Georgian medical news · 2026
EXPLORING POTENTIAL KEY GENES AND MECHANISMS OF PERIODONTITIS THROUGH INTEGRATED BIOINFORMATICS ANALYSIS.
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Zhu Yuxin, Deng Tong, Wen Wenjie, Deng Chao, Li Rui, Zhang Donglin
Аннотация
BACKGROUND: Periodontitis is a prevalent chronic inflammatory disease characterized by progressive destruction of periodontal supporting tissues, yet its precise molecular mechanisms remain incompletely understood. This study aimed to identify key pathogenic genes and elucidate the underlying molecular mechanisms of periodontitis through integrated bioinformatics analysis.
METHODS: Periodontitis-related gene expression datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package and visualized via volcano plots. Gene set enrichment analysis (GSEA) was performed to characterize enriched biological pathways. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized in Cytoscape. Functional enrichment analysis was conducted using the ClueGO plugin, incorporating Reactome, Gene Ontology (GO), and KEGG annotations. Hub genes were identified using the cytoHubba plugin with five topological algorithms (Degree, MNC, MCC, Closeness, and EPC), and key genes were determined through Venn diagram analysis. Individual GSEA was subsequently performed for each key gene. Finally, a competing endogenous RNA (ceRNA) regulatory network was constructed based on the lncRNA-miRNA-mRNA axis.
RESULTS: A total of 7 key genes were identified: IL1B, CXCR4, FCGR3B, SELL, CD19, CXCL8, and CD38. Functional enrichment analyses revealed significant involvement of cytokine-cytokine receptor interaction, hematopoietic cell lineage, collagen degradation, and chemokine signaling pathways. GSEA of individual key genes further confirmed the central roles of immune and inflammatory pathways in periodontitis. The ceRNA network revealed regulatory interactions among lncRNAs, miRNAs, and key genes, particularly centered on IL1B, SELL, and FCGR3B.
CONCLUSION: This integrated bioinformatics study systematically identified key genes and regulatory networks in periodontitis, offering promising candidates for future therapeutic development.
METHODS: Periodontitis-related gene expression datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package and visualized via volcano plots. Gene set enrichment analysis (GSEA) was performed to characterize enriched biological pathways. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized in Cytoscape. Functional enrichment analysis was conducted using the ClueGO plugin, incorporating Reactome, Gene Ontology (GO), and KEGG annotations. Hub genes were identified using the cytoHubba plugin with five topological algorithms (Degree, MNC, MCC, Closeness, and EPC), and key genes were determined through Venn diagram analysis. Individual GSEA was subsequently performed for each key gene. Finally, a competing endogenous RNA (ceRNA) regulatory network was constructed based on the lncRNA-miRNA-mRNA axis.
RESULTS: A total of 7 key genes were identified: IL1B, CXCR4, FCGR3B, SELL, CD19, CXCL8, and CD38. Functional enrichment analyses revealed significant involvement of cytokine-cytokine receptor interaction, hematopoietic cell lineage, collagen degradation, and chemokine signaling pathways. GSEA of individual key genes further confirmed the central roles of immune and inflammatory pathways in periodontitis. The ceRNA network revealed regulatory interactions among lncRNAs, miRNAs, and key genes, particularly centered on IL1B, SELL, and FCGR3B.
CONCLUSION: This integrated bioinformatics study systematically identified key genes and regulatory networks in periodontitis, offering promising candidates for future therapeutic development.