Printer Friendly

Identification of Potential Biomarkers and Biological Pathways in Juvenile Dermatomyositis Based on miRNA-mRNA Network.

1. Introduction

Juvenile dermatomyositis (JDM) is a chronic autoimmune connective tissue disease. Its clinical manifestation is inflammatory myopathy with characteristic skin lesions, like Gottron's papules and heliotrope rash [1-4]. The incidence of JDM is about 1.9-4.1/1,000,000 [5]. The pathogenesis in juvenile is not clear yet, the clinical manifestations of JDM are complex, and its serious cases can even worsen and significantly impact on the prognosis, for example, unspecific clinical course, risk of macrophage activation syndrome, and more in general, chronic systemic inflammation [6, 7]. Therefore, it is urgent to find new diagnostic ideas and new therapies for JDM.

More and more literature indicate that miRNAs play a major role in the occurrence, development, and prognosis of inflammatory myopathy [8]. The latest studies using high-throughput microarray to analyze samples from JDM patients and normal juveniles have given us the opportunity to detect and explore different levels of JDM from genome copy number changes and somatic mutations to transcription levels of gene expression changes and discover the entire molecular landscape of inflammation. Although there have been previous studies involving miRNA-mRNA analysis of JDM, in our knowledge, the regulatory network of miRNAs in JDM has not been constructed.

The purpose of this study is to explore the potential molecular mechanism of JDM and identify effective biomarkers for predicting the pathogenesis of disease and finding target therapy directions in JDM.

2. Materials and Methods

2.1. Microarray Data. Download miRNAs and expression profile data set of GSE49062 [8] and GSE11971 [9] from the GEO database, the downloaded data were analyzed by R 3.5.2 software. The clinical details of GSE49062 and GSE11971 were listed (Tables 1 and 2). The clinical details of GSE49062 and GSE11971 were included (Tables 1 and 2). As to GSE11971, it only has express miRNAs data from females who had JDM, which includes 19 JDM patients and 4 normal. GSE11971 was included in this study because there were no other data on the expression of microRNAs in JDM.

2.2. Screen Differentially Expressed miRNAs and mRNAs. According to the data sets mentioned above, the total data were divided into 2 groups: the JDM group and the normal control group. The R package of limma analysis in R software was used to identify the disorder genes in JDM tissue and normal juvenile skeletal muscle tissue. In order to control the error detection rate, the cutoff standard was set to adj. P val < 0.05 and the R package of ggplot 2 were used to draw volcanic maps with a threshold of P < 0.05 and select the highest 5% of absolute value of logFc for the next step.

2.3. miRNA-mRNA Targeting Relationship Prediction. Differentially expressed miRNAs and mRNAs data were input into microDIP ( Minimum confidence was set as high (Top 5%), and the number of sources was set as 1. In this way, we can predict the regulatory relationship between differentially expressed miRNAs and differentially expressed genes.

2.4. Pathway Enrichment Analysis. The molecular function was studied by the Kyoto Encyclopedia of Genes and Genomes (KEGG), and KEGG pathway analysis was performed on differentially expressed mRNA by Cytoscape's clueGo plug-in.

2.5. Construction of an miRNA-mRNA Regulatory Network. Based on the analysis of differentially expressed miRNAs, differentially expressed mRNAs, and enrichment pathways, Cytoscape was used to visualize the miRNAs-mRNA regulatory network.

3. Results

3.1. Heterogeneity Test and Preliminary Screening of Differentially Expressed Genes

3.1.1. Heterogeneity Test and Preliminary Screening of Differentially Expressed miRNAs. RMA was used to standardize the data of differentially expressed miRNAs and mRNAs. Then, the limma package of the Bioconductor analysis tool was used to make standardized front-box maps and standardized back-box maps (Figures 1 and 2). A total of 224 differentially expressed miRNAs and 4515 differentially expressed mRNAs were screened. Finally, we selected the top 5% differentially expressed microRNAs and mRNAs by the absolute value of logFc, a total of 11 miRNAs, and 224 differentially expressed mRNAs to perform further analysis.

3.2. Functional and Pathway Enrichment Analysis

3.2.1. Differential Expression of mRNAs Function and Pathway Enrichment Analysis. KEGG pathway enrichment analysis of differentially expressed genes was performed with clueGo plug-in of Cytoscape. The P value was set to <0.05 when the kappa score was set at or above 0.3. KEGG pathways with significant enrichment of these pathways included allograft rejection, Epstein-Barr virus infection, herpes simplex virus 1 infection, chemokine signaling pathway, and TNF signaling pathway (Table 3). Significantly, differential genes were most abundant in the Allograft rejection pathway (Figure 3).

3.3. Analysis of miRNA-mRNA Regulatory Network. The predicted regulatory table of miRNA-mRNA was derived from microDIP, which was constructed based on the interaction between miRNA-mRNA pathways (Figure 4). The number of genes around miRNAs regulation or the number of miRNAs around a surrounding gene was defined as degrees, which represented the center of the miRNA-mRNA regulatory network, and the more important of the network was the greater the degree of its base. Obviously, three miRNAs, which include mir-193b (degree = 76), mir-199b-5p (degree = 50), and mir-665 (degree = 43), are at the core of the regulatory network (Table 4).

3.4. Construction of PPI Network. Using STRING database to predict gene interaction, set the minimum required interaction score, high confidence (0.700), downloading the protein interaction relationship and importing Cytoscape software to construct the PPI network (Figure 5) of JDM differentially expressed genes. Many of these differentially expressed genes have high node degrees: MX1 (degree = 28), OAS3 (degree = 27), OAS2 (degree = 27), RSAD2 (degree = 26), IFIT3 (degree = 26), XAF1 (degree = 23), IFI35 (degree = 23), IFIT2 (degree = 23), IFI44L IFIT2 (degree = 23), MX2 (degree = 22), DX58 (degree = 21), and STAT1 (degree = 21); these differentially expressed genes are significantly regulated by differentially expressed miRNAs.

4. Discussion

Currently, the computational analysis predicts that miRNAs regulate about 30% of all human genes, and the miRNA-mRNA regulatory network regulates a variety of biological pathways and processes through complex relationships [9]. JDM is an autoimmune connective tissue disease with immunological abnormalities and positive autoantibodies. It has intricate clinical manifestations, so that it is sometimes difficult to cure that [10, 11]. Therefore, if not detected and not treated early in time, JDM would lead to poor prognosis and seriously reduce the quality of life of patients, or even endanger the lives of patients [12-15].

In our study, we used a multistep method to identify the differentially expressed miRNAs and their target genes in JDM from chip data, perform functional and pathway enrichment analysis, and construct a miRNA-mRNA pathway regulatory network. We found that KEGG pathways enriched by differentially expressed genes in JDM, including Allograft rejection, Epstein-Barr virus infection, herpes simplex virus 1 infection, chemokine signaling pathway, and TNF signaling pathway. Obviously, target genes were significantly enriched in allograft rejection. The limitation of this study is that it is currently limited to the theoretical level. To obtain further accurate verification, it depends on the further experimental verification, which will be the direction of our future efforts.

Compared with the expression in JDM skeletal muscle tissues with the expression of normal skeletal muscle tissues, mir-193b and mir-199b-5p were significantly downregulated in JDM, and mir-665 was significantly overexpressed in JDM. With the development of miRNAs therapies, some of them are now in a stage of clinical and preclinical trials, and among them, "miravirsen" was the most developed therapy [16]. There are emerging data from human autoimmune diseases studying miRNAs as novel biomarkers in diagnosing and predicting autoimmune diseases course and response to therapy (Table 5). According to the recent literature, mir-193b, mir-199b-5p, and mir-665 miRNAs are expressed differently in JDM, and cause of their expression in other autoimmune diseases has not been reported yet. Nevertheless, they were also expressed differently in gastric cancer, prostate cancer, lung cancer, and so on [30-32]. JDM always progresses with various manifestations due to its complex pathogenesis, and as a result, it is frequently misdiagnosed. The existing criteria for the diagnosis of disease are unsuitable for the detection of early events in JDM, even insufficient for prediction of aggravations and remissions of established JDM. Therefore, one of the most significant challenges in JDM is the discovery of new biomarkers for early diagnosis and for prediction of the response to medications. Recent studies have revealed dysregulated miRNAs expression during the natural course of JDM, indicating that changes in the miRNA level may lead to the molecular mechanisms of the disease. In this aspect, special attention must be paid to miRNAs, the levels of which were shown to correlate with JDM or could be used for diagnostics in JDM-negative patients.

To date, the regulatory mechanisms of mir-193b, mir-199b-5p, and mir-665 in JDM have not been uncovered. Through KEGG pathway enrichment analysis, these miRNAs may be involved in the regulation of the following pathways: allograft rejection, Epstein-Barr virus infection, herpes simplex virus 1 infection, chemokine signaling pathway, and TNF signaling pathway. However, the potential function of miR-193b, miR-199b-5p, and miR-665 in JDM still remains unknown, and some advantages and limitations of this research should be acknowledged. Compared with microarray data, bioinformatics analysis technology appears as a promising approach, and it has an advantage in that it can help in the molecular understanding of these aspects, as the genes, proteins' interactions, pathways enrich, and miRNA-mRNA networks. This is of great help in the study of the pathophysiological molecular mechanism of JDM.

In conclusion, the future research direction of JDM can start with 3 miRNAs: miR-193b, miR-199b-5p, and miR 665. For more indepth research, we can perfect the clinical information of JDM and expand the sample size of JDM population through clinical follow-up in the next stage, and also we can excavate more biological information of JDM through in vitro experiments; finally, establishing a reliable animal model of dermatomyositis will be essential to all research areas of dermatomyositis.

Data Availability

The data used to support the findings of this study are included in the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors' Contributions

Cheng-Cheng Qiu conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. Qi-Sheng Su conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. Shang-Yong Zhu conceived and designed the experiments, contributed reagents/materials/ analysis tools, and approved the final draft. Ruo-Chuan Liu contributed reagents/materials/analysis tools and approved the final draft.


The authors would like to express their sincere thanks to all those who have lent them hands in the course of writing this paper.


[1] S. Kim, M. El-Hallak, F. Dedeoglu, D. Zurakowski, R. C. Fuhlbrigge, and R. P. Sundel, "Complete and sustained remission of juvenile dermatomyositis resulting from aggressive treatment," Arthritis & Rheumatism, vol. 60, no. 6, pp. 1825-1830, 2009.

[2] S. Rosina, G. C. Varnier, M. Mazzoni, S. Lanni, C. Malattia, and A. Ravelli, "Innovative research design to meet the challenges of clinical trials for juvenile dermatomyositis," Current Rheumatology Reports, vol. 20, no. 5, p. 29, 2018.

[3] S. Rosina, A. Consolaro, P. van Dijkhuizen et al., "Development and validation of a composite disease activity score for measurement of muscle and skin involvement in juvenile dermatomyositis," Rheumatology, vol. 58, no. 7, pp. 1196-1205, 2019.

[4] L. G. Rider, C. B. Lindsley, and F. W. Miller, Juvenile Dermatomyositis. Textbook of Pediatric Rheumatology, Elsevier, Amsterdam, Netherlands, 7th edition, 2016.

[5] S. Sabbagh, I. Pinal-Fernandez, T. Kishi et al., "Anti-Ro52 autoantibodies are associated with interstitial lung disease and more severe disease in patients with juvenile myositis," Annals of the Rheumatic Diseases, vol. 78, no. 7, pp. 988-995, 2019.

[6] D. Poddighe and K. Dauyey, "Macrophage activation syndrome in juvenile dermatomyositis: a systematic review," Rheumatology International, pp. 1-9, 2019, 1007%2Fs00296-019-04442-1.

[7] A. M. Huber, "Update on the clinical management of juvenile dermatomyositis," Expert Review of Clinical Immunology, vol. 14, no. 12, pp. 1021-1028, 2018.

[8] D. Xu, C.-C. Huang, A. Kachaochana et al., "MicroRNA-10a regulation of proinflammatory mediators: an important component of untreated juvenile dermatomyositis," The Journal of Rheumatology, vol. 43, no. 1, pp. 161-168, 2016.

[9] B. P. Lewis, C. B. Burge, and D. P. Bartel, "Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets," Cell, vol. 120, no. 1, pp. 15-20, 2005.

[10] J. Wienke, C. T. Deakin, L. R. Wedderburn, F. van Wijk, and A. van Royen-Kerkhof, "Systemic and tissue inflammation in juvenile dermatomyositis: from pathogenesis to the quest for monitoring tools," Frontiers in Immunology, vol. 9, p. 2951, 2018.

[11] K. S. Berntsen, E. Edvardsen, B. H. Hansen, B. Flato, I. Sjaastad, and H. Sanner, "Cardiorespiratory fitness in longterm juvenile dermatomyositis: a controlled, cross-sectional study of active/inactive disease," Rheumatology, vol. 58, no. 3, pp. 492-501, 2018.

[12] K. Barut, P. O. A. Aydin, A. Adrovic, S. Sahin, and O. Kasapcopur, "Juvenile dermatomyositis: a tertiary center experience," Clinical Rheumatology, vol. 36, no. 2, pp. 361-366, 2017.

[13] D. Poddighe, L. Cavagna, V. Brazzelli, P. Bruni, and G. L. Marseglia, "A hyper-ferritinemia syndrome evolving in recurrent macrophage activation syndrome, as an onset of amyopathic juvenile dermatomyositis: a challenging clinical case in light of the current diagnostic criteria," Autoimmunity Reviews, vol. 13, no. 11, pp. 1142-1148, 2014.

[14] T. B. Niewold, S. C. Wu, M. Smith, G. A. Morgan, and L. M. Pachman, "Familial aggregation of autoimmune disease in juvenile dermatomyositis," Pediatrics, vol. 127, no. 5, pp. e1239-e1246, 2011.

[15] A. R. Halbert, "Juvenile dermatomyositis," Australasian Journal of Dermatology, vol. 37, no. 2, pp. 106-108, 1996.

[16] R. Kalla, N. T. Ventham, N. A. Kennedy et al., "MicroRNAs: new players in IBD," Gut, vol. 64, no. 3, pp. 504-513, 2015.

[17] R. Gandhi, B. Healy, T. Gholipour et al., "Circulating microRNAs as biomarkers for disease staging in multiple sclerosis," Annals of Neurology, vol. 73, no. 6, pp. 729-740, 2013.

[18] Q. Yang, W. Pan, and L. Qian, "Identification of the miRNA-mRNA regulatory network in multiple sclerosis," Neurological Research, vol. 39, no. 2, pp. 142-151, 2017.

[19] F. Salas-Perez, E. Codner, E. Valencia, C. Pizarro, E. Carrasco, and F. Perez-Bravo, "MicroRNAs miR-21a and miR-93 are down regulated in peripheral blood mononuclear cells (PBMCs) from patients with type 1 diabetes," Immunobiology, vol. 218, no. 5, pp. 733-737, 2013.

[20] L. B. Nielsen, C. Wang, K. S0rensen et al., "Circulating levels of microRNA from children with newly diagnosed type 1 diabetes and healthy controls: evidence that miR-25 associates to residual beta-cell function and glycaemic control during disease progression," Experimental Diabetes Research, vol. 2012, Article ID 896362, 7 pages, 2012.

[21] K. A. Padgett, R. Y. Lan, P. C. Leung et al., "Primary biliary cirrhosis is associated with altered hepatic microRNA expression," Journal of Autoimmunity, vol. 32, no. 3-4, pp. 246-253, 2009.

[22] K. Li, Y. Du, B.-L. Jiang, and J.-F. He, "Increased microRNA155 and decreased microRNA-146a may promote ocular inflammation and proliferation in Graves' ophthalmopathy," Medical Science Monitor, vol. 20, pp. 639-643, 2014.

[23] B. Lv, Z. Liu, S. Wang et al., "miR-29a promotes intestinal epithelial apoptosis in ulcerative colitis by down-regulating Mcl-1," International Journal of Clinical and Experimental Pathology, vol. 7, no. 12, pp. 8542-8552, 2014.

[24] C. Felli, A. Baldassarre, and A. Masotti, "Intestinal and circulating microRNAs in coeliac disease," International Journal of Molecular Sciences, vol. 18, no. 9, p. 1907, 2017.

[25] V. Vaira, L. Roncoroni, D. Barisani et al., "microRNA profiles in coeliac patients distinguish different clinical phenotypes and are modulated by gliadin peptides in primary duodenal fibroblasts," Clinical Science, vol. 126, no. 6, pp. 417-423, 2014.

[26] C. Bernecker, F. Halim, M. Haase, H. Willenberg, M. Ehlers, and M. Schott, "MicroRNA expressions in PMBCs, CD4+, and CD8+ T-cells from patients suffering from autoimmune Addison's disease," Hormone and Metabolic Research, vol. 45, no. 8, pp. 599-604, 2013.

[27] S.-F. Wang-Renault, S. Boudaoud, G. Nocturne et al., "Deregulation of microRNA expression in purified T and B lymphocytes from patients with primary Sjogren's syndrome," Annals of the Rheumatic Diseases, vol. 77, no. 1, pp. 133-140, 2018.

[28] L. Zeng, J.-l. Wu, L.-m. Liu et al., "Serum miRNA-371b-5p and miRNA-5100 act as biomarkers for systemic lupus erythematosus," Clinical Immunology, vol. 196, pp. 103-109, 2018.

[29] A. V. Churov, E. K. Oleinik, and M. Knip, "MicroRNAs in rheumatoid arthritis: altered expression and diagnostic potential," Autoimmunity Reviews, vol. 14, no. 11, pp. 1029-1037, 2015.

[30] J. Chen, D. Sun, H. Chu et al., "Screening of differential microRNA expression in gastric signet ring cell carcinoma and gastric adenocarcinoma and target gene prediction," Oncology Reports, vol. 33, no. 6, pp. 2963-2971, 2015.

[31] X. Jin, Y. Guan, H. Sheng, and Y. Liu, "Crosstalk in competing endogenous RNA network reveals the complex molecular mechanism underlying lung cancer," Oncotarget, vol. 8, no. 53, pp. 91270-91280, 2017.

[32] Z. Song, Y. Huang, Y. Zhao et al., "The identification of potential biomarkers and biological pathways in prostate cancer," Journal of Cancer, vol. 10, no. 6, pp. 1398-1408, 2019.

Cheng-Cheng Qiu, (1) Qi-Sheng Su [ID], (2) Shang-Yong Zhu [ID], (1) and Ruo-Chuan Liu (1)

(1) Department of Diagnostic Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China

(2) Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, China

Correspondence should be addressed to Shang-Yong Zhu;

Received 3 June 2019; Revised 14 September 2019; Accepted 8 November 2019; Published 7 December 2019

Academic Editor: Marija Mostarica-Stojkovic

Caption: Figure 1: Box line diagram and volcano map of differentially expressed miRNAs. (a) Differentially expressed miRNAs data sets were not standardized, (b) after standardization of differentially expressed miRNAs data sets, and (c) volcanic maps for differentially expressing miRNAs. Red dots, significantly upregulated genes. Green dots, significantly downregulated genes. Black dots, nondifferentially expressed genes.

Caption: Figure 2: Box line diagram and volcano map of differentially expressed mRNAs. (a) Differentially expressed mRNAs data sets were not standardized, (b) after standardization of differentially expressed mRNAs data sets, (c) volcanic maps for differentially expressing mRNAs. Red dots, significantly upregulated miRNAs. Green dots, significantly downregulated miRNAs. Black dots, nondifferentially expressed miRNAs.

Caption: Figure 4: miRNA-mRNA regulatory network. The relationship between 9 key miRNAs and 224 target genes. Rectangles, miRNAs. Circles, genes. Red, overexpression. Blue, downregulation. Yellow, enrichment pathways. Straight lines, the regulatory relationship between miRNAs and genes.

Caption: Figure 5: JDM's PPI network diagram. The circle represents the gene, and the darker color represents a greater degree. PPI, protein-protein interaction.
Table 1: Clinical information of GSE49062 included cases.

Group            n       Gender           Age (year)

                      Male   Female

JDM              18    8       10     5.98 [+ or -] 0.68
Normal control   6     3       3      12.13 [+ or -] 1.55

Group             Tissue source

JDM              Skeletal muscle
Normal control

JDM, juvenile dermatomyositis; n, number of cases.

Table 2: Clinical information of GSE11971 included cases.

Group    n    Gender              Age (year)        Tissue source

              Male   Female

JDM      19    0       19     5.31 [+ or -] 0.58   Skeletal muscle
Normal   4     0       4         Unavailable       Skeletal muscle

JDM, juvenile dermatomyositis; n, number of cases.

Table 3: Differential expression of mRNAs enrichment pathway.

Pathway                      Gene     P value corrected
                             number    with Bonferroni

Epstein-Barr virus             14        0.000006091

Hepatitis C                    11        0.000101620

Measles                        8         0.004296090

Influenza A                    14        0.000000839

Type I diabetes mellitus       5         0.003325171

Autoimmune thyroid disease     5         0.006558777

Allograft rejection            5         0.002068987

Graft-versus-host disease      5         0.002818762

Viral myocarditis              5         0.008802414

Pathway                                Gene list

                              CD19, CXCL10, DDX58, HLA-A,
Epstein-Barr virus            HLA-B, HLA-C, HLA-F, HLA-G,
infection                    IRF9, ISG15, OAS1, OAS2, OAS3,

Hepatitis C                   CXCL10, DDX58, IFIT1, IRF9,
                             MX1, OAS1, OAS2, OAS3, RSAD2,
                                      STAT1, TLR3

Measles                      DDX58, IRF9, MX1, OAS1, OAS2,
                                  OAS3, STAT1, TNFSF10

                               CCL2, CCL5, CXCL10, DDX58,
Influenza A                   IRF9, MX1, OAS1, OAS2, OAS3,
                              PYCARD, RSAD2, STAT1, TLR3,

Type I diabetes mellitus      HLA-A, HLA-B, HLA-C, HLA-F,

Autoimmune thyroid disease    HLA-A, HLA-B, HLA-C, HLA-F,

Allograft rejection           HLA-A, HLA-B, HLA-C, HLA-F,

Graft-versus-host disease     HLA-A, HLA-B, HLA-C, HLA-F,

Viral myocarditis             HLA-A, HLA-B, HLA-C, HLA-F,

Table 4: Target miRNAs and their degrees.

miRNAs         DE    degree

miR-193b-3p   DOWN     76
miR-199b-5p   DOWN     50
miR-665        UP      43
miR-378c      DOWN     16
miR-3182       UP      12
miR-3607-3p   DOWN     11
miR-3202       UP      11
miR-95-3p     DOWN     6
miR-642b-3p    UP      4

DE: differential expression.

Table 5: Differentially expressed miRNAs in autoimmune diseases.

                      Differentially expressed miRNAs

Autoimmune            Upregulation          Downregulation

Juvenile          miR-193b, miR-199b-          miR-665
dermatomyositis            5p

Multiple           miR-186, miR-145,      miR-20a, miR-211,
sclerosis          miR-125a-5p, miR-      miR-374a, miR-107,
                   331-3p, miR-146a,       miR-633, miR-93,
                    miR-362-5p, miR-      miR-410, miR-196a,
                  501-5p, miR-491-5p,    hsa-miR-582-5p, miR-
                    miR-142-3p, miR-     579, miR-1251, miR-
                   29b, miR-328, miR-     363, miR-572, miR-
                   92b, miR-30a, let-     519e, miR-32, miR-
                   7b, miR-425, miR-      15b, miR-580, miR-
                   520a-3p, miR-596,       556-5p, miR-510,
                   miR-378, miR-29c,      miR-663, miR-638,
                        miR-20b            miR-369-3p, miR-
                                         208b, miR-200a, miR-

Type 1 diabetes    miR-375, miR-152,      miR-21a and miR-93
                    miR-30a-5p, miR-
                   181a, miR-24, miR-
                  148a, miR-210, miR-
                   27a, miR-29a, miR-
                   26a, miR-27b, miR-
                   25, and miR-200a,

Primary biliary     miR-299-5p, miR-      miR-26a, miR-122a,
cirrhosis             328, miR-371             miR-99a

Graves' disease     miR-17, miR-155,           miR-146a

Ulcerative        miR-16, miR-21, miR-    miR-192, miR-375,
colitis            23a, miR-24, miR-         and miR-422b
                   29a, miR-126, miR-
                    195, and Let-7f

Coeliac disease    miR-182, miR-196a,     miR-105, miR-124a,
                   miR-330, miR-449a,     miR-135a, miR-189,
                   miR-492, miR-500,      miR-202, miR-219,
                   miR-503, miR-504,       miR-299-5p, miR-
                        miR-644           323, miR-379, miR-
                                         380-5p, miR-409-5p,
                                          miR-412, miR-512-
                                          3p, miR-566, miR-
                                          576, miR-600, miR-
                                          614, miR-616, miR-
                                          618, miR-631, miR-
                                         659, miR-31-5p, miR-
                                         192-3p, miR-194-5p,
                                         miR-551a, miR-551b-
                                           5p, miR-638, and

Addison's              miR-181a_1          miR-200a_1, miR-
disease                                         200a2

Sjogren's          miR-146a-5p, miR-       miR-30b-5p, miR-
syndrome            107, miR-222-3p,     582-5p, miR-30b-5p,
                    miR-324-3p, miR-       miR-582-5p, and
                   18a-3p, miR-151a-            others
                   3p, miR-107, miR-
                  222-3p, miR-324-3p,
                    miR-18a-3p, miR-
                   151a-3p and others

Systemic lupus     miR-371b-5p, miR-      miR-148b-3p, miR-
erythematosus       5100, miR-4642,       146a-5p, miR-451a,
                  miR518b, miR-548ap-      miR-223-3p, miR-
                    5p, miR-4762-5p,       1246, miR-21-5p,
                    miR-767-3p, miR-       miR-30e-5p, and
                  4708-3p, and others           others

Rheumatoid        miR-16, miR-146a-b,     miR-34a, miR-34b,
arthritis          miR-155, miR-150,     miR-124a, miR-125a-
                   miR-223, miR-125b,     3p, miR-363, miR-
                   miR-125a-5p, miR-      498, miR-451, and
                  146a, miR-146b, miR-          others
                   203, miR-221, miR-
                    222, and others

Autoimmune        References

Juvenile              --

Multiple           [17, 18]

Type 1 diabetes    [19, 20]

Primary biliary      [21]

Graves' disease      [22]

Ulcerative           [23]

Coeliac disease    [24, 25]

Addison's            [26]

syndrome             [27]

Systemic lupus       [28]

Rheumatoid           [29]

Figure 3: Function and pathway analysis of differentially
expressed mRNAs. (a) Bar chart. (b) Pie chart.
(c) Pathway enrichment map.


TNF signalling pathway                               7 **
Cytokine-cytokine receptor interaction              13 **
Chemokine signaling pathway                         12 **
Toll-like receptor signaling pathway                 7 **
Cytosolic DNA-sensing pathway                        5 *
NOD-like receptor signaling pathway                 11 **
Hepatitis C                                         11 **
Measle                                               8 **
Influenza A                                         14 **
Herpes simplex virus 1 infection                    15 **
Hepatitis C_1                                       11 **
Human cytomegalovirus infection                      9 *
Kaposi sarcoma-associated herpes virus infection     9 **
Herpes simplex virus 1 infection_1                  15 **
Epstein-Barr virus infection                        14 **
Cell adhesion molecules (CAMs)                       7 *
Antigen processing and presentation                  5 *
Type I diabetes mellitus                             5 **
Human cytomegalovirus infection_1                    9 *
Kaposi sarcoma-associated herpes virus infection_1   9 **
Herpes simplex virus 1 infection_2                   15 **
Epstein-Barr virus 1 infection_1                     14 **
Autoimmune thyroid disease                            5 **
Allogra rejection                                     5 **
Graft-versus-host disease                             5 **
Viral myocarditis                                     5 **


% terms per group

TNF signaling pathway               3.85%  **
Chemoking signaling pathway        15.38%  **
Herpes simplex virus 1 infection   19.23% **
Epsein-Barr virus infection        19.23% **
Allograft rejection                42.31% **

Note: Table made from bar graph.
COPYRIGHT 2019 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Qiu, Cheng-Cheng; Su, Qi-Sheng; Zhu, Shang-Yong; Liu, Ruo-Chuan
Publication:BioMed Research International
Date:Dec 31, 2019
Previous Article:The Effect of Iron Deficiency Anemia Early in the Third Trimester on Small for Gestational Age and Birth Weight: A Retrospective Cohort Study on Iron...
Next Article:Comparative Study of Microbiological Monitoring Results from Three Types of Sampling Methods after Gastrointestinal Endoscope Reprocessing.

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |