Differential Expression Analysis, Enrichment Analysis, and circRNA–miRNA–mRNA Network Analysis

Differential expression analysis between two groups was performed using the DESeq R package (1.8.3). The p-value was adjusted using the Benjamini and Hochberg method. Corrected p-value of 0.05 was set as the threshold for significantly differential expression by default.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used on significantly differential expressed genes, including host genes of differentially expressed circRNAs and the target gene candidates of differentially expressed miRNAs. Gene ontology (GO) enrichment analysis was implemented by the GOseq R package, in which gene length bias was corrected (Young et al., 2010). GO terms with corrected p-value of less than 0.05 were considered significantly enriched by differentially expressed genes. KEGG is a database resource for understanding high-level functions and utilities of the biological system (Kanehisa et al., 2008), such as the cell, the organism, and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/). We used KOBAS software to test their statistical enrichment in KEGG pathways (Mao et al., 2005).

The circRNA–miRNA–mRNA network was developed based on possible functional relationships between DE–circRNAs, DE–miRNAs, and differential expression genes (DEGs). Firstly, the target circRNAs of DE–miRNAs were predicted by scanning for conserved miRNA target sites with MiRanda (Enright et al., 2003); then the interactions between target circRNAs and DE–circRNAs were identified; and finally circRNA–miRNA regulation network was constructed. Secondly, the target mRNAs of DE–miRNAs were predicted by scanning for conserved miRNA target sites with MiRanda; then the interactions between target mRNAs and DEGs were identified; and finally miRNA–mRNA regulation network was constructed. At last, circRNA–miRNA–mRNA network was generated using a combination of circRNA–miRNA network and miRNA–mRNA network with Cytoscape 3.6.1 software (Su et al., 2014), and only the network follows the expression trend of “up–down–up” or “down–up–down” was selected for further research. In conclusion, the construction of circRNA–miRNA–mRNA network followed the following principles: circRNAs served as bait, microRNAs served as core, and RNA served as target.