Abstract
Hereditary gingival fibromatosis (HGF) is a benign, non-hemorrhagic and fibrous gingival overgrowth that may cover all or part of the teeth. It typically interferes with speech, lip closure and chewing, and can also be a psychological burden that affects the self-esteem of patients. Owing to high genetic heterogeneity, genetic testing to confirm diagnosis is not justified. It is therefore important to identify key signature genes and to understand the molecular mechanisms underlying HGF. The aim of the present study was to determine HGF-related genes and to analyze these genes through bioinformatics methods. A total of 249 differentially expressed genes (DEGs), consisting of 65 upregulated and 184 downregulated genes, were identified in the GSE4250 dataset of Gene Expression Omnibus (GEO) when comparing with the gums of HGF patients with those of healthy controls using the affy and limma packages in R. Subsequently, 28 enriched gene ontology terms were obtained from the Database for Annotation, Visualization and Integrated Discovery, and a protein-protein interaction (PPI) network was constructed and analyzed using STRING and Cytoscape. There were 99 nodes and 118 edges in the PPI network of these DEGs obtained through STRING. Among these nodes, 12 core genes were identified, of which the highest degree node was the gene for POTE ankyrin domain family member I. Collectively the results indicate that bioinformatics methods may provide effective strategies for predicting HGF-related genes and for understanding the molecular mechanisms of HGF.