Identification of key genes for secondary stroke in patients with periodontitis

Background: The immune system plays a particularly important role in the pathogenesis of both stroke and periodontitis. The aim of this study is to identify key diagnostic candidate genes for stroke in patients with periodontitis. Methods: We searched for a periodontitis dataset and a stroke dataset in the Gene Expression Omnibus (GEO) database. Limma, Weighted Gene Co-expression Network Analysis (WGCNA), immune analysis, single-cell sequencing, and machine learning algorithms were used to identify and analyze immune-related genes associated with periodontitis and stroke. Finally, a nomogram and receiver operating characteristic (ROC) curves were used for evaluation. Results: Limma analysis produced 4252 differentially expressed genes (DEGs) from the integrated stroke dataset and 4113 DEGs from the integrated periodontitis dataset. The periodontitis dataset generated the most relevant module after WGCNA, containing 2028 genes, with a total of 169 intersecting genes among the three datasets. Enrichment analysis revealed that most immune-related genes were enriched, and immune infiltration analysis showed an imbalance of various immune cells. Single-cell sequencing was used to screen immune cells. Further machine learning screening produced three core genes, which were evaluated for their diagnostic value using a nomogram and ROC curves, showing high diagnostic value. Conclusion: Three immune-related core genes (SIGIRR, PLCL2, GLRX) were identified, and a nomogram for diagnosing periodontitis and stroke was established. This study may help identify potential peripheral blood diagnostic candidate genes for secondary stroke in patients with periodontitis.