These findings suggest that 6 mRNAs (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL), 3 miRNAs (hsa-miR-125a-3p, hsa-miR-200a, hsa-miR-142-3p) and 3 lncRNAs (MALAT1, TUG1, FGD5-AS1) might be involved in the lncRNA-associated ceRNA network of periodontitis. This study sought to illuminate further the genetic and epigenetic mechanisms of periodontitis through constructing an lncRNA-associated ceRNA network.

Background Hereditary gingival fibromatosis (HGF) is a rare, hereditary oral disease that would cover the crown of teeth, resulting in tooth migration, abnormal occlusion, or psychological issues, mostly seen in children and adolescents. Periodontitis is a chronic inflammatory illness that may lead to bone and tooth loss. While HGF patients with periodontitis often have severe clinical outcomes, its pathogenesis is not fully understood. This study was to construct a competing endogenous RNA (ceRNA) network between HGF and periodontitis using a bioinformatics approach, in order to explore the pathogenesis of these two co-existence diseases.Methods Differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) database between HGF and periodontitis. The Search Tool for Interacting Genes (STRING) database was used to retrieve functional intersection parts between overlapping DEGs for constructing the protein-protein interaction (PPI) network analysis. To build a ceRNA network, 6 databases were used to predict the microRNAs(miRNAs) for the above-mentioned top 5 key genes by using R software, and StarBase (v2.0) database was then predicted to acquire the long non-coding RNAs (lncRNAs) that interact with the aforementioned differentially expressed miRNAs.Results 40 intersecting genes were identified through differential expression analysis and the top 5 key targets, including IL6, FLG2, LOR, KRT2, and LCE2B, were recognized as core targets between HGF and periodontitis from the PPI network. A ceRNA network was constructed with 3 mRNAs (IL6, FLG2, and KRT2), 3 miRNAs (hsa-miR-149-5p, hsa-miR-760, and hsa-miR-376c-3p), and 4 lncRNAs (KCNQ1OT1, NEAT1, HELLPAR, LRRC75A-AS1).Conclusion Current results are obtained by bioinformatics approaches, although its accuracy still needs verification by follow-up biological experiments, this novel ceRNA network may help us to reveal the correlation between HGF and periodontitis deeply, provide diagnosis molecular markers, and develop new therapeutic options for patients with HGF and periodontitis in near future.