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. 2020 Dec;14(6):391-398.
doi: 10.1049/iet-syb.2020.0061.

Identification and analysis of circRNA-miRNA-mRNA regulatory network in hepatocellular carcinoma

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Identification and analysis of circRNA-miRNA-mRNA regulatory network in hepatocellular carcinoma

Daxiang Zhou et al. IET Syst Biol. 2020 Dec.

Abstract

This study was to identify important circRNA-miRNA-mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset GSE97332 and miRNA dataset GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug-gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa-miR-25-3p, hsa-miR-3692-5p, hsa-miR-4270, hsa-miR-331-3p, and hsa-miR-125a-3p. Among the network, hsa-miR-25-3p targeted the most genes, hsa-miR-3692-5p and hsa-miR-4270 were targeted by more circRNAs than other miRNAs, hsa-circ-0034326 and hsa-circ-0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug-gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa-circ-0034326 and hsa-circ-0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR-25-3p, miR-3692-5p, and miR-4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC.

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Figures

Fig 1
Fig 1
Heatmap for differentially expressed (a) circRNA, (b) miRNA The left vertical axis presents the differentially expressed circRNA or miRNA cluster. The horizontal axis represents the samples, among which black indicates normal sample, and gray indicates HCC sample. The color from green to red represents the gene from down‐regulation to up‐regulation
Fig 2
Fig 2
CircRNA–miRNA–mRNA regulatory network Triangle represents up‐regulated miRNA, arrow shape represents down‐regulated miRNA, hexagonal represents up‐regulated circRNA, square represents downregulated circRNA, and circle represents target gene. The T‐arrow indicates the circRNA–miRNA correlation pairs and the arrow indicates the miRNA–mRNA correlation pairs
Fig 3
Fig 3
Top ten terms for target genes enriched pathways (a) Bubble diagram, (b) Network diagram The x ‐axis label represents gene ratio and y ‐axis label represents KEGG pathways. The node colour changes gradually from dark gray to light gray in ascending order according to the P ‐value. The size of each node represents the number of counts. Furthermore, the interconnected nodes represent direct relationships. KEGG, Kyoto Encyclopaedia of Genes and Genomes
Fig 4
Fig 4
Survival curves for NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 The horizontal axis indicates the overall survival time in months, and the vertical axis indicates the survival probability. The cut‐off value is the median gene expression. The gray line means high expression level of genes and black line means low expression level of genes. P  < 0.05 is considered statistically significant
Fig 5
Fig 5
Top ten terms for all 37 prognostic‐related genes enriched pathways (a) Bubble diagram, (b) Network diagram The x ‐axis label represents gene ratio and y ‐axis label represents KEGG pathway. The size of dot indicates the amount of genes under a specific term and the colour of the dot indicates the P ‐value. The interconnected nodes represent direct relationships. KEGG, Kyoto Encyclopaedia of Genes and Genomes
Fig 6
Fig 6
Drug–gene interaction network Dark gray ellipse represents target genes and a gray square represents drugs. The line represents the relationship between drug and gene

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