Identifying the focuses of hereditary gingival fibromatosis with bioinformatics strategies

OBJECTIVEThe objective of this study was to detect the undiscovered bioinformatics information about hereditary gingival fibromatosis and find focuses from published datasets. METHODSTwo published datasets containing gingival tissue expression profiles of HGF and healthy groups were collected from GEO database. GSE4250 was utilized for cardinality analysis, including the differentially expressed gene analysis, enrichment analyses, hierarchical clustering analysis, and protein-protein interaction network. Key genes were obtained from the protein interaction network plot. GSE58482 was utilized for validation. RESULTSAnalysis of the expression profiling by array, there were 785 genes (380 upregulated genes, 405 downregulated genes) expressed differentially between HGF gingival tissue and healthy gingival tissue. KEGG and GO enrichment analyses obtained candidate pathways. Differentially expressed genes were associated with activated pathways like skin barrier pathway and cornified envelope pathway. Repressed pathways included ion homeostasis pathway, receptor ligand activity pathway, and cell population proliferation pathway. Key genes such as F2R, TGM7, and MMP13 were confirmed with differential expression by external validation. CONCLUSIONBy bioinformatics approaches, we found new discoveries including several pathways and key genes. These discoveries deserve attention and research in the future.