FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3027674683> ?p ?o ?g. }

• W3027674683 endingPage "189.e4" @default.
• W3027674683 startingPage "185" @default.
• W3027674683 abstract "Psoriasis is a chronic inflammatory skin disorder resulting from the complex pathogenic interactions of the immune system, keratinocytes, genetic susceptibility, and environmental factors. Moreover, psoriasis is regarded as a systemic disease that extends beyond the skin (Boehncke and Schön, 2015Boehncke W.-H. Schön M.P. Psoriasis.Lancet. 2015; 386: 983-994Abstract Full Text Full Text PDF PubMed Scopus (1201) Google Scholar), and identification of blood-based biomarkers is of clinical and research significance to a certain extent. MicroRNAs (miRNAs) are short single-stranded noncoding RNAs (21–24 nucleotides), which mediate posttranscriptional regulation by binding to the 3′ untranslated regions of targeting mRNAs, thus inhibiting their translation, accelerating turnover or degradation (Zibert et al., 2010Zibert J.R. Løvendorf M.B. Litman T. Olsen J. Kaczkowski B. Skov L. MicroRNAs and potential target interactions in psoriasis.J Dermatol Sci. 2010; 58: 177-185Abstract Full Text Full Text PDF PubMed Scopus (157) Google Scholar). Several publications implicate that miRNAs play an important role in the pathogenesis of psoriasis by mediating inflammatory cytokine signaling, immune cell infiltration and differentiation, and keratinocyte hyperproliferation in psoriatic skin (Jiang et al., 2017Jiang M. Sun Z. Dang E. Li B. Fang H. Li J. et al.TGFβ/SMAD/microRNA-486-3p signaling axis mediates keratin 17 expression and keratinocyte hyperproliferation in psoriasis.J Invest Dermatol. 2017; 137: 2177-2186Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar, Srivastava et al., 2017Srivastava A. Nikamo P. Lohcharoenkal W. Li D. Meisgen F. Xu Landén N. et al.MicroRNA-146a suppresses IL-17-mediated skin inflammation and is genetically associated with psoriasis.J Allergy Clin Immunol. 2017; 139: 550-561Abstract Full Text Full Text PDF PubMed Scopus (81) Google Scholar, Wu et al., 2018Wu R. Zeng J. Yuan J. Deng X. Huang Y. Chen L. et al.MicroRNA-210 overexpression promotes psoriasis-like inflammation by inducing Th1 and Th17 cell differentiation.J Clin Invest. 2018; 128: 2551-2568Crossref PubMed Scopus (97) Google Scholar, Yan et al., 2015Yan S. Xu Z. Lou F. Zhang L. Ke F. Bai J. et al.NF-κB-induced microRNA-31 promotes epidermal hyperplasia by repressing protein phosphatase 6 in psoriasis.Nat Commun. 2015; 6: 7652Crossref PubMed Scopus (138) Google Scholar). Serum levels of miR-369-3p and miR-1266 in patients with psoriasis (Pso) were considerably higher than those in healthy controls (HCs), and miR-1266 levels showed a positive correlation with PASI score (Guo et al., 2013Guo S. Zhang W. Wei C. Wang L. Zhu G. Shi Q. et al.Serum and skin levels of miR-369-3p in patients with psoriasis and their correlation with disease severity.Eur J Dermatol. 2013; 23: 608-613Crossref PubMed Scopus (26) Google Scholar, Seifeldin et al., 2016Seifeldin N.S. El Sayed S.B. Asaad M.K. Increased MicroRNA-1266 levels as a biomarker for disease activity in psoriasis vulgaris.Int J Dermatol. 2016; 55: 1242-1247Crossref PubMed Scopus (9) Google Scholar). Another study found that miR-125b, miR-146a, miR-203, and miR-205 were significantly decreased in the sera from Pso compared with the sera from normal subjects (Koga et al., 2014Koga Y. Jinnin M. Ichihara A. Fujisawa A. Moriya C. Sakai K. et al.Analysis of expression pattern of serum microRNA levels in patients with psoriasis.J Dermatol Sci. 2014; 74: 170-171Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar). Previous evidence reveals that miRNA exchange between cells can be accomplished through extracellular vesicles (EVs) (Valadi et al., 2007Valadi H. Ekström K. Bossios A. Sjöstrand M. Lee J.J. Lötvall J.O. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells.Nat Cell Biol. 2007; 9: 654-659Crossref PubMed Scopus (8216) Google Scholar). EVs are membrane-contained vesicles released by different cell types with the capacity to transfer intercellular information both locally and systemically. Cell-to-cell communication of EVs is realized by the complex cargo, including miRNAs, mRNAs, DNA, proteins, lipids (Robbins and Morelli, 2014Robbins P.D. Morelli A.E. Regulation of immune responses by extracellular vesicles.Nat Rev Immunol. 2014; 14: 195-208Crossref PubMed Scopus (1257) Google Scholar, Witwer et al., 2013Witwer K.W. Buzás E.I. Bemis L.T. Bora A. Lässer C. Lötvall J. et al.Standardization of sample collection, isolation and analysis methods in extracellular vesicle research.J Extracell Vesicles. 2013; 2 (https://doi.org/10.3402/jev.v2i0.20360)Crossref PubMed Scopus (1428) Google Scholar), and the unique package. Therefore, certain miRNA biomarkers of EVs are not only protected from degradation in the bloodstream but may reflect the characteristics of their parent cells (Cheng et al., 2014Cheng L. Sharples R.A. Scicluna B.J. Hill A.F. Exosomes provide a protective and enriched source of miRNA for biomarker profiling compared to intracellular and cell-free blood.J Extracell Vesicles. 2014; 3 (https://doi.org/10.3402/jev.v3.23743)Crossref PubMed Scopus (492) Google Scholar, Valentino et al., 2017Valentino A. Reclusa P. Sirera R. Giallombardo M. Camps C. Pauwels P. et al.Exosomal microRNAs in liquid biopsies: future biomarkers for prostate cancer.Clin Transl Oncol. 2017; 19: 651-657Crossref PubMed Scopus (53) Google Scholar). Thus, these features make EV-enclosed miRNA analysis superior to whole serum for exploring biomarkers in psoriasis. To determine miRNA profiling of serum EVs in psoriasis, we examined paired sera from eight Pso and eight HCs (Supplementary Table S1) in the discovery set by small RNA sequencing. All donors provided written informed consent for this study. Research protocols were approved by the Ethics Committee of Second Affiliated Hospital of Zhejiang University School of Medicine. The small RNA sequencing demonstrated 1,075 and 1,066 known miRNAs in serum EVs from Pso and HCs, respectively (NCBI SRA, accession number: SRP250251). Among them, 913 miRNAs were simultaneously identified in both groups (Figure 1a). Moreover, we found 72 novel miRNAs expressed in at least one of the groups (Supplementary Table S2). According to the expression level in Pso, the top 10 most highly expressed miRNAs were miR-451a, let-7i-5p, miR-126-3p, miR-148a-3p, miR-26a-5p, miR-21-5p, miR-151a-3p, let-7g-5p, let-7f-5p, and let-7a-5p (Supplementary Table S3). Differential expression analysis of mature miRNA was conducted by DESeq (fold change > 1 and adjusted P < 0.05). We identified 50 miRNAs with a significant change in expression level between Pso and HC, out of which 26 were upregulated and 24 were downregulated (Figure 1b–d, Supplementary Table S4). Figure 1c shows the top 10 highest log2-fold-change miRNAs differentially expressed in serum EVs of Pso versus HC, in which miR-11400 upregulated and miR-501-3p downregulated most significantly. The heatmap displays the patterns of miRNA expression between the experimental groups (Figure 1d). To further confirm the miRNA signature found in the discovery set, we compared miRNA in serum EVs from Pso (n = 30), HC (n = 18), and patients with pityriasis rosea (PR, n = 21; Supplementary Table S5), a common exanthematous erythematous skin disease manifested with typically herald patch followed by smaller scaly spots, by qRT-PCR. The expression level of miR-199a-3p was significantly upregulated in Pso than that in HC and PR (Figure 2a), and it had a relatively high receiver operating characteristic curve of 0.8647 (Figure 2b). In addition, the increase in the expression level of miR-199a-3p was positively correlated with PASI score and body surface area in Pso (Figure 2c and d). Furthermore, we tested 14 unpaired Pso who reached PASI90 after systemic treatment, and their miR-199a-3p decreased significantly (Figure 2e–g). However, there was no significant difference in miR-199a-3p in PBMCs (Supplementary Figure S1). Thus, miR-199a-3p may not only provide a clear distinction between psoriasis, pityriasis rosea, and HCs but also reflect the severity of psoriasis and therapeutic effect.Figure 2Validation of candidate miRNAs in serum EVs and their target analysis. (a, h–n) qRT-PCR expression analysis of indicated miRNAs in serum EVs from Pso (n = 30), PR (n = 21), and HC (n = 18). Significance was determined by Kruskal-Wallis test. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001. (b) The ROC curve of miR-199a-3p. (c, d) The expression level of miR-199a-3p in Pso was positively correlated with PASI and BSA. P-value was calculated by the R2 statistic. (e–g) The quantity of miR-199a-3p was significantly reduced in 14 patients with unpaired Pso with systemic treatment whose BSA and PASI were lower than the above patients without systemic treatment. Significance was determined by Mann-Whitney test. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001. (o) ROC curves for comparison of the five differentially expressed miRNAs in PR and Pso. (p) The 114 target mRNAs of miR-199a-3p were predicted by miRNet, of which 17 targets marked with orange dots were significantly enriched in the focal adhesion pathway and mTOR pathway by KEGG analysis. (q) The miRNA-mRNA regulatory network in PR constructed by the miRNet platform. BSA, body surface area; EV, extracellular vesicle; HC, healthy control; KEGG, Kyoto Encyclopedia of Genes and Genomes; miRNA, microRNA; PR, patient with pityriasis rosea; Pso, patient with psoriasis; ROC, receiver operating characteristic.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Pityriasis rosea is sometimes difficult to distinguish from psoriasis. Of note, the levels of miR-500a-3p, miR-484, miR-185-5p, miR-27a-5p, and let-7f-5p in serum EVs from Pso and HC were significantly lower than those from PR (Figure 2h–m), whereas miR-1255b-5p level was less in PR (Figure 2n). In addition, the differentially expressed miRNAs in PR provided fairly good area under the curve values to discriminate them from Pso (for miR-500a-3p, 0.9557; for miR-484, 0.8762; for miR-185-5p, 0.7915; for miR-27a-5p, 0.7964; for miR-1255b-5p, 0.8183; Figure 2o). The abovementioned results indicated that these miRNAs in serum EVs had specific expression levels in pityriasis rosea, suggesting that they may serve as diagnostic biomarkers for psoriasis and pityriasis rosea. In addition to the above miRNAs differentially expressed in Pso and PR, there was no statistical difference in some other miRNAs, including miR-21-5p, let-7a-5p, let-7e-5p, miR-148a-3p, and miR-374b-5p (Supplementary Figure S2a–e). Furthermore, the levels of the remaining miRNAs were too low to allow analysis, such as miR-11400 and miR-501-3p. Next, we searched bioinformatics miRNA target databases to explore the potential functions of these miRNAs. Using miRNet, 114 mRNAs were found to be predicted targets of miR-199a-3p (Figure 2p). Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that 17 targets marked with green dots were significantly enriched in the focal adhesion pathway and mammalian target of rapamycin pathway (Figure 2q). Moreover, we found 16 mRNAs as shared putative targets for the six miRNAs significantly changed in PR. Then, we mapped the miRNA-mRNA regulatory network (Figure 2q). By comparison, the differential expression of miR-199a-3p between Pso and HC found in the validation set was consistent with small RNA sequencing, but other miRNAs had no significant change that was different from the sequencing results. The relatively small sample size and the variation of miRNA profiling among samples might be the cause of these inconsistencies. A recent publication has demonstrated that circulating EV miRNAs, let-7b-5p and miR-30e-5p, were reduced in patients with psoriatic arthritis compared with patients with psoriasis vulgaris (Pasquali et al., 2020Pasquali L. Svedbom A. Srivastava A. Rosén E. Lindqvist U. Ståhle M. et al.Circulating microRNAs in extracellular vesicles as potential biomarkers for psoriatic arthritis in patients with psoriasis.J Eur Acad Dermatol Venereol. 2020; 34: 1248-1256Crossref PubMed Scopus (18) Google Scholar). The study performed small RNA sequencing on the above two types of patients. The most abundant miRNAs included miR-451a and several members of the let-7 family, partially consistent with our results. However, HCs were not included in the study, making it difficult to compare the differentially expressed miRNAs with those of our study. In conclusion, this study provides evidence that serum EVs contain specific miRNAs with potential diagnostic value for psoriasis and pityriasis rosea. The results of this study underscore the involvement of circulating EVs in psoriasis and pityriasis rosea and may contribute to a better definition of their pathogenesis through deep investigation. Small RNA sequencing data are openly available at the National Center for Biotechnology Information Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/), accession number: SRP250251. Zhao-Yuan Wang: https://orcid.org/0000-0003-0653-1198 Bing-Xi Yan: https://orcid.org/0000-0003-3018-1757 Yuan Zhou: https://orcid.org/0000-0002-2065-1922 Xue-Yan Chen: https://orcid.org/0000-0002-3073-7180 Jing Zhang: https://orcid.org/0000-0003-4350-7998 Sui-Qing Cai: https://orcid.org/0000-0002-7543-9013 Min Zheng: https://orcid.org/0000-0003-4565-844X Xiao-Yong Man: https://orcid.org/0000-0003-3331-5538 The authors state no conflict of interest. This work was supported by grants from the National Natural Science Foundation of China (No. 81672089 , 81630082 , 81930089 ). We thank all the patients and healthy donors for their enthusiastic participation in the study. Conceptualization: ZYW, XYM, SQC, MZ; Data Curation: ZYW, YZ; Formal Analysis: ZYW, XYM; Funding Acquisition: SQC, MZ, XYM; Investigation: ZYW, YZ, BXY, XYC, JZ; Methodology: ZYW, XYM, SQC; Project Administration: ZYW, XYM, MZ; Resources: XYM, MZ, SQC; Supervision: XYM, MZ, SQC; Validation: BXY, YZ, XYC; Visualization: ZYW, XYM; Writing - Original Draft Preparation: ZYW, YZ; Writing -Review and Editing: ZYW, XYM, YZ, BXY, XYC, JZ, MZ, SQC For the whole study, we prepared two independent sample sets to discover and validate potential microRNA (miRNA) biomarkers for psoriasis in serum extracellular vesicles (EVs). In both sets, sera from patients with psoriasis (Pso, n = 52), patients with pityriasis rosea (PR, n = 23), and healthy controls (HCs, n = 26) were collected at Dermatology Department, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. PASI and body surface area were determined at the time of sample collection. Research protocols were approved by the Ethics Committee of Second Affiliated Hospital of Zhejiang University School of Medicine. Whole blood (5–10 ml) from subjects was centrifuged at 3,000 r.p.m. for 10 minutes to separate serum. The serum samples were stored at −80 °C in aliquots of 1 ml until analysis. In the discovery set, paired sera from Pso (n = 8) and HC (n = 8) were chosen for small RNA sequence. Paired sera from Pso (n = 30), PR (n = 21), and HC (n = 18) were designated as the validation set. The above patients with Pso were untreated with systemic drugs for at least 1 month. In addition, sera from Pso (n = 14) who reached PASI 90 after systemic treatment were tested. EV RNA was extracted from serum with exoRNeasy Serum-Plasma Midi Kit and exoRNeasy Serum-Plasma Maxi Kit (Qiagen, Hilden, Germany), which were designed for the direct purification of total vesicular RNA without the intermediate isolation of EVs, according to the manufacturer’s instructions. Briefly, 1 volume of serum (1 ml with the midi format and 4 ml with the maxi format) was mixed with 1 volume of buffer XBP and added onto the exoEasy spin column to bind the EVs on the basis of the principle of membrane affinity. After centrifugation (500g for 1 minute at 20 °C), the flow-through was discarded. Buffer XWP was added to the column to remove residual material by another centrifugation (5,000g for 5 minutes at 20 °C) and discarding of the flow-through. Then, we added 700 μl QIAzol Lysis Reagent (Qiagen) to the membrane and collected the lysate through centrifugation. After the addition of chloroform and centrifugation, the lysate was separated into aqueous and organic phases. The upper aqueous phase was recovered and mixed with ethanol. The mixture was applied to the RNeasy MinElute spin column and centrifuged, followed by buffer RWT and buffer RPE washing. Finally, 14 μl RNase-free water was used to elute the RNA. RNA samples were frozen at −80 °C before further use. RNA amount and integrity were analyzed by Qubit 2.0 Fluorometer (Life Technologies, Foster City, CA) and Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA). Sequencing libraries were generated by NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs, Ipswich, MA) following the manufacturer’s recommendations. Briefly, NEB 3′ SR Adaptor was specifically ligated to the 3′ end of small RNA. After the 3′ ligation reaction, the SR RT Primer was applied to hybridize to the excess of 3′ SR Adaptor and transform the single-stranded DNA adaptor into a double-stranded DNA molecule, which was important to prevent adaptor-dimer formation. Then, we added 5′SR Adaptor to ligate to 5′ end of small RNA, followed by the reverse transcription reaction using M-MuLV Reverse Transcriptase (RNase H−). PCR amplification was performed using LongAmp Taq 2X Master Mix, SR primer for Illumina, and index (X) primer. Subsequently, PCR products were separated on an 8% polyacrylamide gel to purify DNA fragments corresponding to 140‒160 base pairs. Finally, libraries were dissolved in 8 μl elution buffer and assessed on the Agilent Bioanalyzer 2100 system (Agilent Technologies). Libraries were sequenced on the HiSeq 2500 platform (Illumina, San Diego, CA). After sequencing, raw reads in fastq format were filtered for possible technical artifacts and biological contaminants. At the same time, Q30 values and GC-content of the raw data were calculated. Then, the filtered reads were mapped to the reference sequence by Bowtie2 (Langmead and Salzberg, 2012Langmead B. Salzberg S.L. Fast gapped-read alignment with Bowtie 2.Nat Methods. 2012; 9: 357-359Crossref PubMed Scopus (22752) Google Scholar). Alignments of known miRNA were retrieved from miRBase 20.0. The available software miREvo (Wen et al., 2012Wen M. Shen Y. Shi S. Tang T. miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments.BMC Bioinformatics. 2012; 13: 140Crossref PubMed Scopus (285) Google Scholar) and mirdeep2 (Friedländer et al., 2012Friedländer M.R. Mackowiak S.D. Li N. Chen W. Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades.Nucleic Acids Res. 2012; 40: 37-52Crossref PubMed Scopus (1597) Google Scholar) were integrated to predict novel miRNA by exploring the secondary structure, the Dicer cleavage site, and the minimum-free energy of the small RNA tags unannotated in the former steps. MiRNA expression level was estimated by transcript per million. Differential expression analysis was conducted with the DESeq R package (1.8.3). The P-values were adjusted using the Benjamini-Hochberg method, with a false discovery rate of 0.05. The above small RNA sequence and data analysis were performed by Novogene Bioinformatics Technology (Beijing, China). Human PBMCs were prepared from the blood of Pso (n = 12) and HC (n = 10). A total of 4 ml of whole blood was diluted with 4 ml PBS, layered onto 4 ml of Ficoll (Sigma-Aldrich, St. Louis, MI), and centrifuged at 800g for 20 minutes at room temperature. The PBMC interface was carefully recovered and washed with PBS by centrifugation (250g for 5 minutes at 4 °C). Total RNA was extracted from PBMCs using the miRNeasy Mini Kit (Qiagen), following the manufacturer’s instructions. The reverse transcription reaction was performed using the miScript II RT kit (Qiagen) according to the manufacturer’s instructions. Briefly, 12 μl isolated RNA was added to the cDNA master mix, composed of miScript Reverse Transcriptase Mix, 10× miScript Nucleics Mix, and 5× miScript HiSpec Buffer (mature miRNA detection only), to a total volume of 20 μl. The reaction mixture was incubated at 37 °C for 60 minutes, followed by 5-minute incubation at 95 °C. The cDNA was diluted 10 times to serve as a template for further analysis. The expression levels of candidate miRNAs were validated by qRT-PCR using FastStart Universal SYBR Green PCR Master (Roche Holdings AG, Basel, Switzerland) and miRNA-specific primers in a QuantStudio 5 Real-Time PCR system (Applied Biosystems, Waltham, MA), following the amplification procedures recommended by the manufacturer (15 minutes at 95 °C, followed by 40 cycles of 15 seconds at 94 °C, 30 seconds at 55 °C, and 35 seconds at 70 °C). The primers were designed by miRprimer2.0 (Busk, 2014Busk P.K. A tool for design of primers for microRNA-specific quantitative RT-qPCR.BMC Bioinformatics. 2014; 15: 29Crossref PubMed Scopus (140) Google Scholar). For miRNAs in EVs, geNorm software was used to select the optimal set of reference genes for normalization (Vandesompele et al., 2002Vandesompele J. De Preter K. Pattyn F. Poppe B. Van Roy N. De Paepe A. et al.Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.Genome Biol. 2002; 3 (RESEARCH0034)Crossref PubMed Google Scholar). After geNorm analysis, the geometric mean of miR-126-3p, miR-26a-5p, and let-7i-5p was considered as the optimal normalization factor for our experimental conditions. For miRNAs in PBMCs, RNU6 was used as the reference gene. The relative expression level of miRNAs was calculated by the 2-△△Ct method. Bioinformatics miRNA target prediction, regulatory network visualization, and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using miRNet platform, which integrates data from 11 different miRNA databases (http://https://www.mirnet.ca/miRNet/home.xhtml/) (Fan et al., 2016Fan Y. Siklenka K. Arora S.K. Ribeiro P. Kimmins S. Xia J. miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis.Nucleic Acids Res. 2016; 44: W135-W141Crossref PubMed Scopus (255) Google Scholar). The comparisons of means were analyzed by Mann-Whitney test and one-way ANOVA, the latter including Kruskal-Wallis test and Dunn Multiple Comparison test. The associations between miR-199a-3p expression level and PASI score or body surface area were confirmed by linear regression. P-value was calculated by R2 statistic. The area under the receiver operating characteristic curve (area under the curve) was calculated. All these statistical analyses were performed using GraphPad Prism 8 (GraphPad Software, San Diego, CA). P < 0.05 was considered statistically significant.Supplementary Figure S2Expression analysis of candidate miRNAs in serum EVs by qRT-PCR. (a–e) qRT-PCR expression analysis of miR-21-5p, let-7a-5p, let-7e-5p, miR-148a-3p, and miR-374b-5p in serum EVs from Pso (n = 30), PR (n = 21), and HC (n = 18). Significance was determined by Kruskal-Wallis test. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001. EV, extracellular vesicle; HC, healthy control; miRNA, microRNA; PR, patient with pityriasis rosea; Pso, patient with psoriasis.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Table S1The Characteristics of Pso and HC Studied in the Discovery SetGenderPsoAge, yBSA, %PASIHCsGenderAge, yMale209050Female25Male469057.5Female67Male537030Male45Male308552Male55Male44812.1Male53Male403528.7Male47Male519055.6Male46Female352521.3Male33Abbreviations: BSA, body surface area; HC, healthy control; Pso, patient with psoriasis. Open table in a new tab Supplementary Table S3The Top 10 Most Highly Expressed miRNAs in Serum EVs from Pso and HC on the Basis of the Expression Levels of PsomiRNA IDHCPsoFold changeAdjusted P-valuehsa-miR-451a600465.0175275.00.290.15924hsa-let-7i-5p106598.085439.70.800.32862hsa-miR-126-3p113580.975338.80.660.92168hsa-miR-148a-3p25254.8.070784.72.800.00106hsa-miR-26a-5p70160.046630.00.660.95614hsa-miR-21-5p23210.246214.61.990.00402hsa-miR-151a-3p26910.027577.21.020.24694hsa-let-7g-5p11649.218737.51.610.04939hsa-let-7f-5p5971.214334.22.400.00754hsa-let-7a-5p6900.914169.52.050.02787Abbreviations: EV, extracellular vesicle; HC, healthy control; miRNA, microRNA; Pso, patient with psoriasis. Open table in a new tab Abbreviations: BSA, body surface area; HC, healthy control; Pso, patient with psoriasis. Abbreviations: EV, extracellular vesicle; HC, healthy control; miRNA, microRNA; Pso, patient with psoriasis. Download .xlsx (.03 MB) Help with xlsx files Supplementary Table S2 Download .xlsx (.05 MB) Help with xlsx files Supplementary Table S4 Download .xlsx (.01 MB) Help with xlsx files Supplementary Table S5" @default.
• W3027674683 created "2020-05-29" @default.
• W3027674683 creator A5011747309 @default.
• W3027674683 creator A5015330477 @default.
• W3027674683 creator A5015863683 @default.
• W3027674683 creator A5017947172 @default.
• W3027674683 creator A5023501210 @default.
• W3027674683 creator A5031348125 @default.
• W3027674683 creator A5081325802 @default.
• W3027674683 creator A5082713628 @default.
• W3027674683 date "2021-01-01" @default.
• W3027674683 modified "2023-10-16" @default.
• W3027674683 title "miRNA Profiling of Extracellular Vesicles Reveals Biomarkers for Psoriasis" @default.
• W3027674683 cites W1577577364 @default.
• W3027674683 cites W1856181312 @default.
• W3027674683 cites W2027541642 @default.
• W3027674683 cites W2055313665 @default.
• W3027674683 cites W2091279041 @default.
• W3027674683 cites W2096884048 @default.
• W3027674683 cites W2103657395 @default.
• W3027674683 cites W2121725913 @default.
• W3027674683 cites W2130305733 @default.
• W3027674683 cites W2170551349 @default.
• W3027674683 cites W2263619077 @default.
• W3027674683 cites W2341117915 @default.
• W3027674683 cites W2461743622 @default.
• W3027674683 cites W2514306162 @default.
• W3027674683 cites W2570544313 @default.
• W3027674683 cites W2642432975 @default.
• W3027674683 cites W2799871629 @default.
• W3027674683 cites W3000169647 @default.
• W3027674683 cites W4211077900 @default.
• W3027674683 doi "https://doi.org/10.1016/j.jid.2020.04.021" @default.
• W3027674683 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32445741" @default.
• W3027674683 hasPublicationYear "2021" @default.
• W3027674683 type Work @default.
• W3027674683 sameAs 3027674683 @default.
• W3027674683 citedByCount "12" @default.
• W3027674683 countsByYear W30276746832020 @default.
• W3027674683 countsByYear W30276746832021 @default.
• W3027674683 countsByYear W30276746832022 @default.
• W3027674683 countsByYear W30276746832023 @default.
• W3027674683 crossrefType "journal-article" @default.
• W3027674683 hasAuthorship W3027674683A5011747309 @default.
• W3027674683 hasAuthorship W3027674683A5015330477 @default.
• W3027674683 hasAuthorship W3027674683A5015863683 @default.
• W3027674683 hasAuthorship W3027674683A5017947172 @default.
• W3027674683 hasAuthorship W3027674683A5023501210 @default.
• W3027674683 hasAuthorship W3027674683A5031348125 @default.
• W3027674683 hasAuthorship W3027674683A5081325802 @default.
• W3027674683 hasAuthorship W3027674683A5082713628 @default.
• W3027674683 hasBestOaLocation W30276746831 @default.
• W3027674683 hasConcept C104317684 @default.
• W3027674683 hasConcept C111919701 @default.
• W3027674683 hasConcept C145059251 @default.
• W3027674683 hasConcept C185592680 @default.
• W3027674683 hasConcept C187191949 @default.
• W3027674683 hasConcept C203014093 @default.
• W3027674683 hasConcept C20518536 @default.
• W3027674683 hasConcept C2780564577 @default.
• W3027674683 hasConcept C2908689518 @default.
• W3027674683 hasConcept C2992929900 @default.
• W3027674683 hasConcept C41008148 @default.
• W3027674683 hasConcept C55493867 @default.
• W3027674683 hasConcept C70721500 @default.
• W3027674683 hasConcept C71924100 @default.
• W3027674683 hasConcept C86803240 @default.
• W3027674683 hasConcept C95444343 @default.
• W3027674683 hasConceptScore W3027674683C104317684 @default.
• W3027674683 hasConceptScore W3027674683C111919701 @default.
• W3027674683 hasConceptScore W3027674683C145059251 @default.
• W3027674683 hasConceptScore W3027674683C185592680 @default.
• W3027674683 hasConceptScore W3027674683C187191949 @default.
• W3027674683 hasConceptScore W3027674683C203014093 @default.
• W3027674683 hasConceptScore W3027674683C20518536 @default.
• W3027674683 hasConceptScore W3027674683C2780564577 @default.
• W3027674683 hasConceptScore W3027674683C2908689518 @default.
• W3027674683 hasConceptScore W3027674683C2992929900 @default.
• W3027674683 hasConceptScore W3027674683C41008148 @default.
• W3027674683 hasConceptScore W3027674683C55493867 @default.
• W3027674683 hasConceptScore W3027674683C70721500 @default.
• W3027674683 hasConceptScore W3027674683C71924100 @default.
• W3027674683 hasConceptScore W3027674683C86803240 @default.
• W3027674683 hasConceptScore W3027674683C95444343 @default.
• W3027674683 hasFunder F4320321001 @default.
• W3027674683 hasIssue "1" @default.
• W3027674683 hasLocation W30276746831 @default.
• W3027674683 hasOpenAccess W3027674683 @default.
• W3027674683 hasPrimaryLocation W30276746831 @default.
• W3027674683 hasRelatedWork W1990602103 @default.
• W3027674683 hasRelatedWork W2059234380 @default.
• W3027674683 hasRelatedWork W2068396393 @default.
• W3027674683 hasRelatedWork W2161617639 @default.
• W3027674683 hasRelatedWork W2464399839 @default.
• W3027674683 hasRelatedWork W2558389982 @default.
• W3027674683 hasRelatedWork W3047759831 @default.
• W3027674683 hasRelatedWork W3112569589 @default.
• W3027674683 hasRelatedWork W3211320499 @default.

• next