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Cai YM, Lu ZQ, Li B, Huang JY, Zhang M, Chen C, Fan LY, Ma QY, He CY, Chen SN, Jiang Y, Li YM, Ning CB, Zhang FW, Wang WZ, Liu YZ, Zhang H, Jin M, Wang XY, Han JX, Xiong Z, Cai M, Huang CQ, Yang XJ, Zhu X, Zhu Y, Miao XP, Zhang SK, Wei YC, Tian JB. Genome-wide enhancer RNA profiling adds molecular links between genetic variation and human cancers. Mil Med Res 2024; 11:36. [PMID: 38863031 PMCID: PMC11165858 DOI: 10.1186/s40779-024-00539-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 05/17/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Dysregulation of enhancer transcription occurs in multiple cancers. Enhancer RNAs (eRNAs) are transcribed products from enhancers that play critical roles in transcriptional control. Characterizing the genetic basis of eRNA expression may elucidate the molecular mechanisms underlying cancers. METHODS Initially, a comprehensive analysis of eRNA quantitative trait loci (eRNAQTLs) was performed in The Cancer Genome Atlas (TCGA), and functional features were characterized using multi-omics data. To establish the first eRNAQTL profiles for colorectal cancer (CRC) in China, epigenomic data were used to define active enhancers, which were subsequently integrated with transcription and genotyping data from 154 paired CRC samples. Finally, large-scale case-control studies (34,585 cases and 69,544 controls) were conducted along with multipronged experiments to investigate the potential mechanisms by which candidate eRNAQTLs affect CRC risk. RESULTS A total of 300,112 eRNAQTLs were identified across 30 different cancer types, which exert their influence on eRNA transcription by modulating chromatin status, binding affinity to transcription factors and RNA-binding proteins. These eRNAQTLs were found to be significantly enriched in cancer risk loci, explaining a substantial proportion of cancer heritability. Additionally, tumor-specific eRNAQTLs exhibited high responsiveness to the development of cancer. Moreover, the target genes of these eRNAs were associated with dysregulated signaling pathways and immune cell infiltration in cancer, highlighting their potential as therapeutic targets. Furthermore, multiple ethnic population studies have confirmed that an eRNAQTL rs3094296-T variant decreases the risk of CRC in populations from China (OR = 0.91, 95%CI 0.88-0.95, P = 2.92 × 10-7) and Europe (OR = 0.92, 95%CI 0.88-0.95, P = 4.61 × 10-6). Mechanistically, rs3094296 had an allele-specific effect on the transcription of the eRNA ENSR00000155786, which functioned as a transcriptional activator promoting the expression of its target gene SENP7. These two genes synergistically suppressed tumor cell proliferation. Our curated list of variants, genes, and drugs has been made available in CancereRNAQTL ( http://canernaqtl.whu.edu.cn/#/ ) to serve as an informative resource for advancing this field. CONCLUSION Our findings underscore the significance of eRNAQTLs in transcriptional regulation and disease heritability, pinpointing the potential of eRNA-based therapeutic strategies in cancers.
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Affiliation(s)
- Yi-Min Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Ze-Qun Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jin-Yu Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Ming Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Can Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Lin-Yun Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Qian-Ying Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Chun-Yi He
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Shuo-Ni Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yuan Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yan-Min Li
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Cai-Bo Ning
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Fu-Wei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Wen-Zhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yi-Zhuo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Heng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Meng Jin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiao-Yang Wang
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jin-Xin Han
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhen Xiong
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ming Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Chao-Qun Huang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Xiao-Jun Yang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Xu Zhu
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Xiao-Ping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
| | - Yong-Chang Wei
- Department of Gastrointestinal Oncology, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
| | - Jian-Bo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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Wang Y, Jin W, Pan X, Liao W, Shen Q, Cai J, Gong W, Tian Y, Xu D, Li Y, Li J, Gong J, Zhang Z, Yuan X. Pig-eRNAdb: a comprehensive enhancer and eRNA dataset of pigs. Sci Data 2024; 11:157. [PMID: 38302497 PMCID: PMC10834423 DOI: 10.1038/s41597-024-02960-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Enhancers and the enhancer RNAs (eRNAs) have been strongly implicated in regulations of transcriptions. Based the multi-omics data (ATAC-seq, ChIP-seq and RNA-seq) from public databases, Pig-eRNAdb is a dataset that comprehensively integrates enhancers and eRNAs for pigs using the machine learning strategy, which incorporates 82,399 enhancers and 37,803 eRNAs from 607 samples across 15 tissues of pigs. This user-friendly dataset covers a comprehensive depth of enhancers and eRNAs annotation for pigs. The coordinates of enhancers and the expression patterns of eRNAs are downloadable. Besides, thousands of regulators on eRNAs, the target genes of eRNAs, the tissue-specific eRNAs, and the housekeeping eRNAs are also accessible as well as the sequence similarity of eRNAs with humans. Moreover, the tissue-specific eRNA-trait associations encompass 652 traits are also provided. It will crucially facilitate investigations on enhancers and eRNAs with Pig-eRNAdb as a reference dataset in pigs.
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Affiliation(s)
- Yifei Wang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Weiwei Jin
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiangchun Pan
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Weili Liao
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Qingpeng Shen
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiali Cai
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Wentao Gong
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yuhan Tian
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Dantong Xu
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yipeng Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhe Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
| | - Xiaolong Yuan
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
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3
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Zang Y, Ran X, Yuan J, Wu H, Wang Y, Li H, Teng H, Sun Z. Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer. Brief Bioinform 2024; 25:bbae023. [PMID: 38343327 PMCID: PMC10859687 DOI: 10.1093/bib/bbae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/11/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.
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Affiliation(s)
- Yue Zang
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences and Institute of Genomic Medicine, Wenzhou Medical University, China
| | - Xia Ran
- Liangzhu Laboratory, Zhejiang University Medical Center, China
| | - Jie Yuan
- BGI Education Center, University of Chinese Academy of Sciences, China
| | - Hao Wu
- Institute of Genomic Medicine, Wenzhou Medical University, China
| | - Youya Wang
- Institute of Genomic Medicine, Wenzhou Medical University, China
| | - He Li
- Institute of Genomic Medicine, Wenzhou Medical University, China
| | - Huajing Teng
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education) at Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Zhongsheng Sun
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Institute of Genomic Medicine, Wenzhou Medical University, and Beijing Institutes of Life Science, Chinese Academy of Sciences, Hangzhou, China
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Song C, Zhang G, Mu X, Feng C, Zhang Q, Song S, Zhang Y, Yin M, Zhang H, Tang H, Li C. eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse. Nucleic Acids Res 2024; 52:D81-D91. [PMID: 37889077 PMCID: PMC10767853 DOI: 10.1093/nar/gkad925] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/26/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.
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Affiliation(s)
- Chao Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Xinxin Mu
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shuang Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hang Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Huifang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
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Chen C, Liu Y, Luo M, Yang J, Chen Y, Wang R, Zhou J, Zang Y, Diao L, Han L. PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers. Nucleic Acids Res 2024; 52:D1400-D1406. [PMID: 37870463 PMCID: PMC10767806 DOI: 10.1093/nar/gkad916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/29/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
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Affiliation(s)
- Chengxuan Chen
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Yuan Liu
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Mei Luo
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Jingwen Yang
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Yamei Chen
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Runhao Wang
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Joseph Zhou
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Lixia Diao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Leng Han
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
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6
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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7
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Liu Y, Yang J, Wang T, Luo M, Chen Y, Chen C, Ronai Z, Zhou Y, Ruppin E, Han L. Expanding PROTACtable genome universe of E3 ligases. Nat Commun 2023; 14:6509. [PMID: 37845222 PMCID: PMC10579327 DOI: 10.1038/s41467-023-42233-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023] Open
Abstract
Proteolysis-targeting chimera (PROTAC) and other targeted protein degradation (TPD) molecules that induce degradation by the ubiquitin-proteasome system (UPS) offer new opportunities to engage targets that remain challenging to be inhibited by conventional small molecules. One fundamental element in the degradation process is the E3 ligase. However, less than 2% amongst hundreds of E3 ligases in the human genome have been engaged in current studies in the TPD field, calling for the recruiting of additional ones to further enhance the therapeutic potential of TPD. To accelerate the development of PROTACs utilizing under-explored E3 ligases, we systematically characterize E3 ligases from seven different aspects, including chemical ligandability, expression patterns, protein-protein interactions (PPI), structure availability, functional essentiality, cellular location, and PPI interface by analyzing 30 large-scale data sets. Our analysis uncovers several E3 ligases as promising extant PROTACs. In total, combining confidence score, ligandability, expression pattern, and PPI, we identified 76 E3 ligases as PROTAC-interacting candidates. We develop a user-friendly and flexible web portal ( https://hanlaboratory.com/E3Atlas/ ) aimed at assisting researchers to rapidly identify E3 ligases with promising TPD activities against specifically desired targets, facilitating the development of these therapies in cancer and beyond.
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Affiliation(s)
- Yuan Liu
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Jingwen Yang
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Tianlu Wang
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Mei Luo
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Yamei Chen
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Chengxuan Chen
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Ze'ev Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Yubin Zhou
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, 20892, MD, USA.
| | - Leng Han
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA.
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA.
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8
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Wang D, Cao W, Yang W, Jin W, Luo H, Niu X, Gong J. Pancan-MNVQTLdb: systematic identification of multi-nucleotide variant quantitative trait loci in 33 cancer types. NAR Cancer 2022; 4:zcac043. [PMID: 36568962 PMCID: PMC9773367 DOI: 10.1093/narcan/zcac043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/22/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Multi-nucleotide variants (MNVs) are defined as clusters of two or more nearby variants existing on the same haplotype in an individual. Recent studies have identified millions of MNVs in human populations, but their functions remain largely unknown. Numerous studies have demonstrated that single-nucleotide variants could serve as quantitative trait loci (QTLs) by affecting molecular phenotypes. Therefore, we propose that MNVs can also affect molecular phenotypes by influencing regulatory elements. Using the genotype data from The Cancer Genome Atlas (TCGA), we first identified 223 759 unique MNVs in 33 cancer types. Then, to decipher the functions of these MNVs, we investigated the associations between MNVs and six molecular phenotypes, including coding gene expression, miRNA expression, lncRNA expression, alternative splicing, DNA methylation and alternative polyadenylation. As a result, we identified 1 397 821 cis-MNVQTLs and 402 381 trans-MNVQTLs. We further performed survival analysis and identified 46 173 MNVQTLs associated with patient overall survival. We also linked the MNVQTLs to genome-wide association studies (GWAS) data and identified 119 762 MNVQTLs that overlap with existing GWAS loci. Finally, we developed Pancan-MNVQTLdb (http://gong_lab.hzau.edu.cn/mnvQTLdb/) for data retrieval and download. Pancan-MNVQTLdb will help decipher the functions of MNVs in different cancer types and be an important resource for genetic and cancer research.
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Affiliation(s)
| | | | | | - Weiwei Jin
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430074, China
| | - Haohui Luo
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430074, China
| | - Xiaohui Niu
- Correspondence may also be addressed to Xiaohui Niu. Tel: +86 027 87285085;
| | - Jing Gong
- To whom correspondence should be addressed. Tel: +86 027 87285085;
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9
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Luo M, Ye L, Chang R, Ye Y, Zhang Z, Liu C, Li S, Jing Y, Ruan H, Zhang G, He Y, Liu Y, Xue Y, Chen X, Guo AY, Liu H, Han L. Multi-omics characterization of autophagy-related molecular features for therapeutic targeting of autophagy. Nat Commun 2022; 13:6345. [PMID: 36289218 PMCID: PMC9606020 DOI: 10.1038/s41467-022-33946-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/07/2022] [Indexed: 02/08/2023] Open
Abstract
Autophagy is a major contributor to anti-cancer therapy resistance. Many efforts have been made to understand and overcome autophagy-mediated therapy resistance, but these efforts have been unsuccessful in clinical applications. In this study, we establish an autophagy signature to estimate tumor autophagy status. We then classify approximately 10,000 tumor samples across 33 cancer types from The Cancer Genome Atlas into autophagy score-high and autophagy score-low groups. We characterize the associations between multi-dimensional molecular features and tumor autophagy, and further analyse the effects of autophagy status on drug response. In contrast to the conventional view that the induction of autophagy serves as a key resistance mechanism during cancer therapy, our analysis reveals that autophagy induction may also sensitize cancer cells to anti-cancer drugs. We further experimentally validate this phenomenon for several anti-cancer drugs in vitro and in vivo, and reveal that autophagy inducers potentially sensitizes tumor cells to etoposide through downregulating the expression level of DDIT4. Our study provides a comprehensive landscape of molecular alterations associated with tumor autophagy and highlights an opportunity to leverage multi-omics analysis to utilize multiple drug sensitivity induced by autophagy.
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Affiliation(s)
- Mei Luo
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lin Ye
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ruimin Chang
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Youqiong Ye
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhao Zhang
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Chunjie Liu
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Shengli Li
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ying Jing
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Hang Ruan
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Guanxiong Zhang
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yi He
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yaoming Liu
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Yu Xue
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - An-Yuan Guo
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Hong Liu
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Leng Han
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA.
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10
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Integrative Analysis of N6-Methyladenosine-Related Enhancer RNAs Identifies Distinct Prognosis and Tumor Immune Micro-Environment Patterns in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2022; 14:cancers14194657. [PMID: 36230580 PMCID: PMC9563840 DOI: 10.3390/cancers14194657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/04/2022] [Accepted: 09/22/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Head and neck squamous cell carcinoma (HNSCC) has high morbidity and mortality. The interaction between immune cells and tumor cells in the tumor micro-environment is an important factor affecting the tumor progression and prognosis of HNSCC patients. More biomarkers and targets need to be explored to improve patient outcomes. The m6A modification on enhancer RNAs (eRNAs) is associated with the signature of active enhancer, and the function of m6A driving eRNAs in tumor progression has not been reported. In this study, we screened and identified a risk model containing 5 m6A-related eRNA, which can better predict the survival and immunotherapy outcome of patients. The role of m6A-related eRNA in HNSCC cells was verified in vitro. We also combined the risk score and multiple clinical features to construct a nomogram for predicting OS of HNSCC patients, which provides an effective quantitative analysis tool for guiding the personalized precise treatment for patients. Abstract At present, the prognostic value of N6-methyladenosine (m6A)-related enhancer RNAs (eRNAs) for head and neck squamous cell carcinoma (HNSCC) still remains unclear. Our study aims to explore the prognostic value of m6A-related eRNAs in HNSCC patients and their potential significance in immune infiltration and immunotherapy. We constructed a 5 m6A-related eRNAs risk model from The Cancer Genome Atlas (TCGA) HNSCC dataset, using univariate and multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the SRAMP website and in vitro experiments, it was verified that these 5 m6A-related eRNAs had m6A sites, the expression of which was regulated by corresponding m6A regulators. Moreover, we constructed a nomogram base on 5 m6A-related eRNAs and confirmed the consistency and robustness of an internal TCGA testing set. Further analysis found that the risk score was positively associated with low overall survival (OS), tumor cell metastasis, metabolic reprogramming, low immune surveillance, lower expression of immune-related genes, and higher expression of targeted genes. Finally, we verified that silencing MIR4435-2HG inhibited HNSCC cell migration and invasion. This study contributes to the understanding of the characteristics of m6A-related eRNAs in HNSCC and provides a reference for effective immunotherapy and targeted therapy.
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Enhancer RNAs (eRNAs) in Cancer: The Jacks of All Trades. Cancers (Basel) 2022; 14:cancers14081978. [PMID: 35454885 PMCID: PMC9030334 DOI: 10.3390/cancers14081978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary This review focuses on eRNAs and the several mechanisms by which they can regulate gene expression. In particular we describe here the most recent examples of eRNAs dysregulated in cancer or involved in the immune escape of tumor cells. Abstract Enhancer RNAs (eRNAs) are non-coding RNAs (ncRNAs) transcribed in enhancer regions. They play an important role in transcriptional regulation, mainly during cellular differentiation. eRNAs are tightly tissue- and cell-type specific and are induced by specific stimuli, activating promoters of target genes in turn. eRNAs usually have a very short half-life but in some cases, once activated, they can be stably expressed and acquire additional functions. Due to their critical role, eRNAs are often dysregulated in cancer and growing number of interactions with chromatin modifiers, transcription factors, and splicing machinery have been described. Enhancer activation and eRNA transcription have particular relevance also in inflammatory response, placing the eRNAs at the interplay between cancer and immune cells. Here, we summarize all the possible molecular mechanisms recently reported in association with eRNAs activity.
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