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Mulero-Hernández J, Mironov V, Miñarro-Giménez JA, Kuiper M, Fernández-Breis JT. Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation. Nucleic Acids Res 2024:gkae566. [PMID: 38967009 DOI: 10.1093/nar/gkae566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 07/06/2024] Open
Abstract
Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.
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Affiliation(s)
- Juan Mulero-Hernández
- Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, Instituto Murciano de Investigación Biosanitaria (IMIB),30100 Murcia, Spain
| | - Vladimir Mironov
- Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
| | - José Antonio Miñarro-Giménez
- Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, Instituto Murciano de Investigación Biosanitaria (IMIB),30100 Murcia, Spain
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
| | - Jesualdo Tomás Fernández-Breis
- Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, Instituto Murciano de Investigación Biosanitaria (IMIB),30100 Murcia, Spain
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2
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Qi D, Liu C, Zhang Z, Liu X, Kang P. Construction of a Lung Adenocarcinoma Prognostic Model Utilizing Serine and Glycine Metabolism-Related Genes. J Proteome Res 2024; 23:797-808. [PMID: 38212294 DOI: 10.1021/acs.jproteome.3c00700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
The objective of this study was to construct a prognostic model by utilizing serine/glycine metabolism-related genes (SGMGs), thus establishing a risk score for lung adenocarcinoma (LUAD). Based on the TCGA-LUAD and SGMG data set, two subtypes with different SGMG expression levels were identified by clustering analysis. Thirteen differential expression genes were used to construct RiskScore by Cox regression. GSE72094 data set was used for validation. The survival characteristics, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ABCC12, RIC3, CYP4B1, SFTPB, CACNA2D2, IGF2BP1, NTSR1, DKK1, CREG2, PITX3, RGS20, FETUB, and IGFBP1. Patients in the low-risk (LR) group exhibited a superior overall survival. In addition, aDCs, iDSs, mast cells, neutrophils, HLA, and type II IFN were more abundant in the LR group with higher IPS scores and lower TIDE scores. In contrast, NK cells, APC coinhibition, and MHC-I were more common in the high-risk (HR) group, which may be more sensitive to chemotherapy drugs such as cisplatin, oxaliplatin, and nilotinib. RiskScore was a promising biomarker that can be used to distinguish LUAD prognosis, immune features, and sensitivity to chemotherapy drugs.
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Affiliation(s)
- Dongdong Qi
- Department of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Chengjun Liu
- Department of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Zuwang Zhang
- Department of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Xun Liu
- Department of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Poming Kang
- Department of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
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3
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Wang Q, Zhang J, Liu Z, Duan Y, Li C. Integrative approaches based on genomic techniques in the functional studies on enhancers. Brief Bioinform 2023; 25:bbad442. [PMID: 38048082 PMCID: PMC10694556 DOI: 10.1093/bib/bbad442] [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: 08/28/2023] [Revised: 10/22/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.
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Affiliation(s)
- Qilin Wang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Junyou Zhang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Zhaoshuo Liu
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Yingying Duan
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chunyan Li
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing 100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
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4
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Phan LT, Oh C, He T, Manavalan B. A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome. Proteomics 2023; 23:e2200409. [PMID: 37021401 DOI: 10.1002/pmic.202200409] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/18/2023] [Accepted: 03/27/2023] [Indexed: 04/07/2023]
Abstract
Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.
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Affiliation(s)
- Le Thi Phan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Changmin Oh
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Tao He
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
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5
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Luo ZH, Shi MW, Zhang Y, Wang DY, Tong YB, Pan XL, Cheng S. CenhANCER: a comprehensive cancer enhancer database for primary tissues and cell lines. Database (Oxford) 2023; 2023:7173547. [PMID: 37207350 DOI: 10.1093/database/baad022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/09/2023] [Accepted: 03/21/2023] [Indexed: 05/21/2023]
Abstract
Enhancers, which are key tumorigenic factors with wide applications for subtyping, diagnosis and treatment of cancer, are attracting increasing attention in the cancer research. However, systematic analysis of cancer enhancers poses a challenge due to the lack of integrative data resources, especially those from tumor primary tissues. To provide a comprehensive enhancer profile across cancer types, we developed a cancer enhancer database CenhANCER by curating public resources including all the public H3K27ac ChIP-Seq data from 805 primary tissue samples and 671 cell line samples across 41 cancer types. In total, 57 029 408 typical enhancers, 978 411 super-enhancers and 226 726 enriched transcription factors were identified. We annotated the super-enhancers with chromatin accessibility regions, cancer expression quantitative trait loci (eQTLs), genotype-tissue expression eQTLs and genome-wide association study risk single nucleotide polymorphisms (SNPs) for further functional analysis. The identified enhancers were highly consistent with accessible chromatin regions in the corresponding cancer types, and all the 10 super-enhancer regions identified from one colorectal cancer study were recapitulated in our CenhANCER, both of which testified the high quality of our data. CenhANCER with high-quality cancer enhancer candidates and transcription factors that are potential therapeutic targets across multiple cancer types provides a credible resource for single cancer analysis and for comparative studies of various cancer types. Database URL http://cenhancer.chenzxlab.cn/.
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Affiliation(s)
- Zhi-Hui Luo
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei 430030, P.R. China
| | - Meng-Wei Shi
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, 97 Buxin Road, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Road, Shenzhen 518000, China
| | - Yuan Zhang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, 97 Buxin Road, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Road, Shenzhen 518000, China
| | - Dan-Yang Wang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
| | - Yi-Bo Tong
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
| | - Xue-Ling Pan
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, No. 1, Shizishan Street, Wuhan, Hubei 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, 97 Buxin Road, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Road, Shenzhen 518000, China
| | - ShanShan Cheng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei 430030, P.R. China
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6
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Typical Enhancers, Super-Enhancers, and Cancers. Cancers (Basel) 2022; 14:cancers14184375. [PMID: 36139535 PMCID: PMC9496678 DOI: 10.3390/cancers14184375] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/29/2022] [Accepted: 09/05/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary The cancer genome has been exhaustively studied upon the advent of Next-Generation Sequencing technologies. Coding and non-coding sequences have been defined as hotspots of genomic variations that affect the naïve gene expression programs established in normal cells, thus working as endogenous drivers of carcinogenesis. In this review, we comprehensively summarize fundamental aspects of gene expression regulation, with emphasis on the impact of sequence and structural variations mapped across non-coding cis-acting elements of genes encoding for tumor-related transcription factors. Chromatin architecture, epigenome reprogramming, transcriptional enhancers and Super-enhancers, oncogene regulation, cutting-edge technologies, and pharmacological treatment are substantially highlighted. Abstract Non-coding segments of the human genome are enriched in cis-regulatory modules that constitute functional elements, such as transcriptional enhancers and Super-enhancers. A hallmark of cancer pathogenesis is the dramatic dysregulation of the “archetype” gene expression profiles of normal human cells. Genomic variations can promote such deficiencies when occurring across enhancers and Super-enhancers, since they affect their mechanistic principles, their functional capacity and specificity, and the epigenomic features of the chromatin microenvironment across which these regulatory elements reside. Here, we comprehensively describe: fundamental mechanisms of gene expression dysregulation in cancers that involve genomic abnormalities within enhancers’ and Super-enhancers’ (SEs) sequences, which alter the expression of oncogenic transcription factors (TFs); cutting-edge technologies applied for the analysis of variation-enriched hotspots of the cancer genome; and pharmacological approaches for the treatment of Super-enhancers’ aberrant function. Finally, we provide an intratumor meta-analysis, which highlights that genomic variations in transcription-factor-driven tumors are accompanied overexpression of genes, a portion of which encodes for additional cancer-related transcription factors.
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7
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Mulero Hernández J, Fernández-Breis JT. Analysis of the landscape of human enhancer sequences in biological databases. Comput Struct Biotechnol J 2022; 20:2728-2744. [PMID: 35685360 PMCID: PMC9168495 DOI: 10.1016/j.csbj.2022.05.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 12/01/2022] Open
Abstract
The process of gene regulation extends as a network in which both genetic sequences and proteins are involved. The levels of regulation and the mechanisms involved are multiple. Transcription is the main control mechanism for most genes, being the downstream steps responsible for refining the transcription patterns. In turn, gene transcription is mainly controlled by regulatory events that occur at promoters and enhancers. Several studies are focused on analyzing the contribution of enhancers in the development of diseases and their possible use as therapeutic targets. The study of regulatory elements has advanced rapidly in recent years with the development and use of next generation sequencing techniques. All this information has generated a large volume of information that has been transferred to a growing number of public repositories that store this information. In this article, we analyze the content of those public repositories that contain information about human enhancers with the aim of detecting whether the knowledge generated by scientific research is contained in those databases in a way that could be computationally exploited. The analysis will be based on three main aspects identified in the literature: types of enhancers, type of evidence about the enhancers, and methods for detecting enhancer-promoter interactions. Our results show that no single database facilitates the optimal exploitation of enhancer data, most types of enhancers are not represented in the databases and there is need for a standardized model for enhancers. We have identified major gaps and challenges for the computational exploitation of enhancer data.
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Affiliation(s)
- Juan Mulero Hernández
- Dept. Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, Spain
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8
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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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9
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Gao T, Zheng Z, Pan Y, Zhu C, Wei F, Yuan J, Sun R, Fang S, Wang N, Zhou Y, Qian J. scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species. Nucleic Acids Res 2021; 50:D371-D379. [PMID: 34761274 PMCID: PMC8728125 DOI: 10.1093/nar/gkab1032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/04/2021] [Accepted: 10/19/2021] [Indexed: 12/14/2022] Open
Abstract
Previous studies on enhancers and their target genes were largely based on bulk samples that represent ‘average’ regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populations. In recent years, single-cell sequencing technology has enabled the profiling of open chromatin accessibility at the single-cell level (scATAC-seq), which can be used to annotate the enhancers and promoters in specific cell types. A comprehensive resource is highly desirable for exploring how the enhancers regulate the target genes at the single-cell level. Hence, we designed a single-cell database scEnhancer (http://enhanceratlas.net/scenhancer/), covering 14 527 776 enhancers and 63 658 600 enhancer-gene interactions from 1 196 906 single cells across 775 tissue/cell types in three species. An unsupervised learning method was employed to sort and combine tens or hundreds of single cells in each tissue/cell type to obtain the consensus enhancers. In addition, we utilized a cis-regulatory network algorithm to identify the enhancer-gene connections. Finally, we provided a user-friendly platform with seven useful modules to search, visualize, and browse the enhancers/genes. This database will facilitate the research community towards a functional analysis of enhancers at the single-cell level.
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Affiliation(s)
- Tianshun Gao
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.,Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Zilong Zheng
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Yihang Pan
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.,Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Chengming Zhu
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Fuxin Wei
- Department of Orthopaedics, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Jinqiu Yuan
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.,Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Rui Sun
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.,Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Shuo Fang
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.,Department of Oncology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Nan Wang
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Yang Zhou
- Big Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Jiang Qian
- The Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD 21231, USA.,The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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10
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Zhou S, Han Y, Li J, Pi X, Lyu J, Xiang S, Zhou X, Chen X, Wang Z, Yang R. New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation. Front Oncol 2021; 11:745384. [PMID: 34722301 PMCID: PMC8548670 DOI: 10.3389/fonc.2021.745384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/21/2021] [Indexed: 11/23/2022] Open
Abstract
Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.
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Affiliation(s)
- Sitong Zhou
- Department of Dermatology, The First People's Hospital of Foshan, Foshan, China
| | - Yuanyuan Han
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, China
| | - Jiehua Li
- Department of Dermatology, The First People's Hospital of Foshan, Foshan, China
| | - Xiaobing Pi
- Department of Dermatology, The First People's Hospital of Foshan, Foshan, China
| | - Jin Lyu
- Department of Pathology, The First People's Hospital of Foshan, Foshan, China
| | - Shijian Xiang
- Department of Pharmacy, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xinzhu Zhou
- The Second School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Xiaodong Chen
- Department of Burn Surgery and Skin Regeneration, The First People's Hospital of Foshan, Foshan, China
| | - Zhengguang Wang
- Department of Orthopedics, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ronghua Yang
- Department of Burn Surgery and Skin Regeneration, The First People's Hospital of Foshan, Foshan, China
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Kaur H, Kumar R, Lathwal A, Raghava GPS. Computational resources for identification of cancer biomarkers from omics data. Brief Funct Genomics 2021; 20:213-222. [PMID: 33788922 DOI: 10.1093/bfgp/elab021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/11/2021] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.
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Xiao J, Jin X, Zhang C, Zou H, Chang Z, Han N, Li X, Zhang Y, Li Y. Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma. Am J Cancer Res 2021; 11:3060-3073. [PMID: 33537074 PMCID: PMC7847679 DOI: 10.7150/thno.54150] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background: Enhancers are emerging regulatory regions controlling gene expression in diverse cancer types. However, the functions of enhancer regulatory circuit perturbations driven by copy number variations (CNVs) in malignant glioma are unclear. Therefore, we aimed to investigate the comprehensive enhancer regulatory perturbation and identify potential biomarkers in glioma. Results: We performed a meta-analysis of the enhancer centered regulatory circuit perturbations in 683 gliomas by integrating CNVs, gene expression, and transcription factors (TFs) binding. We found widespread CNVs of enhancers during glioma progression, and CNVs were associated with the perturbations of enhancer activities. In particular, the degree of perturbations for amplified enhancers was much greater accompanied by the glioma malignant progression. In addition, CNVs and enhancers cooperatively regulated the expressions of cancer-related genes. Genome-wide TF binding profiles revealed that enhancers were pervasively regulated by TFs. A network-based analysis of TF-enhancer-gene regulatory circuits revealed a core TF-gene module (58 interactions including seven genes and 14 TFs) that was associated survival of patients with glioma (p < 0.001). Finally, we validated this prognosis-associated TF-gene regulatory module in an independent cohort. In summary, our analyses provided new molecular insights for enhancer-centered transcriptional perturbation in glioma therapy. Conclusion: Integrative analysis revealed enhancer regulatory perturbations in glioma and also identified a network module that was associated with patient survival, thereby providing novel insights into enhancer-centered cancer therapy.
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