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.