Identification of potentially functional circRNAs and prediction of circRNA‐miRNA‐mRNA regulatory network in periodontitis: Bridging the gap between bioinformatics and clinical needs

BACKGROUND AND OBJECTIVE: Periodontitis is a multifactorial chronic inflammatory disease that can lead to the irreversible destruction of dental support tissues. As an epigenetic factor, the expression of circRNA is tissue‐dependent and disease‐dependent. This study aimed to identify novel periodontitis‐associated circRNAs and predict relevant circRNA‐periodontitis regulatory network by using recently developed bioinformatic tools and integrating sequencing profiling with clinical information for getting a better and more thorough image of periodontitis pathogenesis, from gene to clinic. MATERIAL AND METHODS: High‐throughput sequencing and RT‐qPCR were conducted to identify differentially expressed circRNAs in gingival tissues from periodontitis patients. The relationship between upregulated circRNAs expression and probing depth (PD) was performed using Spearman's correlation analysis. Bioinformatic analyses including GO analysis, circRNA‐disease association prediction, and circRNA‐miRNA‐mRNA network prediction were performed to clarify potential regulatory functions of identified circRNAs in periodontitis. A receiver‐operating characteristic (ROC) curve was established to assess the diagnostic significance of identified circRNAs. RESULTS: High‐throughput sequencing identified 70 differentially expressed circRNAs (68 upregulated and 2 downregulated circRNAs) in human periodontitis (fold change >2.0 and p < .05). The top five upregulated circRNAs were validated by RT‐qPCR that had strong associations with multiple human diseases, including periodontitis. The upregulation of circRNAs were positively correlated with PD (R = .40–.69, p < .05, moderate). A circRNA‐miRNA‐mRNA network with the top five upregulated circRNAs, differentially expressed mRNAs, and overlapped predicted miRNAs indicated potential roles of circRNAs in immune response, cell apoptosis, migration, adhesion, and reaction to oxidative stress. The ROC curve showed that circRNAs had potential value in periodontitis diagnosis (AUC = 0.7321–0.8667, p < .05). CONCLUSION: CircRNA‐disease associations were predicted by online bioinformatic tools. Positive correlation between upregulated circRNAs, circPTP4A2, chr22:23101560‐23135351+, circARHGEF28, circBARD1 and circRASA2, and PD suggested function of circRNAs in periodontitis. Network prediction further focused on downstream targets regulated by circRNAs during periodontitis pathogenesis.