AI Summary & Key Takeaways
AI InsightAs protein-protein interactions may differ in vivo, further validation of the presented data is needed.
📌 3 Key Takeaways
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 (GSE156993) of Type 2 Diabetes Mellitus (T2DM) and Periodontitis (P) patients was selected from the Gene Expression Omnibus (GEO) database. Study groups were defined as follows; T2DM-poor (HbA1c≥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 (log2FC≥0 or ≤0). Kyoto Encyclopedia of Genes and Genomes (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.