Background: Immune and inflammatory responses are important in the occurrence and development of periodontitis. The aim of this study was to screen for immune-related genes and construct a disease diagnostic model to further investigate the underlying molecular mechanisms of periodontitis.
Methods: GSE16134 and GSE10334 datasets were used in this study. Differentially expressed genes (DEGs) between the periodontitis and control groups were selected. Immune-related genes were identified, and functional analysis and construction of an interaction network were conducted. Immune characteristics were evaluated using gene set variation analysis GSVA. Immunity-related modules were analyzed using weighted gene co-expression network analysis (WGCNA). The LASSO algorithm was applied to optimize the module genes. Correlation between optimized immune-related DEGs and immune cells was analyzed.
Results: A total of 324 immune-related DEGs enriched in immune- and inflammation-related functions and pathways were identified. Of which, 23 immune cells were significantly different between the periodontitis and control groups. Nine optimal immune-related genes were selected using the WGCNA and LASSO algorithms to construct a diagnostic model. Except for CXCL1, the other eight genes were significantly positively correlated with regulatory T cells, immature B cells, activated B cells, and myeloid-derived suppressor cells.
Conclusion: This study identified nine immune-related genes and developed a diagnostic model for periodontitis.
Keywords: Diagnostic model; Immune; Periodontitis; WGCNA.
© 2023. The Author(s).