Results and Discussion
Three diseases-related subnetworks
We used three differentially expressed genes to construct three
disaease-related subnetworks based on the disease background network,
which contained 409 TFs, 2300 miRNAs, 10,697 target gene and 48,423
edges. We then mapped the 247, 237, and 103 differentially expressed
genes obtained from the three disease-related differential expression
datasets to the background network to obtain three subnetworks. The
HCC-related TF-miRNA regulatory subnetwork included 228 edges and 169
nodes, including 22 TFs, 95 miRNAs, and 52 target genes.
The HCC-related TF-miRNA regulatory
subnetwork included 911 edges and 464 nodes including 46 TFs, 236
miRNAs, and 182 target genes. The HCC-HCV-related TF-miRNA regulatory
subnetwork included 513 edges and 307 nodes, including 29 TFs, 157
miRNAs, and 121 target genes (Fig. S1).
Gene ontology (GO) and KEGG functional enrichment analyses were
performed to identify the significantly enriched biological processes
and pathways in the three subnetworks using DAVID 23online tools to perform enrichment analysis. The significantly enriched
results (FDR<0.05) are shown in Fig. 2.
We found that the significantly
enriched biological processes included cell cycle, response to organic
substance, positive regulation of transcription from RNA polymerase II
promoter, and positive regulation of RNA metabolic process. We also
found significantly enriched KEGG pathways, such as cell cycle, p53
signaling pathway, pathways in cancer, metabolism of xenobiotics by
cytochrome P450, MAPK signaling pathway and Wnt signaling pathway. Many
of these pathways have previously been implicated in HCC and HCV
diseases 24-27.
The risk regulatory pathways and key
regulators
The BFS method was used to traverse three subnetworks to obtain all
pathways in the network with an in-degree of 0 to out-degree of 0, with
pathway lengths required to be longer than two nodes. The HCC subnetwork
obtained 5,284,069 pathways, the HCC-HCV subnetwork contained 929
pathways, and the HCV subnetwork contained 235 pathways. Each pathway
contained several subnetworks, and elucidating key regulators that play
important roles in the development of the disease requires identifying
the most important of these. To this end, we used KP scores to screen
the 5 highest scoring pathways in all the subnetworks (Table 1). All the
regulatory pathways were integrated to obtain a network of HCV and HCC
processes. From this network, we found that HCV-related genes were
mainly enriched in the upstream
nodes of the network (green background in Fig. 3), while the genes
affecting HCC and HCV were mainly enriched in the middle regions of the
network (yellow background in Fig. 3). Finally, genes related to HCC
appeared in the downstream regions of the network (violet background in
Fig. 3). The network structure also reflected the role of inflammation
in carcinogenesis, as many genes associated with inflammatory factors
linked nodes in the HCC and HCV networks. To test whether abnormal
expression of some core genes at the center of the network affected
patient outcome, we examined hsa-miR-155-5p , FOXM1 ,EZH2 more closely.
EZH2 and hsa-miR-155-5p are key regulators
We further analyzed the core genes in the network, and found thatFOXM1 , EZH2 , E2F1 and hsa-miR-93-5p were
significantly correlated with the occurrence of HCC in HCV patients
(Fig. S2). Out of these genes, EZH2 was the most downstream and
directly regulated the hub node for hsa-miR-155-5p within the
network (Fig. S3). This indicated that EZH2 may be an important
gene implicated in the transition from HCV to HCC. Other research has
found that EZH2 and hsa-miR-155-5p may play important
roles in the progression of both HCC and HCV 28-31,
which fits with works indicating that hsa-miR-155-5p participates
in HCV-induced HCC processes 32,33. High expression ofEZH2 and hsa-miR-155-5p has also been shown to correlate
with the severity of HCC 34,35. Our network analysis
indicated that hsa-miR-155-5p not only plays a key regulatory
role in HCC, but also plays a role in hepatitis-induced liver cancer.
Research investigating the effects of controlling the expression ofEZH2 and hsa-miR-155-5p in HCV patients may yield new
treatment options.
In order to study the correlation of EZH2 andhsa-miR-155-5p expression with HCC survival rate, we compared the
expression of these two genes in normal samples versus disease samples.
Interestingly, we found that both EZH2 and hsa-miR-155-5pwere expressed significantly higher in tumor samples compared with
normal tissue. Between the two, we found that EZH2 was strongly
correlated with the survival of patients (Fig. 4), indicating that it
may be important for early diagnosis and risk prediction.