences (Spitz and Furlong, 2012). If abnormally enriched at oncogene promoters or enhancers, however, they can result in gene overexpression and carcinogenesis (You et al., 2018). SE-driven genes encode core TFs, which can bind to their SEs or others and interact with other core TFs to regulate the expression of additional genes and form cell- or tissue-specific core regulatory circuits (CRC) (Saint-André et al., 2016; Feng C. et al., 2023). For example, the three specifically SE-activated core TFs ELF3, EHF, and TGIF1 constituted the major component of a CRC with an interdependent pattern that tightly regulated cell migration and invasion in lung adenocarcinoma (LUAD) (Zhang T. et al., 2020).

Transcriptional coactivators are a variety of proteins with transcription-related functions. As mentioned above, BRD4 is a crucial part of SEs and plays an essential role in the transcriptional activation of oncogenes. Besides, MED1, Mediator, and CBP/p300 with acetyltransferase activity bind to TFs to assist in the assembly of the transcription preinitiation complex and stabilize the transcriptional activity of RNA polymerase II (Ogryzko et al., 1996; Mao et al., 2019; Rengachari et al., 2021). Benjamin R Sabari’s team proposed that transcriptional coactivators enhance transcriptional activity by forming droplets around SEs through a phase separation process (Sabari et al., 2018). However, whether SE-dependent transcriptional enhancement depends on this mechanism is still debatable (Trojanowski et al., 2022).

Overall, the effects of SEs on transcriptional activation are more forceful than that of typical enhancers (TEs) due to their higher region spanning, higher histone modifications, TFs, and RNA polymerase II enrichment, and more binding sites for transcriptional coactivators (Hnisz et al., 2013; Whyte et al., 2013).

Two key epigenetic regulatory mechanisms involved in SEs are histone modifications (mostly methylation and acetylation) and DNA methylation. Modified histones in the presence of catalytic enzymes (histone lysine methyltransferases or histone acetyltransferases, etc.) can alter chromatin accessibility, affect the assembly of transcriptional complexes, and enhance or reduce the interaction of TFs to gene structures, resulting in altered levels of transcription and translation of downstream genes, which can trigger the development of cancer, autoimmune diseases, and even chronic diseases (Arrowsmith et al., 2012; Fatma et al., 2022; Yang J. et al., 2023). For instance, in a mouse model of LUAD, the deletion of KMT2D (a histone methyltransferase) caused decreased global levels of H3K4me1 and H3K27ac of SEs, leading to the suppression of SE-associated antioncogene Per2 (an antioncogene that inhibits glycolysis) and the activation of glycolytic processes, promoting the growth of cancer (Alam et al., 2020). Another study found that overexpression of NSD2, a histone methyltransferase, resulted in H3K36me2 spreading to the intergenic region of the low H3K27me3 region to exert pro-transcriptional effects (García-Carpizo et al., 2016). That is an example of long-range epigenetic remodeling. It is generally accepted that hypermethylation of CpG islands in the promoter region usually represses gene expression, whereas hypermethylation of gene bodies usually shows the opposite effects (Yang et al., 2014). Interestingly, a study found that highly expressed genes covered by SEs had lower levels of gene body methylation, which resulted in a significant negative correlation between gene body methylation and expression, contrary to popular belief (Pongor et al., 2022). These findings imply an intrinsic link between DNA modification, histone modification, and SEs that jointly influence gene expression.

Several studies have elucidated that the formation (or translocation) of SEs is dependent on genetic variants such as single nucleotide variants (SNVs), insertion deletions (indels), and chromosomal structural variants (SVs) (Mansour et al., 2014; Xing et al., 2019; Wang J. et al., 2023). However, the current research discovered only gene fusion or rearrangement was responsible for SEs’ formation in different tissue subtypes of lung cancer. A study found that focal amplified SEs in LUAD acted on the MYC promoter to spark off transcriptional overactivation (Zhang et al., 2016). And it subsequently demonstrated that the mutations arose from a tandem duplication of a non-coding sequence. In LUAD patients, ALK or ROS1 rearrangements triggered SEs hijacking upstream of EML4 and SLC34A2, driving expression of the new oncogenic fusion genes ALK-EML4 and ROS1-SLC34A2, respectively (Yuan et al., 2021). NUT Carcinoma of the Lung is a rare lung cancer subtype of squamous origin that is extremely aggressive and highly associated with NUT gene fusions (Travis et al., 2015). The fusion gene BRD4/3-NUT co-localized with p300, and the corresponding hyperacetylated region showed a similar SE hijacking function to increase the pro-transcriptional effects of BRD4 (Eagen and French, 2021). Adenoid cystic carcinoma (ACC) originates from the secretory glands and is usually molecularly labeled by the MYB-NFIB fusion oncogene (Persson et al., 2009). MYB rearrangements are widely observed in primary pulmonary adenoid cystic carcinoma (PACC) (Roden et al., 2015). A self-regulatory feedback loop was generated when gene rearrangements (e.g., MYB -NFIB, MYB-TGFBR3, MYB-RAD51B gene fusion) occurring in ACC caused SE translocations and overexpression of MYB proteins (Drier et al., 2016).

Previous studies on SE-associated activation of oncogenes and signaling pathways have mainly focused on LUAD, as shown in Figure 2. A study showed that focal amplification of the SEs 3′to MYC caused increased expression of MYC and other MYC-target genes (Zhang et al., 2016). The master transcription factor ASCL1 regulates the expression of multiple SE-associated oncogenes, such as RET and MYCL (Miyashita et al., 2018). Nuclear transcription factor estrogen-related receptor alpha (ERRα) coordinates with BRD4 to drive the expression of SE-associated glycolytic gene HK2, leading to enhanced glycolysis progress and growth of malignant cells (Song et al., 2022). Specific SE-driven PADI gene families associated with tumor proliferation, invasion, and colony formation were identified as highly expressed in Osimertinib-resistant LUAD cells (Li H. et al., 2022). SE-associated CENPO was highly expressed in LUAD and positively correlated with the tumor stage, which could promote carcinogenesis and progression by regulating the cell cycle and tumor immune microenvironment (Shi et al., 2023).

As we all know, long noncoding RNAs (LncRNAs) are a class of RNAs longer than 200 nt without encoding proteins. Super-enhancer lncRNAs (SE-lncRNAs) are transcribed from super-enhancer genomic regions, which activate the neighboring genes or trans-activate the distant genes through several mechanisms such as transcription factor trapping, promotion of chromatin looping, histone modification, recruitment of phosphorylating RNA polymerase II (Pol II), and isolation of the transcriptional repressor (Schaukowitch et al., 2014; Alvarez-Dominguez et al., 2017; Soibam, 2017; Lee et al., 2020). FOXA1, a driver of multiple cancers (Martínez-Jiménez et al., 2020), can maintain malignant cell survival and promote progression by sensitizing the TGF-β pathway (Wei et al., 2023), activating glycolysis (Xie et al., 2022), and inhibiting autophagy (Li J. et al., 2022). FOXA1 was reported to stimulate LncRNA DSCAM-AS1 at its SEs region (Zhang Y. et al., 2020). DSCAM-AS1, in turn, positively activates FOXA1 expression by interacting with Y box binding protein 1 (YBX1, a DNA/RNA binding protein). Silencing of DSCAM-AS1 inhibits FOXA1 expression and cell growth. Epithelial-mesenchymal transition (EMT), a process by which epithelial cells acquire a mesenchymal cell phenotype and the ability to migrate and invade, is regulated by a series of TFs (Akhmetkaliyev et al., 2023). As a mediator between CDK1 and PRDX1, SE-associated lncRNA LINC00880, activated by TFs FOXP3, leads to phosphorylation of PRDX1 and dysregulation of the PTEN/AKT pathway and metastasis of LUAD (Feng Y. et al., 2023). However, as the LUAD stage progressed, the expression of LINC00880 tended to decrease, and over-expression of LINC00880 had a significant negative prognostic value only in patients with pathological stage I. SEs and related TFs such as ETS2, HNF4A, and JUNB control the high expression of some key EMT genes and define the intermediate state of the EMT process in LUAD cells (Chang et al., 2016). Besides, EMT can be mediated by classical TGF-β/SMAD pathway (Zhang et al., 2014). As one of the receptor-regulated SMAD family members, SMAD3 readily occupies the SE site of LINC01977 and drives its expression in a high TGF-β background (Zhang et al., 2022). In response, LINC01977 boosts the interaction between SMAD3 and CBP/P300 to form a SMAD3/CBP/P300 complex targeting and activating the ZEB1 gene, which induces EMT and causes migration, proliferation, and invasion of LUAD, especially in the early stage of LUAD due to high infiltration of TAM2 (inducing a rich TGF-β environment) and elevated expression of SE-related LINC01977 in this stage. Notably, in p53-deficient lung cancers, the presence of the USP7 (a deubiquitinase) maintains the adherence of the SMAD3 to its own SE elements (EN4, EN9, and EN10), thereby maintaining a positive autoregulatory loop (Huang et al., 2021). However, due to the absence of the p53/MDM2 pathway, USP7 acts as a cancer suppressor. A study showed that TP63-mediated SE-driven LINC01503 was overexpressed and oncogenic in esophageal squamous carcinoma (Xie et al., 2018). Overexpression of LINC01503 was also observed in NSCLC cells and associated with proliferation, migration, and invasion, although it is unclear whether this process is SE-dependent (Shen et al., 2020). Another analysis found that SE-associated lncRNA AC074117.1 was significantly expressed and served as an independent prognostic marker in LUAD (Li et al., 2021).

Several studies comparing LUAD cell lines (or cancerous tissues from patients) with normal tissues have revealed marked differences in SEs landscapes. Enrichment analysis of the respective SE-target genes may help to explain normal physiological processes and tumor carcinogenesis and progression to some extent. LUAD-specific SE-regulated genes are involved in the “lung cancer” disease class and series of oncogenic pathways and are strongly associated with transcriptional disorder. In contrast, normal tissue-specific SE-regulated genes are prominent in fundamental cellular structures and activities (Zhang T. et al., 2020; Zhou et al., 2020; Yuan et al., 2021). Interestingly, from one study, only GO terms (like cell-cell adhesion) with significantly lower SEs counts were observed in cancer cells, which suggested that downregulation of “normal” genes that negatively regulated carcinogenesis could also lead to cancers even when cancer-specific SEs “did not work” (Li X. et al., 2019). A bioinformatics study revealed that cancer-specific H3K27Ac peak had a higher percentage of overlap with accessible chromatin regions to favor transcription than H3K27Ac peak of normal-specific (Jiang et al., 2022).

In conclusion, cancer-specific SEs are more susceptible to other regulators and play a crucial role in determining cancer progression.

Dentro’s whole genome sequence analysis of 2,658 cancer samples from 38 cancer types from the PCAWG project found that genomic instability allowed tumors to evolve into distinct subclonal populations with different biological behaviors among them (Dentro et al., 2021). Epigenomic diversity and instability are widely observed in heterogeneous subclonal populations of multiple types of cancer, involving differences in chromatin accessibility, methylation levels of gene structure (such as gene bodies, promoters, and enhancers of non-coding sequences), and histone profiles (Ding et al., 2019; Pastore et al., 2019; Cejas et al., 2021; El et al., 2023). The analysis of the H3K27Ac peak can directly clarify the distribution characteristics of SEs and assist in identifying associated tumor heterogeneity with them. Figure 3 illustrates the major regulators of SEs and enrichment pathways of SE-related genes in different subgroups of lung cancer.

Based on histone modification profiles, a study divided LUAD tissues into two groups that differed significantly in clinical features (e.g., histopathologic subtypes, lymph node invasion), gene mutation patterns, and prognosis (Yuan et al., 2021). Each had its own set of specific core regulatory factors and networks. Specific SE target genes in the poor prognosis group were enriched in invasion- and metastasis-related pathways (e.g., FOXM1, E2F pathway) and dedifferentiated state. In addition, core regulatory networks might shift at early stages, leading to the transition to a terminal stage. Super-enhancer RNA (SeRNA) is known to reflect SE properties and cell specificity (Chen and Liang, 2020; Tu et al., 2021). The distinct groups based on SeRNA profiles showed significant differences in immune infiltration, glycolysis levels, and prognosis (Song et al., 2022). Specifically, tissues in the worst prognostic group had the most chromosomally unstable regions, highest glucose uptake progression, hypoxia-immunotherapy response scores, the most high-risk solid subtype component, and lymph node metastases tendency. A bioinformatics analysis distinguished two subgroups of LUAD with significantly different somatic mutations, survival outcomes, and sensitivities to chemotherapeutic agents by SEs clustering analysis (Jiang et al., 2022). The transcription factor SOX2 usually cooperates with p63 to determine classical subtypes of squamous lung cancer (Watanabe et al., 2014). However, a highly expressed lineage-specific transcription factor Brn2 (an oncogenic factor associated with neuroendocrine carcinomas) was found to replace p63 in lung squamous cell carcinoma (LUSC) cells and to synergize with SOX2 in defining a new “neural” subtype with higher aggressiveness related to poorer prognosis (Sato et al., 2019).

Investigators have found that differential expression of the TFs ASCL1, NEUROD1, POU2F3, and YAP1 defined four molecular subtypes of SCLC, respectively, and that these TFs co-localized with SEs in the corresponding SCLC subtypes (Borromeo et al., 2016; Huang YH. et al., 2018; Rudin et al., 2019). Multiple lineage-specific TFs are also present in different subtypes of SCLC, and subtype switching occurs even in the same tumor (Ireland et al., 2020; Gay et al., 2021). Focusing on the SCLC-A subtype, Karine Pozo found that the TFs ASCL1, NKX2-1, and PROX1 were associated with SEs and that their interactions formed a regulatory network to control Notch signaling (Pozo et al., 2021). On this basis, Ranran Kong subdivided SCLC-A isoforms and discovered that collaborative NKX2-1/SOX1 regulation defined a new, unique SCLC-Aα subtype (Kong et al., 2022). A recent study found that the complex network of ASCL1-regulated SE-associated miRNAs influenced the identification of SCLC molecular subtypes (Miyakawa et al., 2022). SCLC subtypes identified by TFs differ in EMT status, immune gene expression, immunotherapy (or chemotherapy) benefit, and drug resistance (Gay et al., 2021; Qu et al., 2022). Therefore, subdividing the subtypes of SCLC step by step is an important research direction, which helps us better understand its pathogenesis and develop therapeutic approaches.

In recent years, large-scale, individualized molecular profiling of tumors has led to the discovery of an increasing number of targets and therapeutic options to improve the clinical prognosis of patients (Corces et al., 2018; Wu et al., 2022). Due to an inadequate understanding of etiology and pathogenesis and a lack of identified therapeutic targets, more biomarker-driven trials are required to advance therapeutic technologies (William and Glisson, 2011). Transcription of SE-driven genes is highly sensitive to BRD4 inhibitors since studies reveal BETi preferentially repress transcription of SE-related genes relative to TE-related genes (Lovén et al., 2013). The TFIIH complex subunit cyclin-dependent kinase 7 (CDK7), which controls the cell cycle and initiates transcription by Pol II, has emerged as a brand-new target for treating cancer (Larochelle et al., 2012; Suski et al., 2021). Plus, the catalytic member of the P-TEFb complex, cyclin-dependent kinase 9 (CDK9), promotes the transcriptional elongation of Pol II (Zhou et al., 2012). CDK7 can also phosphorylate to activate CDK9 (Larochelle et al., 2012). Therefore, BET, CDK7, and CDK9 may represent viable therapeutic targets for focusing on SE-dependent transcription. Table 1 demonstrates the clinical trials currently underway or have concluded. Besides, a series of preclinical studies of different drug combinations have laid the groundwork for more novel combination therapeutic regimens to enter clinical trials, as shown in Table 2. We provide a brief overview of the results reported.