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      The relationship of human tissue microRNAs with those from body fluids

      research-article
      1 , 1 , 2 ,
      Scientific Reports
      Nature Publishing Group UK
      Bioinformatics, Computational biology and bioinformatics

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          Abstract

          It is known that many microRNAs (miRNAs) stably exist in various body fluids, however, the relationship of body fluids miRNAs (BF-miRNAs) with those from tissues (T-miRNAs) remains largely unclear but is important for understanding the potential of BF-miRNAs to be biomarkers of specific diseases. Here by analyzing miRNA expression data from 40 human healthy tissues and those from human body fluids, including plasma, serum, urine, bile, and feces, we revealed a positive correlation between BF-miRNAs and T-miRNAs. Moreover, plasma and serum have the most communication with pericardium, adipose, liver, and spleen. Urinary miRNAs show the highest correlation with kidney miRNAs. For fecal miRNAs, gastrointestinal tract (colon, ileum, jejunum, small intestine, stomach, proximal colon, duodenum, and distal colon) miRNAs show the strongest correlation. Moreover, miRNA set enrichment analysis revealed that highly expressed fecal miRNAs are mostly associated with gastric and colon cancers etc. Additionally, bile miRNAs from suspected cholangiocarcinoma patients show a positive correlation with the cholangiocarcinoma tumor tissue. Interestingly, the relationship of BF-miRNAs and T-miRNAs shows significant sex differences. Serum miRNAs showed higher correlation with T-miRNAs in males, whereas plasma miRNAs and urine miRNAs showed higher correlations with T-miRNAs in females. These findings together indicate a potential role of BF-miRNAs as biomarkers to monitor corresponding specific human diseases.

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          Most cited references15

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          Characterization of microRNA expression profiles in normal human tissues

          Background Measuring the quantity of miRNAs in tissues of different physiological and pathological conditions is an important first step to investigate the functions of miRNAs. Matched samples from normal state can provide essential baseline references to analyze the variation of miRNA abundance. Results We provided expression data of 345 miRNAs in 40 normal human tissues, which identified universally expressed miRNAs, and several groups of miRNAs expressed exclusively or preferentially in certain tissue types. Many miRNAs with co-regulated expression patterns are located within the same genomic clusters, and candidate transcriptional factors that control the pattern of their expression may be identified by a comparative genomic strategy. Hierarchical clustering of normal tissues by their miRNA expression profiles basically followed the structure, anatomical locations, and physiological functions of the organs, suggesting that functions of a miRNA could be appreciated by linking to the biologies of the tissues in which it is uniquely expressed. Many predicted target genes of miRNAs that had specific reduced expression in brain and peripheral blood mononuclear cells are required for embryonic development of the nervous and hematopoietic systems based on database search. Conclusion We presented a global view of tissue distribution of miRNAs in relation to their chromosomal locations and genomic structures. We also described evidence from the cis-regulatory elements and the predicted target genes of miRNAs to support their tissue-specific functional roles to regulate the physiologies of the normal tissues in which they are expressed.
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            Toward the blood-borne miRNome of human diseases.

            In a multicenter study, we determined the expression profiles of 863 microRNAs by array analysis of 454 blood samples from human individuals with different cancers or noncancer diseases, and validated this 'miRNome' by quantitative real-time PCR. We detected consistently deregulated profiles for all tested diseases; pathway analysis confirmed disease association of the respective microRNAs. We observed significant correlations (P = 0.004) between the genomic location of disease-associated genetic variants and deregulated microRNAs.
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              Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation

              Poor reproducibility within and across studies arising from lack of knowledge regarding the performance of extracellular RNA (exRNA) isolation methods has hindered progress in the exRNA field. A systematic comparison of ten exRNA isolation methods across five biofluids revealed marked differences in the complexity and reproducibility of the resulting small RNAseq profiles. The relative efficiency with which each method accessed different exRNA carrier subclasses was determined by estimating the proportions of extracellular vesicle- (EV), ribonucleoprotein-(RNP)-, and high-density lipoprotein- (HDL) specific miRNA signatures in each profile. An interactive web-based application (miRDaR) was developed to help investigators select the optimal exRNA isolation method for their studies. miRDar provides comparative statistics for all expressed miRNAs or a selected subset of miRNAs in the desired biofluid for each exRNA isolation method, and returns a ranked list of exRNA isolation methods prioritized by complexity, expression level and reproducibility. These results will improve reproducibility and stimulate further progress in exRNA biomarker development. A systematic comparison of 10 extracellular RNA isolation methods across 5 biofluids will aid researchers in selecting optimal approaches for individual studies with the overall goal of enhancing reliability and reproducibility for a rapidly growing field.
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                Author and article information

                Contributors
                cuiqinghua@bjmu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 March 2020
                27 March 2020
                2020
                : 10
                : 5644
                Affiliations
                [1 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, , Peking University, 38 Xueyuan Rd, ; Beijing, 100191 China
                [2 ]ISNI 0000 0004 0369 4060, GRID grid.54549.39, Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, , University of Electronic Science and Technology of China, ; Chengdu, 610054 China
                Article
                62534
                10.1038/s41598-020-62534-6
                7101318
                32221351
                139bb472-e9da-45b4-8571-43a547a44dac
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 January 2020
                : 14 March 2020
                Funding
                Funded by: the Natural Science Foundation of China
                Award ID: 81670462
                Award Recipient :
                Funded by: Natural Science Foundation of China
                Award ID: 81970440
                Award Recipient :
                Funded by: Peking University Clinical Scientist Program
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                © The Author(s) 2020

                Uncategorized
                bioinformatics,computational biology and bioinformatics
                Uncategorized
                bioinformatics, computational biology and bioinformatics

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