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.