Integrating GEO Database, Mendelian Randomization, and Molecular Docking to Identify HLA-C as a Potential Therapeutic...

Background: Periodontitis is a chronic inflammatory disease that leads to the destruction of periodontal tissues, ultimately resulting in tooth loss. Current treatments primarily focus on mitigating inflammation and alleviating symptoms, but they often lack specificity. This study aims to explore the molecular mechanisms of periodontitis using gene expression databases (GEOs) and bioinformatics methods, combined with Mendelian randomization (MR) analysis, to identify key therapeutic targets. Methods: This study analyzed GSE10334 microarray data to identify differentially expressed genes (DEGs) in periodontitis using R. Weighted gene co-expression network analysis (WGCNA) identified key gene modules and enrichment analysis revealed functional pathways. Immune infiltration was assessed with CIBERSORT and MR explored human leukocyte antigen C's (HLA-C's) role. Single-cell analysis using Seurat identified cell types and CellChat mapped cell communication. Molecular docking (MD) and molecular dynamic simulations were used to validate the interaction between the hub target genes and the potential drug. Results: Differential expression analysis identified 167 DEGs in periodontitis. WGCNA revealed a strong association with the blue module. MR analysis confirmed HLA-C as a risk factor. Single-cell RNA sequencing (scRNA-seq) showed elevated plasmablasts and HLA-C expression. MD and molecular dynamic simulation analysis identified metronidazole as a potential drug with stable binding to HLA-C, forming a stable complex with no significant conformational changes during the 100 ns simulation period. Conclusion: This study identifies HLA-C as a potential therapeutic target for periodontitis, with MD studies and molecular dynamic simulations highlighting metronidazole as a potential treatment. These findings provide new insights into periodontitis pathogenesis and potential therapeutic strategies.