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Sahu VK, Sur S, Agarwal S, Madhyastha H, Ranjan A, Basu S. Unveiling theranostic potential: Insights into cell-free microRNA-protein interactions. Biophys Chem 2025; 322:107421. [PMID: 40048894 DOI: 10.1016/j.bpc.2025.107421] [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: 03/31/2024] [Revised: 09/02/2024] [Accepted: 03/01/2025] [Indexed: 04/27/2025]
Abstract
MicroRNAs (miRNAs) belong to a short endogenous class of non-coding RNAs which have been well studied for their crucial role in regulating cellular homeostasis. Their role in the modulation of diverse biological pathways by interacting with cellular proteins, genes, and RNAs through cellular communication projects them as promising biomarkers and therapeutic targets. However, studying miRNA-protein interactions specific to disease in the extracellular or cell-free environments for drug discovery and biomarker establishment is challenging and resource-intensive due to their structural complexities. In this study, we present a computational approach to uncover patterns in miRNA-protein interactions in the cell-free milieu leveraging existing experimental data. We employed motif discovery tools, extracted motifs from 3D protein and miRNA structures, and conducted molecular docking analyses to identify and rank these interactions. This in silico-based approach reveals 204 and 2874 consensus sequences in miRNAs and proteins, respectively, within the interactome highlighting their potential roles in the cardiovascular diseases, neurological disorders, and cancers. The role of proteins like METTL3 and AGO2 and miRNAs such as hsa-miR-484 and hsa-miR-30 families, hsa-mir-126-5p has been discussed contextually. Additionally, we discovered simple sequence repeats in the consensus patterns having unexplored functional roles. Our observations provide new insights into the extracellular miRNA-protein interactions that may drive disease initiation and progression offering potential avenues for overcoming challenges like therapy relapse and drug inefficacy. The results of our analysis are available in the miRPin database (https://www.mirna.in/miRPin).
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Affiliation(s)
- Vishal Kumar Sahu
- Cancer and Translational Research Centre, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Tathawade, Pune 411033, India
| | - Subhayan Sur
- Cancer and Translational Research Centre, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Tathawade, Pune 411033, India
| | - Sanjana Agarwal
- Cancer and Translational Research Centre, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Tathawade, Pune 411033, India
| | - Harishkumar Madhyastha
- Department of Cardiovascular Physiology, Faculty of Medicine, University of Miyazaki, Miyazaki 8891692, Japan
| | - Amit Ranjan
- Cancer and Translational Research Centre, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Tathawade, Pune 411033, India.
| | - Soumya Basu
- Cancer and Translational Research Centre, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Tathawade, Pune 411033, India.
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2
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Cui S, Yu S, Huang HY, Lin YCD, Huang Y, Zhang B, Xiao J, Zuo H, Wang J, Li Z, Li G, Ma J, Chen B, Zhang H, Fu J, Wang L, Huang HD. miRTarBase 2025: updates to the collection of experimentally validated microRNA-target interactions. Nucleic Acids Res 2025; 53:D147-D156. [PMID: 39578692 PMCID: PMC11701613 DOI: 10.1093/nar/gkae1072] [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: 09/15/2024] [Revised: 10/02/2024] [Accepted: 11/14/2024] [Indexed: 11/24/2024] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs (18-26 nucleotides) that regulate gene expression by interacting with target mRNAs, affecting various physiological and pathological processes. miRTarBase, a database of experimentally validated miRNA-target interactions (MTIs), now features over 3 817 550 validated MTIs from 13 690 articles, significantly expanding its previous version. The updated database includes miRNA interactions with therapeutic agents, revealing roles in drug resistance and therapeutic strategies. It also highlights miRNAs as predictive, safety and monitoring biomarkers for toxicity assessment, clinical treatment guidance and therapeutic optimization. The expansion of miRNA-mRNA and miRNA-miRNA networks allows the identification of key regulatory genes and co-regulatory miRNAs, providing deeper insights into miRNA functions and critical target genes. Information on oxidized miRNA sequences has been added, shedding light on how oxidative modifications influence miRNA targeting and regulation. The integration of the LLAMA3 model into the NLP pipeline, alongside prompt engineering, enables the efficient identification of MTIs and miRNA-disease associations without large training datasets. An updated data integration and a redesigned user interface enhance accessibility, reinforcing miRTarBase as an essential resource for molecular oncology, drug development and related fields. The updated miRTarBase is available at https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2025.
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Affiliation(s)
- Shidong Cui
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Sicong Yu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Yang-Chi-Dung Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Bojian Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jihan Xiao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Huali Zuo
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiayi Wang
- Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
| | - Zhuoran Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Guanghao Li
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiajun Ma
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Baiming Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Haoxuan Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiehui Fu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Liang Wang
- Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China
- Department of Endocrinology, Key Laboratory of Endocrinology of National Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
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3
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Liu Y, Min Z, Mo J, Ju Z, Chen J, Liang W, Zhang L, Li H, Chan GCF, Wei Y, Zhang W. ExomiRHub: A comprehensive database for hosting and analyzing human disease-related extracellular microRNA transcriptomics data. Comput Struct Biotechnol J 2024; 23:3104-3116. [PMID: 39219717 PMCID: PMC11362623 DOI: 10.1016/j.csbj.2024.07.024] [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: 04/15/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 09/04/2024] Open
Abstract
Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.
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Affiliation(s)
- Yang Liu
- The Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou 510182, China
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen 518026, China
| | - Zhuochao Min
- School of Zoology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jing Mo
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen 518026, China
| | - Zhen Ju
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jianliang Chen
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Weiling Liang
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Lantian Zhang
- The Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou 510182, China
| | - Hanguang Li
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Godfrey Chi-Fung Chan
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Yanjie Wei
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenliang Zhang
- The Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou 510182, China
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen 518026, China
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4
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Miceli RT, Chen T, Nose Y, Tichkule S, Brown B, Fullard JF, Saulsbury MD, Heyliger SO, Gnjatic S, Kyprianou N, Cordon‐Cardo C, Sahoo S, Taioli E, Roussos P, Stolovitzky G, Gonzalez‐Kozlova E, Dogra N. Extracellular vesicles, RNA sequencing, and bioinformatic analyses: Challenges, solutions, and recommendations. J Extracell Vesicles 2024; 13:e70005. [PMID: 39625409 PMCID: PMC11613500 DOI: 10.1002/jev2.70005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 09/20/2024] [Accepted: 10/07/2024] [Indexed: 12/06/2024] Open
Abstract
Extracellular vesicles (EVs) are heterogeneous entities secreted by cells into their microenvironment and systemic circulation. Circulating EVs carry functional small RNAs and other molecular footprints from their cell of origin, and thus have evident applications in liquid biopsy, therapeutics, and intercellular communication. Yet, the complete transcriptomic landscape of EVs is poorly characterized due to critical limitations including variable protocols used for EV-RNA extraction, quality control, cDNA library preparation, sequencing technologies, and bioinformatic analyses. Consequently, there is a gap in knowledge and the need for a standardized approach in delineating EV-RNAs. Here, we address these gaps by describing the following points by (1) focusing on the large canopy of the EVs and particles (EVPs), which includes, but not limited to - exosomes and other large and small EVs, lipoproteins, exomeres/supermeres, mitochondrial-derived vesicles, RNA binding proteins, and cell-free DNA/RNA/proteins; (2) examining the potential functional roles and biogenesis of EVPs; (3) discussing various transcriptomic methods and technologies used in uncovering the cargoes of EVPs; (4) presenting a comprehensive list of RNA subtypes reported in EVPs; (5) describing different EV-RNA databases and resources specific to EV-RNA species; (6) reviewing established bioinformatics pipelines and novel strategies for reproducible EV transcriptomics analyses; (7) emphasizing the significant need for a gold standard approach in identifying EV-RNAs across studies; (8) and finally, we highlight current challenges, discuss possible solutions, and present recommendations for robust and reproducible analyses of EVP-associated small RNAs. Overall, we seek to provide clarity on the transcriptomics landscape, sequencing technologies, and bioinformatic analyses of EVP-RNAs. Detailed portrayal of the current state of EVP transcriptomics will lead to a better understanding of how the RNA cargo of EVPs can be used in modern and targeted diagnostics and therapeutics. For the inclusion of different particles discussed in this article, we use the terms large/small EVs, non-vesicular extracellular particles (NVEPs), EPs and EVPs as defined in MISEV guidelines by the International Society of Extracellular Vesicles (ISEV).
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Affiliation(s)
- Rebecca T. Miceli
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Tzu‐Yi Chen
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Yohei Nose
- Department of ImmunologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Swapnil Tichkule
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Briana Brown
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - John F. Fullard
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Genetics and Genomics SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Center for Disease Neurogenetics, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Friedman Brain Institute, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Marilyn D. Saulsbury
- Department of Pharmaceutical Sciences, School of PharmacyHampton UniversityHamptonVirginiaUSA
| | - Simon O. Heyliger
- Department of Pharmaceutical Sciences, School of PharmacyHampton UniversityHamptonVirginiaUSA
| | - Sacha Gnjatic
- Department of ImmunologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Natasha Kyprianou
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of UrologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Carlos Cordon‐Cardo
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Susmita Sahoo
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Cardiovascular Research Institute, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Emanuela Taioli
- Department of Population Health and ScienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Thoracic SurgeryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Panos Roussos
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Genetics and Genomics SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Center for Disease Neurogenetics, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Friedman Brain Institute, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Center for Precision Medicine and Translational TherapeuticsJames J. Peters VA Medicinal CenterBronxNew YorkUSA
- Mental Illness Research Education and Clinical Center (MIRECC)James J. Peters VA Medicinal CenterBronxNew YorkUSA
| | - Gustavo Stolovitzky
- Department of Genetics and Genomics SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Biomedical Data Sciences Hub (Bio‐DaSH), Department of Pathology, NYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Edgar Gonzalez‐Kozlova
- Department of ImmunologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Navneet Dogra
- Department of Pathology, Molecular and Cell‐Based MedicineIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Genetics and Genomics SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Genomics Institute, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- AI and Human HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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5
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Zhuang T, Wang S, Yu X, He X, Guo H, Ou C. Current status and future perspectives of platelet-derived extracellular vesicles in cancer diagnosis and treatment. Biomark Res 2024; 12:88. [PMID: 39183323 PMCID: PMC11346179 DOI: 10.1186/s40364-024-00639-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Accepted: 08/12/2024] [Indexed: 08/27/2024] Open
Abstract
Platelets are a significant component of the cell population in the tumour microenvironment (TME). Platelets influence other immune cells and perform cross-talk with tumour cells, playing an important role in tumour development. Extracellular vesicles (EVs) are small membrane vesicles released from the cells into the TME. They can transfer biological information, including proteins, nucleic acids, and metabolites, from secretory cells to target receptor cells. This process affects the progression of various human diseases, particularly cancer. In recent years, several studies have demonstrated that platelet-derived extracellular vesicles (PEVs) can help regulate the malignant biological behaviours of tumours, including malignant proliferation, resistance to cell death, invasion and metastasis, metabolic reprogramming, immunity, and angiogenesis. Consequently, PEVs have been identified as key regulators of tumour progression. Therefore, targeting PEVs is a potential strategy for tumour treatment. Furthermore, the extensive use of nanomaterials in medical research has indicated that engineered PEVs are ideal delivery systems for therapeutic drugs. Recent studies have demonstrated that PEV engineering technologies play a pivotal role in the treatment of tumours by combining photothermal therapy, immunotherapy, and chemotherapy. In addition, aberrant changes in PEVs are closely associated with the clinicopathological features of patients with tumours, which may serve as liquid biopsy markers for early diagnosis, monitoring disease progression, and the prognostic assessment of patients with tumours. A comprehensive investigation into the role and potential mechanisms of PEVs in tumourigenesis may provide novel diagnostic biomarkers and potential therapeutic strategies for treating human tumours.
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Affiliation(s)
- Tongtao Zhuang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Shenrong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xiaoqian Yu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xiaoyun He
- Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Hongbin Guo
- Department of Orthopedics, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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6
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Basso M, Gori A, Nardella C, Palviainen M, Holcar M, Sotiropoulos I, Bobis‐Wozowicz S, D'Agostino VG, Casarotto E, Ciani Y, Suetsugu S, Gualerzi A, Martin‐Jaular L, Boselli D, Kashkanova A, Parisse P, Lippens L, Pagliuca M, Blessing M, Frigerio R, Fourniols T, Meliciano A, Fietta A, Fioretti PV, Soroczyńska K, Picciolini S, Salviano‐Silva A, Bergese P, Zocco D, Chiari M, Jenster G, Waldron L, Milosavljevic A, Nolan J, Monopoli MP, Witwer KW, Bussolati B, Di Vizio D, Falcon Perez J, Lenassi M, Cretich M, Demichelis F. International Society for Extracellular Vesicles Workshop. QuantitatEVs: multiscale analyses, from bulk to single extracellular vesicle. JOURNAL OF EXTRACELLULAR BIOLOGY 2024; 3:e137. [PMID: 38405579 PMCID: PMC10883470 DOI: 10.1002/jex2.137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/19/2023] [Accepted: 12/23/2023] [Indexed: 02/27/2024]
Abstract
The 'QuantitatEVs: multiscale analyses, from bulk to single vesicle' workshop aimed to discuss quantitative strategies and harmonized wet and computational approaches toward the comprehensive analysis of extracellular vesicles (EVs) from bulk to single vesicle analyses with a special focus on emerging technologies. The workshop covered the key issues in the quantitative analysis of different EV-associated molecular components and EV biophysical features, which are considered the core of EV-associated biomarker discovery and validation for their clinical translation. The in-person-only workshop was held in Trento, Italy, from January 31st to February 2nd, 2023, and continued in Milan on February 3rd with "Next Generation EVs", a satellite event dedicated to early career researchers (ECR). This report summarizes the main topics and outcomes of the workshop.
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Affiliation(s)
- Manuela Basso
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
| | - Alessandro Gori
- National Research Council of ItalyIstituto di Scienze e Tecnologie Chimiche (SCITEC‐CNR)MilanItaly
| | - Caterina Nardella
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
| | - Mari Palviainen
- EV group, Molecular and Integrative Biosciences Research Program, Faculty of Biological and Environmental SciencesUniversity of HelsinkiHelsinkiFinland
| | - Marija Holcar
- Institute of Biochemistry and Molecular Genetics, Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
| | - Ioannis Sotiropoulos
- Institute of Biosciences & ApplicationsNational Center for Scientific Research (NCSR) DemokritosParaskeviGreece
| | - Sylwia Bobis‐Wozowicz
- Faculty of Biochemistry, Biophysics and Biotechnology, Department of Cell BiologyJagiellonian UniversityKrakowPoland
| | - Vito G. D'Agostino
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
| | - Elena Casarotto
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti” (DiSFeB), Dipartimento di EccellenzaUniversità degli Studi di MilanoMilanItaly
| | - Yari Ciani
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
| | - Shiro Suetsugu
- Division of Biological ScienceGraduate School of Science and Technology, Nara Institute of Science and TechnologyIkomaJapan
| | | | | | - Daniela Boselli
- FRACTAL (Flow Cytometry Resource, Advanced Cytometry Technical Applications Laboratory)San Raffaele Scientific InstituteMilanItaly
| | - Anna Kashkanova
- Max Planck Institute for the Science of LightErlangenGermany
| | - Pietro Parisse
- National Research Council of Italy, Istituto Officina dei Materiali (IOM‐CNR)TriesteItaly
| | - Lien Lippens
- Department of Human Structure and Repair, Laboratory of Experimental Cancer ResearchGhent UniversityGhentBelgium
- Cancer Research Institute GhentGhentBelgium
| | - Martina Pagliuca
- Molecular Predictors and New Targets in OncologyGustave RoussyVillejuifFrance
- Clinical and Translational OncologyScuola Superiore MeridionaleNaplesItaly
| | - Martin Blessing
- Max Planck Institute for the Science of LightErlangenGermany
| | - Roberto Frigerio
- National Research Council of ItalyIstituto di Scienze e Tecnologie Chimiche (SCITEC‐CNR)MilanItaly
| | | | - Ana Meliciano
- iBET‐Instituto de Biologia Experimental e TecnológicaOeirasPortugal
| | - Anna Fietta
- Department of Biomedical Sciences (DSB)University of PaduaPaduaItaly
- Fondazione Istituto di Ricerca Pediatrica Città della Speranza (IRP)PaduaItaly
| | - Paolo Vincenzo Fioretti
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
| | | | | | | | - Paolo Bergese
- Department of Molecular and Translational MedicineUniversità degli Studi di BresciaBresciaItaly
- IRIB ‐ Institute for Research and Biomedical Innovation of CNRPalermoItaly
| | | | - Marcella Chiari
- National Research Council of ItalyIstituto di Scienze e Tecnologie Chimiche (SCITEC‐CNR)MilanItaly
| | - Guido Jenster
- Department of Urology, Erasmus MC Cancer InstituteErasmus University Medical CenterRotterdamThe Netherlands
| | - Levi Waldron
- Graduate School of Public Health and Health PolicyCity University of New YorkNew YorkNew YorkUSA
| | - Aleksandar Milosavljevic
- Department of Molecular and Human Genetics, Dan L Duncan Comprehensive Cancer Center, and Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
| | - John Nolan
- Scintillon InstituteSan DiegoCaliforniaUSA
| | | | - Kenneth W. Witwer
- Department of Molecular and Comparative PathobiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Benedetta Bussolati
- Department of Molecular Biotechnology and Health SciencesUniversity of TurinTurinItaly
| | - Dolores Di Vizio
- Department of Surgery, Division of Cancer Biology and TherapeuticsCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Juan Falcon Perez
- Center for Cooperative Research in Biosciences (CIC bioGUNE)Basque Research and Technology Alliance (BRTA), Exosomes LaboratoryDerioSpain
- Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd)MadridSpain
- IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
| | - Metka Lenassi
- Institute of Biochemistry and Molecular Genetics, Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
| | - Marina Cretich
- National Research Council of ItalyIstituto di Scienze e Tecnologie Chimiche (SCITEC‐CNR)MilanItaly
| | - Francesca Demichelis
- Department of Cellular, Computational, and Integrative Biology (CIBIO)University of TrentoTrentoItaly
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7
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Zhou M, He X, Mei C, Ou C. Exosome derived from tumor-associated macrophages: biogenesis, functions, and therapeutic implications in human cancers. Biomark Res 2023; 11:100. [PMID: 37981718 PMCID: PMC10658727 DOI: 10.1186/s40364-023-00538-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/05/2023] [Indexed: 11/21/2023] Open
Abstract
Tumor-associated macrophages (TAMs), one of the most abundant immune cell types in the tumor microenvironment (TME), account for approximately 50% of the local hematopoietic cells. TAMs play an important role in tumorigenesis and tumor development through crosstalk between various immune cells and cytokines in the TME. Exosomes are small extracellular vesicles with a diameter of 50-150 nm, that can transfer biological information (e.g., proteins, nucleic acids, and lipids) from secretory cells to recipient cells through the circulatory system, thereby influencing the progression of various human diseases, including cancer. Recent studies have suggested that TAMs-derived exosomes play crucial roles in malignant cell proliferation, invasion, metastasis, angiogenesis, immune responses, drug resistance, and tumor metabolic reprogramming. TAMs-derived exosomes have the potential to be targeted for tumor therapy. In addition, the abnormal expression of non-coding RNAs and proteins in TAMs-derived exosomes is closely related to the clinicopathological features of patients with cancer, and these exosomes are expected to become new liquid biopsy markers for the early diagnosis, prognosis, and monitoring of tumors. In this review, we explored the role of TAMs-derived exosomes in tumorigenesis to provide new diagnostic biomarkers and therapeutic targets for cancer prevention.
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Affiliation(s)
- Manli Zhou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xiaoyun He
- Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Cheng Mei
- Department of Blood Transfusion, Xiangya Hospital, Clinical Transfusion Research Center, Central South University, Changsha, 410008, Hunan, China.
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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8
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Zhao D, Wu K, Sharma S, Xing F, Wu SY, Tyagi A, Deshpande R, Singh R, Wabitsch M, Mo YY, Watabe K. Exosomal miR-1304-3p promotes breast cancer progression in African Americans by activating cancer-associated adipocytes. Nat Commun 2022; 13:7734. [PMID: 36517516 PMCID: PMC9751138 DOI: 10.1038/s41467-022-35305-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/25/2022] [Indexed: 12/15/2022] Open
Abstract
Breast cancer displays disparities in mortality between African Americans and Caucasian Americans. However, the exact molecular mechanisms remain elusive. Here, we identify miR-1304-3p as the most upregulated microRNA in African American patients. Importantly, its expression significantly correlates with poor progression-free survival in African American patients. Ectopic expression of miR-1304 promotes tumor progression in vivo. Exosomal miR-1304-3p activates cancer-associated adipocytes that release lipids and enhance cancer cell growth. Moreover, we identify the anti-adipogenic gene GATA2 as the target of miR-1304-3p. Notably, a single nucleotide polymorphism (SNP) located in the miR-1304 stem-loop region shows a significant difference in frequencies of the G allele between African and Caucasian American groups, which promotes the maturation of miR-1304-3p. Therefore, our results reveal a mechanism of the disparity in breast cancer progression and suggest a potential utility of miR-1304-3p and the associated SNP as biomarkers for predicting the outcome of African American patients.
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Affiliation(s)
- Dan Zhao
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Kerui Wu
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Sambad Sharma
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Fei Xing
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Shih-Ying Wu
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Abhishek Tyagi
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Ravindra Deshpande
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Ravi Singh
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatric and Adolescent Medicine, Ulm University Medical Center, Ulm, Germany
| | - Yin-Yuan Mo
- Cancer Institute, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Kounosuke Watabe
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC, 27157, USA.
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9
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinform 2022; 23:6696143. [PMID: 36094095 DOI: 10.1093/bib/bbac397] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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10
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Wu D, Tao T, Eshraghian EA, Lin P, Li Z, Zhu X. Extracellular RNA as a kind of communication molecule and emerging cancer biomarker. Front Oncol 2022; 12:960072. [PMID: 36465402 PMCID: PMC9714358 DOI: 10.3389/fonc.2022.960072] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/10/2022] [Indexed: 11/04/2023] Open
Abstract
Extracellular RNA (exRNA) is a special form of RNA in the body. RNA carries information about genes and metabolic regulation in the body, which can reflect the real-time status of cells. This characteristic renders it a biomarker for disease diagnosis, treatment, and prognosis. ExRNA is transported through extracellular vesicles as a signal medium to mediate communication between cells. Tumor cells can release more vesicles than normal cells, thereby promoting tumor development. Depending on its easy detection, the advantages of non-invasive molecular diagnostic technology can be realized. In this systematic review, we present the types, vectors, and biological value of exRNA. We briefly describe new methods of tumor diagnosis and treatment, as well as the difficulties faced in the progress of such research. This review highlights the groundbreaking potential of exRNA as a clinical biomarker.
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Affiliation(s)
- Danny Wu
- Institute of Marine Medicine, Guangdong Medical University, Zhanjiang, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, Zibo, China
| | - Emily A. Eshraghian
- Department of Medicine, University of California (UC) San Diego Health, San Diego, CA, United States
| | - Peixu Lin
- Institute of Marine Medicine, Guangdong Medical University, Zhanjiang, China
| | - Zesong Li
- Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Key Laboratory of Genitourinary Tumor, Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital (Shenzhen Institute of Translational Medicine), Shenzhen, China
| | - Xiao Zhu
- Institute of Marine Medicine, Guangdong Medical University, Zhanjiang, China
- Ningbo Institute of Life and Health Industry, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
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11
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A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:206. [PMID: 36175717 DOI: 10.1007/s12032-022-01815-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/29/2022] [Indexed: 10/14/2022]
Abstract
Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.
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12
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Rhim J, Baek W, Seo Y, Kim JH. From Molecular Mechanisms to Therapeutics: Understanding MicroRNA-21 in Cancer. Cells 2022; 11:cells11182791. [PMID: 36139366 PMCID: PMC9497241 DOI: 10.3390/cells11182791] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that play an important role in regulating gene expression at a posttranscriptional level. As one of the first discovered oncogenic miRNAs, microRNA-21 (miR-21) has been highlighted for its critical role in cancers, such as glioblastoma, pancreatic adenocarcinoma, non-small cell lung cancer, and many others. MiR-21 targets many vital components in a wide range of cancers and acts on various cellular processes ranging from cancer stemness to cell death. Expression of miR-21 is elevated within cancer tissues and circulating miR-21 is readily detectable in biofluids, making it valuable as a cancer biomarker with significant potential for use in diagnosis and prognosis. Advances in RNA-based therapeutics have revealed additional avenues by which miR-21 can be utilized as a promising target in cancer. The purpose of this review is to outline the roles of miR-21 as a key modulator in various cancers and its potential as a therapeutic target.
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Affiliation(s)
- Jiho Rhim
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Korea
- Department of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang 10408, Korea
| | - Woosun Baek
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Korea
- Department of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang 10408, Korea
| | - Yoona Seo
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Korea
- Department of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang 10408, Korea
| | - Jong Heon Kim
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Korea
- Department of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang 10408, Korea
- Correspondence: ; Tel.: +82-31-920-2204
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13
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Identification of Radiation-Induced miRNA Biomarkers Using the CGL1 Cell Model System. Bioengineering (Basel) 2022; 9:bioengineering9050214. [PMID: 35621492 PMCID: PMC9137836 DOI: 10.3390/bioengineering9050214] [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: 05/03/2022] [Revised: 05/11/2022] [Accepted: 05/14/2022] [Indexed: 11/17/2022] Open
Abstract
MicroRNAs (miRNAs) have emerged as a potential class of biomolecules for diagnostic biomarker applications. miRNAs are small non-coding RNA molecules, produced and released by cells in response to various stimuli, that demonstrate remarkable stability in a wide range of biological fluids, in extreme pH fluctuations, and after multiple freeze–thaw cycles. Given these advantages, identification of miRNA-based biomarkers for radiation exposures can contribute to the development of reliable biological dosimetry methods, especially for low-dose radiation (LDR) exposures. In this study, an miRNAome next-generation sequencing (NGS) approach was utilized to identify novel radiation-induced miRNA gene changes within the CGL1 human cell line. Here, irradiations of 10, 100, and 1000 mGy were performed and the samples were collected 1, 6, and 24 h post-irradiation. Corroboration of the miRNAome results with RT-qPCR verification confirmed the identification of numerous radiation-induced miRNA expression changes at all doses assessed. Further evaluation of select radiation-induced miRNAs, including miR-1228-3p and miR-758-5p, as well as their downstream mRNA targets, Ube2d2, Ppp2r2d, and Id2, demonstrated significantly dysregulated reciprocal expression patterns. Further evaluation is needed to determine whether the candidate miRNA biomarkers identified in this study can serve as suitable targets for radiation biodosimetry applications.
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14
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Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:23-73. [DOI: 10.1007/978-3-031-08356-3_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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15
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Cione E, Cannataro R, Gallelli L, De Sarro G, Caroleo MC. Exosome microRNAs in Metabolic Syndrome as Tools for the Early Monitoring of Diabetes and Possible Therapeutic Options. Pharmaceuticals (Basel) 2021; 14:1257. [PMID: 34959658 PMCID: PMC8706321 DOI: 10.3390/ph14121257] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023] Open
Abstract
Exosomes are nano-sized extracellular vesicles produced and released by almost all cell types. They play an essential role in cell-cell communications by delivering cellular bioactive compounds such as functional proteins, metabolites, and nucleic acids, including microRNA, to recipient cells. Thus, they are involved in various physio-pathological conditions. Exosome-miRNAs are associated with numerous diseases, including type 2 diabetes, a complex multifactorial metabolic disorder linked to obesity. In addition, exosome-miRNAs are emerging as essential regulators in the progression of diabetes, principally for pancreatic β-cell injury and insulin resistance. Here, we have clustered the recent findings concerning exosome-miRNAs associated with β-cell dysfunction to provide a novel approach for the early diagnosis and therapy of diabetes.
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Affiliation(s)
- Erika Cione
- Department of Pharmacy, Health and Nutritional Sciences, Department of Excellence 2018-2022, University of Calabria, Ed. Polifunzionale, Arcavacata di Rende, 87036 Rende, CS, Italy;
- GalaScreen Laboratories, University of Calabria, Ed. Polifunzionale, Arcavacata di Rende, 87036 Rende, CS, Italy;
| | - Roberto Cannataro
- GalaScreen Laboratories, University of Calabria, Ed. Polifunzionale, Arcavacata di Rende, 87036 Rende, CS, Italy;
| | - Luca Gallelli
- Department of Health Science, University of Catanzaro and Operative Unit of Clinical Pharmacology and Pharmacovigilance, Mater Domini Hospital, 88100 Catanzaro, CZ, Italy; (L.G.); (G.D.S.)
| | - Giovambattista De Sarro
- Department of Health Science, University of Catanzaro and Operative Unit of Clinical Pharmacology and Pharmacovigilance, Mater Domini Hospital, 88100 Catanzaro, CZ, Italy; (L.G.); (G.D.S.)
| | - Maria Cristina Caroleo
- Department of Pharmacy, Health and Nutritional Sciences, Department of Excellence 2018-2022, University of Calabria, Ed. Polifunzionale, Arcavacata di Rende, 87036 Rende, CS, Italy;
- GalaScreen Laboratories, University of Calabria, Ed. Polifunzionale, Arcavacata di Rende, 87036 Rende, CS, Italy;
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16
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Huang HY, Lin YCD, Cui S, Huang Y, Tang Y, Xu J, Bao J, Li Y, Wen J, Zuo H, Wang W, Li J, Ni J, Ruan Y, Li L, Chen Y, Xie Y, Zhu Z, Cai X, Chen X, Yao L, Chen Y, Luo Y, LuXu S, Luo M, Chiu CM, Ma K, Zhu L, Cheng GJ, Bai C, Chiang YC, Wang L, Wei F, Lee TY, Huang HD. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 2021; 50:D222-D230. [PMID: 34850920 PMCID: PMC8728135 DOI: 10.1093/nar/gkab1079] [Citation(s) in RCA: 526] [Impact Index Per Article: 131.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/09/2021] [Accepted: 10/25/2021] [Indexed: 12/19/2022] Open
Abstract
MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update’s critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3′ untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.
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Affiliation(s)
- Hsi-Yuan Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yang-Chi-Dung Lin
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Shidong Cui
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yixian Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yun Tang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jiatong Xu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jiayang Bao
- Division of Biological Sciences, Section of Bioinformatics, University of California, San Diego, San Diego, CA 92093, USA
| | - Yulin Li
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jia Wen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Huali Zuo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
| | - Weijuan Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jing Li
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jie Ni
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yini Ruan
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Liping Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yidan Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yueyang Xie
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Zihao Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Xiaoxuan Cai
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Xinyi Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China
| | - Yigang Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yijun Luo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Shupeng LuXu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Chih-Min Chiu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Kun Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Lizhe Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Gui-Juan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Chen Bai
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China
| | - Liping Wang
- Department of Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, China
| | - Fengxiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang 154007, China.,Shenzhen Children's Hospital of China Medical University, Shenzhen, Guangdong518172, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Hsien-Da Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
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17
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Li R, Qu H, Wang S, Chater JM, Wang X, Cui Y, Yu L, Zhou R, Jia Q, Traband R, Wang M, Xie W, Yuan D, Zhu J, Zhong WD, Jia Z. CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer. Nucleic Acids Res 2021; 50:D1139-D1146. [PMID: 34500460 PMCID: PMC8728249 DOI: 10.1093/nar/gkab784] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/22/2021] [Accepted: 08/30/2021] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.
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Affiliation(s)
- Ruidong Li
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA
| | - Han Qu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - Shibo Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - John M Chater
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - Xuesong Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA
| | - Yanru Cui
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Lei Yu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA
| | - Rui Zhou
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - Qiong Jia
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA
| | - Ryan Traband
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - Meiyue Wang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Weibo Xie
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Dongbo Yuan
- Department of Urology, Guizhou Provincial People's Hospital, Guizhou, China
| | - Jianguo Zhu
- Department of Urology, Guizhou Provincial People's Hospital, Guizhou, China
| | - Wei-De Zhong
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.,Urology Key Laboratory of Guangdong Province, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.,Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA
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18
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Tastsoglou S, Miliotis M, Kavakiotis I, Alexiou A, Gkotsi EC, Lambropoulou A, Lygnos V, Kotsira V, Maroulis V, Zisis D, Skoufos G, Hatzigeorgiou AG. PlasmiR: A Manual Collection of Circulating microRNAs of Prognostic and Diagnostic Value. Cancers (Basel) 2021; 13:cancers13153680. [PMID: 34359584 PMCID: PMC8345031 DOI: 10.3390/cancers13153680] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/11/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022] Open
Abstract
Only recently, microRNAs (miRNAs) were found to exist in traceable and distinctive amounts in the human circulatory system, bringing forth the intriguing possibility of using them as minimally invasive biomarkers. miRNAs are short non-coding RNAs that act as potent post-transcriptional regulators of gene expression. Extensive studies in cancer and other disease landscapes investigate the protective/pathogenic functions of dysregulated miRNAs, as well as their biomarker potential. A specialized resource amassing experimentally verified, circulating miRNA biomarkers does not exist. We queried the existing literature to identify articles assessing diagnostic/prognostic roles of miRNAs in blood, serum, or plasma samples. Articles were scrutinized in order to exclude instances lacking sufficient experimental documentation or employing no biomarker assessment methods. We incorporated information from more than 200 biomedical articles, annotating crucial meta-information including cohort sizes, inclusion-exclusion criteria, disease/healthy confirmation methods and quantification details. miRNAs and diseases were systematically characterized using reference resources. Our circulating miRNA biomarker collection is provided as an online database, plasmiR. It consists of 1021 entries regarding 251 miRNAs and 112 diseases. More than half of plasmiR's entries refer to cancerous and neoplastic conditions, 183 of them (32%) describing prognostic associations. plasmiR facilitates smart queries, emphasizing visualization and exploratory modes for all researchers.
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Affiliation(s)
- Spyros Tastsoglou
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
- Correspondence: (S.T.); (A.G.H.)
| | - Marios Miliotis
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
| | - Ioannis Kavakiotis
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
| | - Athanasios Alexiou
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
| | - Eleni C. Gkotsi
- Department of Informatics and Telecommunications, Postgraduate Program: ‘Information Technologies in Medicine and Biology’, University of Athens, 15784 Athens, Greece; (E.C.G.); (V.M.)
| | - Anastasia Lambropoulou
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
| | - Vasileios Lygnos
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
| | - Vasiliki Kotsira
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
| | - Vasileios Maroulis
- Department of Informatics and Telecommunications, Postgraduate Program: ‘Information Technologies in Medicine and Biology’, University of Athens, 15784 Athens, Greece; (E.C.G.); (V.M.)
| | - Dimitrios Zisis
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
| | - Giorgos Skoufos
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
- Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece
| | - Artemis G. Hatzigeorgiou
- Hellenic Pasteur Institute, 11521 Athens, Greece; (M.M.); (A.A.); (A.L.); (V.L.); (D.Z.); (G.S.)
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece; (I.K.); (V.K.)
- Correspondence: (S.T.); (A.G.H.)
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19
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He X, Kuang G, Wu Y, Ou C. Emerging roles of exosomal miRNAs in diabetes mellitus. Clin Transl Med 2021; 11:e468. [PMID: 34185424 PMCID: PMC8236118 DOI: 10.1002/ctm2.468] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 06/01/2021] [Accepted: 06/07/2021] [Indexed: 12/14/2022] Open
Abstract
Exosomes are small extracellular vesicles 40-160 nm in diameter that are secreted by almost all cell types. Exosomes can carry diverse cargo including RNA, DNA, lipids, proteins, and metabolites. Exosomes transfer substances and information between cells by circulating in body fluids and are thus involved in diverse physiological and pathological processes in the human body. Recent studies have closely associated exosomal microRNAs (miRNAs) with various human diseases, including diabetes mellitus (DM), which is a complex multifactorial metabolic disorder disease. Exosomal miRNAs are emerging as pivotal regulators in the progression of DM, mainly in terms of pancreatic β-cell injury and insulin resistance. Exosomal miRNAs are closely associated with DM-associated complications, such as diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic cardiomyopathy (DCM), etc. Further investigations of the mechanisms of action of exosomal miRNAs and their role in DM will be valuable for the thorough understanding of the physiopathological process of DM. Here, we have summarized recent findings regarding exosomal miRNAs associated with DM to provide a new strategy for identifying potential diagnostic biomarkers and drug targets for the early diagnosis and treatment, respectively, of DM.
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Affiliation(s)
- Xiaoyun He
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunan410008China
- Departments of Ultrasound Imaging, Xiangya HospitalCentral South UniversityChangshaHunan410008China
| | - Gaoyan Kuang
- Department of OrthopedicsThe First Affiliated Hospital of Hunan University of Chinese MedicineChangshaHunan410007China
- Postdoctoral Research WorkstationHinye Pharmaceutical Co. LtdChangshaHunan410331China
| | - Yongrong Wu
- Hunan university of Chinese MedicineChangshaHunan410208China
| | - Chunlin Ou
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunan410008China
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20
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Guo Q, Lu Y, Huang Y, Guo Y, Zhu S, Zhang Q, Zhu D, Wang Z, Luo J. Exosomes from β-Cells Promote Differentiation of Induced Pluripotent Stem Cells into Insulin-Producing Cells Through microRNA-Dependent Mechanisms. Diabetes Metab Syndr Obes 2021; 14:4767-4782. [PMID: 34934332 PMCID: PMC8678630 DOI: 10.2147/dmso.s342647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Exosomes have emerged as potential tools for the differentiation of induced pluripotent stem cells (iPSCs) into insulin-producing cells (IPCs). Exosomal microRNAs are receiving increasing attention in this process. Here, we aimed at investigating the role of exosomes derived from a murine pancreatic β-cell line and identifying signature exosomal miRNAs on iPSCs differentiation. METHODS Exosomes were isolated from MIN6 cells and identified with TEM, NTA and Western blot. PKH67 tracer and transwell assay were used to confirm exosome delivery into iPSCs. qRT-PCR was applied to detect key pancreatic transcription gene expression and exosome-derived miRNA expression. Insulin secretion was determined using FCM and immunofluorescence. The specific exosomal miRNAs were determined via RNA-interference of Ago2. The therapeutic effect of 21 day-exosome-induced IPCs was validated in T1D mice induced by STZ. RESULTS iPSCs cultured in medium containing exosomes showed sustained higher expression of MAFA, Insulin1, Insulin2, Isl1, Neuroud1, Nkx6.1 and NGN3 compared to control iPSCs. In FCM analysis, approximately 52.7% of the differentiated cells displayed insulin expression at the middle stage. Consistent with the gene expression data, immunofluorescence assays showed that Nkx6.1 and insulin expression in iPSCs were significantly upregulated. Intriguingly, the expression of pancreatic markers and insulin was significantly decreased in iPSCs cultured with siAgo2 exosomes. Transplantation of 21 day-induced IPCs intoT1D mice efficiently enhanced glucose tolerance and partially controlled hyperglycemia. The therapeutic effect was significantly attenuated in T1D mice that received iPSCs cultured with siAgo2 exosomes. Of the seven exosomal microRNAs selected for validation, miR-706, miR-709, miR-466c-5p, and miR-423-5p showed dynamic expression during 21 days in culture. CONCLUSION These data indicate that differentiation of exosome-induced iPSCs into functional cells is crucially dependent on the specific miRNAs encased within exosomes, whose functional analysis is likely to provide insight into novel regulatory mechanisms governing iPSCs differentiation into IPCs.
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Affiliation(s)
- Qingsong Guo
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Yuhua Lu
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Yan Huang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Yibing Guo
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Shajun Zhu
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Qiuqiang Zhang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Donghui Zhu
- Nantong University Medical School, Nantong, 226001, People’s Republic of China
| | - Zhiwei Wang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
- Zhiwei Wang Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Xi Si Road, Nantong, 226001, People’s Republic of China Email
| | - Jia Luo
- Department of Pharmacy, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
- Correspondence: Jia Luo Department of Pharmacy, Affiliated Hospital of Nantong University, Xi Si Road, Nantong, 226001, People’s Republic of China Email
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21
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Terkelsen T, Russo F, Gromov P, Haakensen VD, Brunak S, Gromova I, Krogh A, Papaleo E. Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration. Breast Cancer Res 2020; 22:73. [PMID: 32605588 PMCID: PMC7329449 DOI: 10.1186/s13058-020-01295-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 05/14/2020] [Indexed: 12/21/2022] Open
Abstract
Background Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis. Methods Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases. Results Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454. Conclusion Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.
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Affiliation(s)
- Thilde Terkelsen
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Francesco Russo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pavel Gromov
- Breast Cancer Biology Group, Genome Integrity Unit, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Vilde Drageset Haakensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Irina Gromova
- Breast Cancer Biology Group, Genome Integrity Unit, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Anders Krogh
- Unit of Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark. .,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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22
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Huang HY, Lin YCD, Li J, Huang KY, Shrestha S, Hong HC, Tang Y, Chen YG, Jin CN, Yu Y, Xu JT, Li YM, Cai XX, Zhou ZY, Chen XH, Pei YY, Hu L, Su JJ, Cui SD, Wang F, Xie YY, Ding SY, Luo MF, Chou CH, Chang NW, Chen KW, Cheng YH, Wan XH, Hsu WL, Lee TY, Wei FX, Huang HD. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 2020; 48:D148-D154. [PMID: 31647101 PMCID: PMC7145596 DOI: 10.1093/nar/gkz896] [Citation(s) in RCA: 644] [Impact Index Per Article: 128.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 09/30/2019] [Accepted: 10/22/2019] [Indexed: 12/15/2022] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs (typically consisting of 18–25 nucleotides) that negatively control expression of target genes at the post-transcriptional level. Owing to the biological significance of miRNAs, miRTarBase was developed to provide comprehensive information on experimentally validated miRNA–target interactions (MTIs). To date, the database has accumulated >13,404 validated MTIs from 11,021 articles from manual curations. In this update, a text-mining system was incorporated to enhance the recognition of MTI-related articles by adopting a scoring system. In addition, a variety of biological databases were integrated to provide information on the regulatory network of miRNAs and its expression in blood. Not only targets of miRNAs but also regulators of miRNAs are provided to users for investigating the up- and downstream regulations of miRNAs. Moreover, the number of MTIs with high-throughput experimental evidence increased remarkably (validated by CLIP-seq technology). In conclusion, these improvements promote the miRTarBase as one of the most comprehensively annotated and experimentally validated miRNA–target interaction databases. The updated version of miRTarBase is now available at http://miRTarBase.cuhk.edu.cn/.
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Affiliation(s)
- Hsi-Yuan Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yang-Chi-Dung Lin
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Jing Li
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Kai-Yao Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Sirjana Shrestha
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Hsiao-Chin Hong
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yun Tang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yi-Gang Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Chen-Nan Jin
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yuan Yu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Jia-Tong Xu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yue-Ming Li
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Xiao-Xuan Cai
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Zhen-Yu Zhou
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Xiao-Hang Chen
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China
| | - Yuan-Yuan Pei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China
| | - Liang Hu
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China
| | - Jin-Jiang Su
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang Province 154007, China
| | - Shi-Dong Cui
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Fei Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Yue-Yang Xie
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Si-Yuan Ding
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Meng-Fan Luo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Chih-Hung Chou
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.,Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
| | - Nai-Wen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
| | - Kai-Wen Chen
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Yu-Hsiang Cheng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Xin-Hong Wan
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China
| | - Feng-Xiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang Province 154007, China.,Department of Pathogenic Microorganisms, Zunyi Medical University, Zunyi, Guizhong Province 563006, China
| | - Hsien-Da Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong Province 518172, China.,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan
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van den Berg M, Krauskopf J, Ramaekers J, Kleinjans J, Prickaerts J, Briedé J. Circulating microRNAs as potential biomarkers for psychiatric and neurodegenerative disorders. Prog Neurobiol 2020; 185:101732. [DOI: 10.1016/j.pneurobio.2019.101732] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/25/2019] [Accepted: 12/03/2019] [Indexed: 12/21/2022]
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