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

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 methods Expression data (GSE156993) Type 2 Diabetes Mellitus (T2DM) (P) patients was selected from the Gene Omnibus (GEO) database. Study groups were defined follows; T2DM-poor (HbA1c≥8.5%, n=7), T2DM-well (HbA1c<7.0%, n=7) P (n=6). differentially expressed genes (DEGs) between analyzed GEO2R (log2FC≥0 or ≤0). Kyoto Encyclopedia Genes Genomes (KEGG) used identification biological pathways. Protein network constructed in STRING database hub detected. Data validation performed ELISA assay two genes. Significance set P<0.05. Results 1008 upregulated, while 610 down-regulated group compared controls. KEGG analysis revealed highest number clustered cancer pathways PI3K-Akt signaling pathway, purine metabolism, parathyroid hormone cGMP-PKG pathway Rap1 pathway. For increasing decreasing expression profiles, nodes score SMAD4, HNF4A, SMARCA4 SRC, TNF, RFC2, RFC3 genes, respectively. Conclusion Bioinformatic analyses metabolomic, inflammatory altered periodontitis poorly controlled diabetes. As protein-protein interactions differ vivo, further presented needed.