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Tu G, Wang X, Xia R, Song B. m6A-TCPred: a web server to predict tissue-conserved human m 6A sites using machine learning approach. BMC Bioinformatics 2024; 25:127. [PMID: 38528499 PMCID: PMC10962094 DOI: 10.1186/s12859-024-05738-1] [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: 11/06/2023] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level. RESULTS We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively. CONCLUSION Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .
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Affiliation(s)
- Gang Tu
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Xuan Wang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L7 8TX, UK.
| | - Rong Xia
- Department of Financial and Actuarial Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Bowen Song
- Department of Public Health, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
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Chen Y, Xie Y, Bi L, Ci H, Li W, Liu D. A novel serum m 7G-harboring microRNA signature for cancer detection. Front Genet 2024; 15:1270302. [PMID: 38384713 PMCID: PMC10879580 DOI: 10.3389/fgene.2024.1270302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Background: Emerging evidence points to the exceptional importance and value of m7G alteration in the diagnosis and prognosis of cancers. Nonetheless, a biomarker for precise screening of various cancer types has not yet been developed based on serum m7G-harboring miRNAs. Methods: A total of 20,702 serum samples, covering 12 cancer types and consisting of 7,768 cancer samples and 12,934 cancer-free samples were used in this study. A m7G target miRNA diagnostic signature (m7G-miRDS) was established through the least absolute shrinkage and selection operator (LASSO) analyses in a training dataset (n = 10,351), and validated in a validation dataset (n = 10,351). Results: The m7G-miRDS model, a 12 m7G-target-miRNAs signature, demonstrated high accuracy and was qualified for cancer detection. In the training and validation cohort, the area under the curve (AUC) reached 0.974 (95% CI 0.971-0.977) and 0.972 (95% CI 0.969-0.975), respectively. The m7G-miRDS showed superior sensitivity in each cancer type and had a satisfactory AUC in identifying bladder cancer, lung cancer and esophageal cancer. Additionally, the diagnostic performance of m7G-miRDS was not interfered by the gender, age and benign disease. Conclusion: Our results greatly extended the value of serum circulating miRNAs and m7G in cancer detection, and provided a new direction and strategy for the development of novel biomarkers with high accuracy, low cost and less invasiveness for mass cancer screening, such as ncRNA modification.
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Affiliation(s)
- Yaxin Chen
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yufang Xie
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Jiujiang First People’s Hospital, Jiujiang, Jiangxi, China
| | - Liyun Bi
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Ci
- Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dan Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Jiang J, Song B, Meng J, Zhou J. Tissue-specific RNA methylation prediction from gene expression data using sparse regression models. Comput Biol Med 2024; 169:107892. [PMID: 38171264 DOI: 10.1016/j.compbiomed.2023.107892] [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] [Received: 07/20/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024]
Abstract
N6-methyladenosine (m6A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m6A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.
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Affiliation(s)
- Jie Jiang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
| | - Bowen Song
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
| | - Jingxian Zhou
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University Entrepreneur College (Taicang), Taicang, Suzhou, Jiangsu Province, 215400, China; Department of Computer Science, University of Liverpool, L69 7ZB, Liverpool, United Kingdom.
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Rigden DJ, Fernández XM. The 2024 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2024; 52:D1-D9. [PMID: 38035367 PMCID: PMC10767945 DOI: 10.1093/nar/gkad1173] [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: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023] Open
Abstract
The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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Wei H, Xu Y, Lin L, Li Y, Zhu X. A review on the role of RNA methylation in aging-related diseases. Int J Biol Macromol 2024; 254:127769. [PMID: 38287578 DOI: 10.1016/j.ijbiomac.2023.127769] [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] [Received: 09/18/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 01/31/2024]
Abstract
Senescence is the underlying mechanism of organism aging and is robustly regulated at the post-transcriptional level. This regulation involves the chemical modifications, of which the RNA methylation is the most common. Recently, a rapidly growing number of studies have demonstrated that methylation is relevant to aging and aging-associated diseases. Owing to the rapid development of detection methods, the understanding on RNA methylation has gone deeper. In this review, we summarize the current understanding on the influence of RNA modification on cellular senescence, with a focus on mRNA methylation in aging-related diseases, and discuss the emerging potential of RNA modification in diagnosis and therapy.
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Affiliation(s)
- Hong Wei
- Reproductive Center, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Department of Neurology, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Central Laboratory of the Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - Yuhao Xu
- Medical School, Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - Li Lin
- Reproductive Center, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Central Laboratory of the Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - Yuefeng Li
- Medical School, Jiangsu University, Zhenjiang, Jiangsu 212001, China.
| | - Xiaolan Zhu
- Reproductive Center, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Central Laboratory of the Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China.
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Ren J, Chen X, Zhang Z, Shi H, Wu S. DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features. Front Genet 2023; 14:1334132. [PMID: 38169665 PMCID: PMC10758487 DOI: 10.3389/fgene.2023.1334132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 01/05/2024] Open
Abstract
Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.
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Affiliation(s)
- Jinjin Ren
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaozhen Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Zhengqian Zhang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Haoran Shi
- Institute of Applied Microbiology, Research Center for BioSystems, Land Use, and Nutrition (IFZ), Justus-Liebig-University Giessen, Giessen, Germany
| | - Shuxiang Wu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, Fujian, China
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