Identification of Genetic Alterations in Periodontitis Patients with Poorly Controlled Type 2 Diabetes Mellitus

Authors : Duru ARAS TOSUN, Aynur KARADAĞ

Pages : 101-107

Doi:10.52037/eads.2022.0041

View : 37 | Download : 13

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :Purpose Periodontitis and diabetes are highly prevalent chronic diseases associated with upregulated inflammation that may adversely affect each other. The aim of this study is to determine underlying molecular mechanisms via bioinformatic tools as a guide for future studies. Materials and methods Expression data insert ignore into journalissuearticles values(GSE156993); of Type 2 Diabetes Mellitus insert ignore into journalissuearticles values(T2DM); and Periodontitis insert ignore into journalissuearticles values(P); patients was selected from the Gene Expression Omnibus insert ignore into journalissuearticles values(GEO); database. Study groups were defined as follows; T2DM-poor insert ignore into journalissuearticles values(HbA1c≥8.5%, n=7);, T2DM-well insert ignore into journalissuearticles values(HbA1c<7.0%, n=7); and P insert ignore into journalissuearticles values(n=6);. The differentially expressed genes insert ignore into journalissuearticles values(DEGs); between groups were analyzed with GEO2R insert ignore into journalissuearticles values(log2FC≥0 or ≤0);. Kyoto Encyclopedia of Genes and Genomes insert ignore into journalissuearticles values(KEGG); was used for the identification of biological pathways. Protein network was constructed in STRING database and hub genes were detected. Data validation was performed via ELISA assay for two hub genes. Significance was set to P<0.05. Results 1008 genes were upregulated, while 610 genes were down-regulated in T2DM-poor group compared to the controls. KEGG analysis revealed that the highest number of down-regulated genes were clustered in cancer pathways and PI3K-Akt signaling pathway, as upregulated genes were purine metabolism, parathyroid hormone metabolism, cGMP-PKG signaling pathway and Rap1 signaling pathway. For increasing and decreasing expression profiles, hub nodes with the highest score were selected as SMAD4, HNF4A, SMARCA4 and SRC, TNF, RFC2, RFC3 genes, respectively. Conclusion Bioinformatic analyses revealed that metabolomic, inflammatory and cancer pathways were altered in periodontitis patients with poorly controlled diabetes. As protein-protein interactions may differ in vivo, further validation of the presented data is needed.
Keywords : Type 2 Diabetes Mellitus, Periodontitis, Bioinformatic Analysis, GEO, Microarray

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