Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis

Periodontitis is a complex immune-inflammatory condition characterized by the disruption of the periodontal ligament and subsequent formation of periodontal pockets and by alveolar bone loss, often resulting in tooth loss [1]. The amount of tissue destruction is generally commensurate with dental plaque levels, host defence, and related risk factors [2, 3]. Periodontitis contributes significantly to the overall oral disease burden, with its severe form representing the sixth most prevalent condition, estimated to affect 7–11% of the global adult population [3, 4]. The prevalence of periodontitis increases gradually with age [3], particularly in adults over 50 years of age [5]. Periodontitis is also associated with systemic conditions such as neoplastic disorders, obesity, and diabetes [1]. The etiology of periodontitis is multifactorial. Subgingival dental biofilm elicits a host inflammatory and immune response, ultimately leading to irreversible destruction of the periodontium in a susceptible host [6]; however, the precise molecular pathogenesis of periodontitis has not been completely identified.

In recent years, microRNAs (miRNAs) have gained increased attention from researchers for periodontal disease studies [7]. MiRNAs are endogenous, noncoding, small RNAs containing approximately 22 nucleotides [8]. MiRNAs regulate target genes at the transcriptional or translational level based on the sequence complementarity between miRNAs and the 3′-untranslated region of target genes [9]. An independent study found that miR-146a is highly expressed in the serum of patients with chronic periodontitis and that its expression is directly proportional to disease severity [10]. Zhou et al. suggested that miR-21 downregulates Porphyromonas gingivalis lipopolysaccharide (LPS)-induced inflammation and that miR-21 plays a protective role in periodontitis progression [11]. Akkouch et al. suggested that local treatment with miR-200c was effective for alveolar bone resorption in a rat model of periodontitis [12]. MiRNAs mediate the progression of periodontal disease in a variety of ways, (i) through their role in periodontal inflammation and the dysregulation of homeostasis, (ii) as regulatory targets of lncRNAs, (iii) by contributing to periodontal disease susceptibility through miRNA polymorphisms, and (iv) as periodontal microflora modulators via viral miRNAs [7].

Previous studies have examined the expression profile of periodontitis to identify the differentially expressed miRNAs (DEMis) and mRNAs (DEMs). Due to the limitations of the comparative analysis in independent studies, there are still problems with the interaction between DEMis and DEMs involved in periodontitis. The aim of this study was to identify the key genes, miRNAs and TFs and construct miRNA–mRNA–TF regulatory networks to investigate the underlying molecular mechanism in periodontitis.

Periodontitis is a complex infectious disease with various causes and contributing factors [22]. An increasing number of studies are now being conducted on the diagnosis and treatment of periodontitis. However, due to the limited understanding of the pathogenesis of periodontitis, the prognosis of patients with periodontitis remains poor. Recently, microarray technology has been used to reveal thousands of gene changes in the development of various diseases. Previous studies have examined the expression profile of periodontitis to identify DEMis and DEMs [23, 24]. Due to the limitations of the comparative analysis in independent studies, there are still problems with the interaction between DEMis and DEMs involved in periodontitis. In addition, in the context of genetic regulatory networks, the synergistic effects of transcription factors and miRNAs are largely unknown. To the best of our knowledge, this is the first attempt to integrate miRNA and mRNA expression profile data and construct a miRNA–mRNA–TF regulatory network. The identification and analysis of periodontitis-related genes, miRNAs and TFs may reveal the potential pathogenesis of periodontitis at the molecular level, which is helpful for identifying potential diagnostic and therapeutic strategies for future studies.

In our study, an integrative analysis of microarray and RNA-sequencing datasets was conducted, and a total of 121 DEMs were identified as significant elements for periodontitis. For the purpose of fully understanding the function and mechanism of these DEMs, we conducted GO and KEGG enrichment analyses utilizing the “enrichplot” packages in R. Gene Ontology analysis of DEMs showed that DEMs are mainly involved in positive regulation of the cell cycle, gland development, and positive regulation of the cell cycle process, and these biological processes mainly contribute to cardiac cell proliferation and differentiation. KEGG pathway analysis demonstrated that abnormal molecular expression of several pathways may contribute to the pathogenesis of periodontitis, such as the IL-17 signaling pathway. In previous studies, the activation of the IL-17 signalling pathway has been shown to be associated with periodontitis. A study performed by Satoru showed that IL-17A may promote the progression of periodontitis through proinflammatory cytokine production [25]. Moreover, IL-17 is essential for the maintenance of bone mass, as it orchestrates osteoclast differentiation and activation [26]. Kukolj et al. revealed that IL-17 inhibited both the proliferation and migration of periodontal ligament mesenchymal stem cells and decreased their osteogenic differentiation by activating ERK1/2 and JNK mitogen-activated protein kinases [27]. Therefore, inhibition of the IL-17 signalling pathway may be a therapeutic strategy for periodontitis.

Among the five TFs (SPI1, SPIB, CEBPB, NFATC1, and SRF) predicted in the present study. All these TFs have been verified as key modulators and potential therapeutic targets in a wide variety of inflammatory diseases. SPI-1 genes are responsible for the invasion of host cells, regulation of the host immune response, e.g., the host inflammatory response, immune cell recruitment and apoptosis, and biofilm formation [28]. SRIB has been previously reported to display anti-inflammatory functions by producing interleukin IL-9, and higher levels of IL-9 and SRIB were detected in gingivitis patients than in healthy individuals [29]. CEBPB is a member of the family of transcription factors that play roles in a wide range of cellular processes, such as cellular apoptosis, proliferation, adipocyte differentiation, carbohydrate metabolism and inflammation [30, 31]. Overexpression of CEBPB markedly increased the expression of the proinflammatory cytokines IL-6 and IL-8 in human periodontal ligament cells in response to LPS [32]. Li et al. demonstrated that SRF appears to be involved in the regulation of IL1B and CXCL8 [33].

Hsa-mir-203 and hsa-mir-671-5p were identified as hub miRNAs based on integrated network analysis. MiR-203 was first identified in the pathogenesis of psoriasis and is involved in various physiological and pathological processes [34]. Zhang et al. demonstrated that miR-203 overexpression suppresses TRAF6-induced IL-β, IL-6, and TNF-α activation in human renal mesangial cells and proximal tubular cell line cells [35]. A recent study showed that miR-203 protects against microglia-mediated brain injury by targeting the MyD88 protein to modulate the inflammatory response [36]. Furthermore, Wang et al. showed that miR-203 inhibits inflammation to alleviate myocardial ischaemia–reperfusion injury [37]. It has been reported that miR-671-5p represses cell proliferation, migration, invasion and the inflammatory response [38]. MiR-671 mimics ameliorated IL-1β-induced proliferation inhibition and apoptosis stimulation and alleviated the progression of osteoarthritis in mice [39]. However, whether miR-671-5p and miR-203 are implicated in the pathogenesis of periodontitis can be investigated in future studies.

Although we constructed a potential miRNA–mRNA–TF regulatory network by integrating multiple microarray datasets for the first time, our study is limited by the fact that we performed an integrated analysis based on only one microarray dataset and one RNA-seq dataset, which may reduce the credibility of miRNA–mRNA–TF coregulatory network analysis. In addition, the clinical significance of the predictions has not been evaluated by the receiver operating curve (ROC) using the area under the curve (AUC), and further work needs to be done in the future. Moreover, predictions were made based on public databases; further experiments are lacking to validate the analytical results as a firm basis.

In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a miRNA–mRNA–TF network was established to provide a comprehensive perspective of regulatory mechanism networks of periodontitis.