Gingival fibromatosis (GF) is a rare condition of gingival overgrowth, characterized by a slowly progressive, benign, localized or generalized fibrous enlargement of maxillary and mandibular keratinized gingiva (1–3). GF may co-exist with various genetic syndromes, such as Rutherfurd syndrome, Cowden syndrome, Zimmerman-Laband syndrome, Murray-Puretic syndrome and hyaline fibromatosis syndrome, or occurs as an apparent isolated trait as non-syndromic hereditary GF (HGF) (1,4–6). HGF, also known as hereditary gingival hyperplasia or idiopathic gingival fibromatosis, is the most common genetic form of GF that is typically transmitted as an autosomal-dominant trait (7,8). HGF affects males and females equally at an estimated incidence of 1 per 175,000 of the population (1,9). As HGF is rare and benign, and due to an increase in the number of non-surgical treatments, it is difficult to collect large samples of HGF. To date, four loci, namely 2p22.1, 5q13-q22, 2p23.3-p22.3 and 11p15, have been associated with HGF, and a heterozygous frameshift mutation in Son of sevenless-1 (SOS1) has been reported as the cause of autosomal-dominant HGF in a family showing linkage to 2p21 (10). HGF exhibits an autosomal dominant inheritance pattern, although its penetrance and expressivity are variable (8). Indeed, 20% of cases have no family history of the condition (11). Diagnosis of HGF mainly depends on medical history, clinical examination, blood tests and histopathological evaluation of affected gingival tissue (1). However, owing to high genetic heterogeneity, genetic testing to confirm the diagnosis is not justified (12). It is therefore important to identify key signature genes and to understand the molecular mechanisms underlying HGF.

In the present study, microarray data from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) (13) were used to identify differentially expressed genes (DEGs) in HGF. Subsequently, the identified DEGs were analyzed for enriched gene ontology (GO) terms and used for protein-protein interaction (PPI) network construction. Overall, this aimed to predict HGF-related genes and understand the molecular mechanisms of HGF.

In the current periodontal diseases and conditions classification, which was developed by Armitage in 1999 (21), HGF is defined a benign, non-hemorrhagic and fibrous gingival overgrowth that may cover all or part of the teeth. HGF is also one of the subtypes of gingival lesions of genetic origin among gingival diseases (6,22,23). HGF gingiva is typically pink in color and has a fibrous appearance and marked stippling without signs of inflammation, and covers the teeth partially or totally with a variable degree of severity, without affecting the bone (6,23–26). It generally interferes with speech, lip closure and chewing, and may also become a psychological burden by affecting the self-esteem of patients (6). HGF presents an autosomal dominant inheritance pattern, although its penetrance and expressivity are variable. However, owing to high genetic heterogeneity, genetic testing to confirm the diagnosis is not justified. Thus, it is important to identify the key signature genes and to understand the molecular mechanisms of HGF. As an important discipline of biological science, bioinformatics analysis employs scientific resources for research purposes (27), and is considered an efficient method for predicting disease-related genes.

In the present study, 249 DEGs were identified, consisting of 65 upregulated and 184 downregulated genes, in the GSE4250 dataset. Notably, among the top 10 upregulated genes of the DEGs, the gene encoding bone morphogenetic protein and activin membrane bound inhibitor has previously been associated with fibromatosis (28). The DEGs were subsequently assessed by GO enrichment and PPI network analyses. The top 10 enriched GO terms of the DEGs were ‘plasma membrane’, ‘extracellular exosome’, ‘extracellular region’, ‘extracellular space’, ‘structural molecule activity’, ‘apical plasma membrane’, ‘endosome’, ‘structural constituent of cytoskeleton’, ‘keratinocyte differentiation’ and ‘peptide cross-linking’. These results indicated that HGF-related enriched GO terms are principally associated with cell growth and tissue hyperplasia. Histologically, HGF is characterized by the growth and hyperplasia of gingival epithelial cells (22). Straka et al (29) observed in HGF that some collagen fibrils exhibited loops in the gingival lamina propria and identified the presence of empty perinuclear space in the cytoplasm of epithelial cells. These findings may relate to the enriched GO terms. The GO enrichment may indicate targets for research and guide studies on HGF-related genes based on the enriched GO terms. KEGG pathways were also evaluated based on the DEGs, but no significant pathways were identified. The method of DEG screening or the algorithms of the tools used may have lead to this result. From the PPI network of the DEGs, 12 core genes were identified that may serve critical roles in HGF. Among these core genes, POTEI had the highest node degree. However, to the best of our knowledge, there are no reports on the potential role of POTEI in HGF, though this may be due to the general lack of studies on HGF-related genes. Furthermore, while it has not been reported that CALB2, is directly associated with HGF, Barak et al (30) documented that calretinin encoded by the CALB2 gene may be an important immunohistochemical marker in other benign and malignant fibromatosis. The FGF family also serve an important role in fibroblast growth (31). Meanwhile, Lee et al (32) reported that LOR was important in keratinocyte differentiation in a study on the cell envelope of normal human oral keratinocytes. C3 has also been associated with fibrous papule development (33). Although reports on the functions of these core genes are limited, they may serve important roles in HGF.

In the present study, genes reported previously, including SOS1 (10), calcium/calmodulin-dependent protein kinase IV and adenosine triphosphate-binding cassette subfamily A member 5 (1), were not identified. This may have been due to the tools employed and the restricted screening parameters, as well as the limited scope of research on HGF. A crucial limitation of the present study was the small number of samples with and without HGF. Therefore, larger datasets and further experiments, for instance using reverse transcription-quantitative polymerase chain reaction, are required to validate the present results. Bioinformatics analysis is an efficient method for predicting potential diagnostic and therapeutic targets. However, the increased number of data mining and analytical tools and algorithms poses a challenge (34), as results for the same data using different bioinformatics tools may vary. Furthermore, the predictions require verification through experimental and clinical methods.

In summary, the prediction of potential diagnostic and therapeutic targets in diseases using bioinformatics methods is an efficient strategy in clinical research, though also poses a number of challenges. In the present study, a public dataset of GEO was used to analyze the potential diagnostic and therapeutic targets of HGF. The current predictions of potential diagnostic and therapeutic targets now require verification through experimental methods in cell and animal models prior to clinical trials.