Akademik Veri Yönetim Sistemi
European annals of dental sciences (Online), cilt.49, sa.3, ss.101-107, 2022 (Hakemli Dergi)
Purpose: Periodontitis and diabetes are highly prevalent chronic diseases associated with upregulated inŕammation that may adversely affect each other. The aim of this study was to determine underlying molecular mechanisms via bioinformatic tools as a guide for future studies. Materials and Methods: Expression data (GSE156993) of Type 2 Diabetes Mellitus (T2DM) and Periodontitis (P) patients was selected from the Gene Expression Omnibus (GEO) database. Study groups were deőned as follows; T2DM-poor (HbA1≥8.5%, n=7), T2DM-well (HbA1c<7.0%, n=7) and P (n=6). The differentially expressed genes (DEGs) between groups were analyzed with GEO2R software (log2FC≥0 or ≤0). Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for the identiőcation 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. Signiőcance was set to P<0.05. Results: 1008 genes were upregulated, while 610 genes were downregulated in T2DM-poor group compared to the controls. KEGG analysis revealed that the highest number of downregulated genes were clustered in cancer pathways and PI3K/Akt signaling pathway, as upregulated genes were clustered in purine metabolism, parathyroid hormone metabolism, cGMP/PKG signaling pathway and Rap1 signaling pathway. For increasing and decreasing expression proőles, hub nodes with the highest score were selected as SMAD4, HNF4A, SMARCA4 and SRC, TNF, RFC2, RFC3 genes, respectively. Conclusions: Bioinformatic analyses revealed that metabolomic, inŕammatory 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.