Exploring a novel risk model based on core disulfidptosis-related genes in periodontitis: Bioinformatics analyses and...

Bacteria in dental plaque invade periodontal tissues, causing chronic inflammation known as periodontitis. Despite advancements in understanding periodontitis, its molecular pathogenesis remains incompletely elucidated. In this study, a total of 247 samples were retrieved from the Gene Expression Omnibus (GEO) database, comprising 183 from individuals with periodontitis and 64 from healthy controls. Differentially expressed DRGs (DE-DRGs) were identified, and their expression correlations were analyzed. Immune cell infiltration and its association with DE-DRGs were assessed. Gene Set Variation Analysis (GSVA) was performed to determine key functions and pathways related to DE-DRGs. Characteristic DE-DRGs (CDE-DRGs) were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and a risk model and personalized nomogram were constructed. Model performance was validated through calibration and decision curve analysis (DCA). External experiments, including qRT-PCR and Western blot, confirmed the differential expression of DE-DRGs. Fourteen DE-DRGs were identified. Expression analysis revealed a strong synergistic correlation between MYH9 and ACTB (coefficient = 0.86) and an antagonistic correlation between NCKAP1 and FLNA (coefficient = -0.52). Immune profiling showed significant differences in the proportions of 22 immune cell types between groups, with 14 DE-DRGs correlated with immune infiltration levels. Cluster analysis of periodontitis samples revealed distinct patterns of DE-DRGs expression and immune cell infiltration across two clusters. A risk model incorporating four CDE-DRGs (DSTN, SLC7A11, SLC3A2, and RPN1) was developed, alongside a personalized nomogram for predicting periodontitis risk. qRT-PCR and Western blot analyses demonstrated downregulation of DSTN, SLC3A2, IQGAP1, CD2AP, and NCKAP1 and upregulation of SLC7A11, RPN1, FLNA, MYH9, TLN1, ACTB, MYH10, CAPZB, and PDLIM1 in periodontitis tissues. This study identified key DRGs in periodontitis, developed a predictive risk model and nomogram, and detailed the immune infiltration profile and its association with DRGs. These findings provide insights into the molecular pathogenesis of periodontitis and suggest potential strategies for personalized risk assessment, early diagnosis, and targeted therapy.