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Biomed Res Int ; 2019: 7814287, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31886250


Objective: The aim of this study is to explore the potential pathogenesis of juvenile dermatomyositis by bioinformatics analysis of gene chips, which would screen the hub genes, identify potential biomarkers, and reveal the development mechanism of juvenile dermatomyositis. Material and Methods: We retrieved juvenile dermatomyositis's original expression microarray data of message RNAs (mRNAs) and microRNAs (miRNAs) from NCBI's Gene Expression Omnibus database (GEO,; through the R package of limma in Bioconductor, we can screen the differentially expressed miRNAs and mRNAs, and then we further analyzed the predicted target genes by the methods such as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and miRNA-mRNA regulatory network construction and protein-protein interaction (PPI) network using Cytoscape 3.6.1. Results: Compared with normal juvenile skin tissues, 6 upregulated microRNAs and 5 downregulated microRNAs were identified from 166 downregulated microRNAs and 58 upregulated microRNAs in juvenile dermatomyositis tissues. The enrichment pathways of differentially expressed microRNAs include cell adhesion molecules (CAMs), autoimmune thyroid disease, Type I diabetes mellitus, antigen and presentation, viral myocardium, graft-versus-host disease, and Kaposi sarcoma-associated herpes virus infection. By screening of microRNA-messenger RNA regulatory network and construction of PPI network map, three target miRNAs were identified, namely, miR-193b, miR-199b-5p, and miR-665. Conclusion: We identified mir-193b, mir-199b-5p, and mir-6653 target miRNAs by exploring the miRNA-mRNA regulation network mechanism related to the pathogenesis of juvenile dermatomyositis, which will be of great significance for further study on the pathogenesis and targeted therapy of juvenile dermatomyositis.

PLoS One ; 14(8): e0221638, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31442259


OBJECTIVE: To explore the use of Contrast-enhanced Ultrasound (CEUS) in evaluating angiogenesis in a xenograft nasopharyngeal carcinoma (NPC) model in nude mice and the evolution of CEUS parameters according to the growth of NPC. METHODS: Nude mice were divided into three groups according to experiments conducted at various times from tumor implantation (8 mice/group; group A: 4 weeks from implantation; group B:6 weeks from implantation; group C:8 weeks from implantation). CNE-2 cells were transplanted in 24 nude mice and CEUS evaluations of the tumors were performed at 4, 6 or 8 weeks from implantation. CEUS parametric perfusion images and pathological findings were recorded. R version 3.4.4 software was used to analyze the CEUS parameters and pathological findings. RESULTS: One-way anova analysis indicated statistically significant differences among the three groups with the parameters of peak intensity (PI) (p<0.001), area wash in (AWI) (p<0.001), area wash out (AWO) (p<0.001) and tumor volumes (p<0.001).Pearson correlation coefficient analysis indicated that microvessel density (MVD) was correlated with tumor volume (r = 0.644, p = 0.001), PI (r = 0.904, p<0.0001), AWI (r = 0.547, p = 0.008) and AWO (r = 0.744, P<0.0001). Tumor volume was correlated with MVD (r = 0.644, p = 0.001), PI (r = 0.625, p = 0.002), AWI (r = 0.528, p = 0.012) and AWO (r = 0.784, p<0.001). The percentage of necrosis in histological sections was correlated with the percentage of CEUS unperfused area (r = 0.446,p = 0.038). Spearman rank correlation coefficient analysis indicated that vascular endothelial growth factor (VEGF) was correlated with PI (r = 0.462, P = 0.032). Welch t test indicated PI, AWI and AWO parameters were significantly lower than that of kidneys (p<0.001, p = 0.009, p = 0.005). CONCLUSIONS: The CEUS parameters PI, AWI and AWO indirectly reflect the MVD and the tumor volume in our model of subcutaneous transplanted NPC in nude mice, providing precious information on angiogenesis and tumor growth. VEGF may play a role in promoting angiogenesis of NPC.