Figure Legends
Fig. 1 Illustration of the framework for mining regulatory pathways and key regulators by constructing HCV-induced HCC networks.
Fig. 2 A, B and C. The HCC, HCC-HCV and HCV-related TF-miRNA regulatory network GO and KEGG functional enrichment analysis. The numbers represent gene counts for each pathway/GO term within the network. The Q-value represents the Bonferroni-corrected p-value in gene enrichment analysis. The rich factor represents the ratio of the number of genes in the subnetworks to the total number of genes in the pathways/GO terms. (A) Disease-related background network. (B) HCC-related TF-miRNA regulatory subnetwork. (C) HCC-HCV-related TF-miRNA regulatory subnetwork. (D) HCV-related TF-miRNA regulatory subnetwork.
Fig. 3 The key TF-miRNA regulatory network. The orange nodes represent target genes, the blue nodes represent TFs, and the green nodes represent miRNAs. Different border colors represent the gene source of different subnetworks.
Fig. 4 EZH2 gene differential expression significantly correlated with the overall survival of HCC patients.
Table 1 The KP score of predicted pathways from the three disease-associated subnetworks.