SEgene identifies links between super enhancers and gene expression across cell types
Abstract Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through hi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00533-x |
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| author | Norio Shinkai Ken Asada Hidenori Machino Ken Takasawa Satoshi Takahashi Nobuji Kouno Masaaki Komatsu Ryuji Hamamoto Syuzo Kaneko |
| author_facet | Norio Shinkai Ken Asada Hidenori Machino Ken Takasawa Satoshi Takahashi Nobuji Kouno Masaaki Komatsu Ryuji Hamamoto Syuzo Kaneko |
| author_sort | Norio Shinkai |
| collection | DOAJ |
| description | Abstract Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis. To address these limitations, we developed the super-enhancer to gene links (SE-to-gene Links) analysis, a platform named “SEgene” which incorporates the peak-to-gene links approach—a statistical method designed to reveal correlations between genes and peak regions ( https://github.com/hamamoto-lab/SEgene ). This platform enables a targeted evaluation of super-enhancer regions in relation to gene expression, facilitating the identification of super-enhancers that are functionally linked to transcriptional activity. Here, we demonstrate the application of SE-to-gene Links analysis to public datasets, confirming its efficacy in accurately detecting super-enhancers and identifying functionally associated genes. Additionally, SE-to-gene Links analysis identified ERBB2 as a significant gene of interest in the lung adenocarcinoma dataset from the National Cancer Center Japan cohort, suggesting a potential impact across multiple patient samples. Thus, the SE-to-gene Links analysis provides an analytical tool for evaluating super-enhancers as potential therapeutic targets, supporting the identification of clinically significant super-enhancer regions and their functionally associated genes. |
| format | Article |
| id | doaj-art-6bd289fbeb6642adb2f2548aa26b4521 |
| institution | OA Journals |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-6bd289fbeb6642adb2f2548aa26b45212025-08-20T01:53:14ZengNature Portfolionpj Systems Biology and Applications2056-71892025-05-0111111110.1038/s41540-025-00533-xSEgene identifies links between super enhancers and gene expression across cell typesNorio Shinkai0Ken Asada1Hidenori Machino2Ken Takasawa3Satoshi Takahashi4Nobuji Kouno5Masaaki Komatsu6Ryuji Hamamoto7Syuzo Kaneko8Division of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteAbstract Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis. To address these limitations, we developed the super-enhancer to gene links (SE-to-gene Links) analysis, a platform named “SEgene” which incorporates the peak-to-gene links approach—a statistical method designed to reveal correlations between genes and peak regions ( https://github.com/hamamoto-lab/SEgene ). This platform enables a targeted evaluation of super-enhancer regions in relation to gene expression, facilitating the identification of super-enhancers that are functionally linked to transcriptional activity. Here, we demonstrate the application of SE-to-gene Links analysis to public datasets, confirming its efficacy in accurately detecting super-enhancers and identifying functionally associated genes. Additionally, SE-to-gene Links analysis identified ERBB2 as a significant gene of interest in the lung adenocarcinoma dataset from the National Cancer Center Japan cohort, suggesting a potential impact across multiple patient samples. Thus, the SE-to-gene Links analysis provides an analytical tool for evaluating super-enhancers as potential therapeutic targets, supporting the identification of clinically significant super-enhancer regions and their functionally associated genes.https://doi.org/10.1038/s41540-025-00533-x |
| spellingShingle | Norio Shinkai Ken Asada Hidenori Machino Ken Takasawa Satoshi Takahashi Nobuji Kouno Masaaki Komatsu Ryuji Hamamoto Syuzo Kaneko SEgene identifies links between super enhancers and gene expression across cell types npj Systems Biology and Applications |
| title | SEgene identifies links between super enhancers and gene expression across cell types |
| title_full | SEgene identifies links between super enhancers and gene expression across cell types |
| title_fullStr | SEgene identifies links between super enhancers and gene expression across cell types |
| title_full_unstemmed | SEgene identifies links between super enhancers and gene expression across cell types |
| title_short | SEgene identifies links between super enhancers and gene expression across cell types |
| title_sort | segene identifies links between super enhancers and gene expression across cell types |
| url | https://doi.org/10.1038/s41540-025-00533-x |
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