Exploring a novel risk model based on core disulfidptosis‐related genes in periodontitis: Bioinformatics analyses and experimental validation DOI Open Access

Yiqiang Yang,

Qi Liu,

Xun Lu

et al.

The FASEB Journal, Journal Year: 2025, Volume and Issue: 39(3)

Published: Feb. 9, 2025

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

Language: Английский

Exploring a novel risk model based on core disulfidptosis‐related genes in periodontitis: Bioinformatics analyses and experimental validation DOI Open Access

Yiqiang Yang,

Qi Liu,

Xun Lu

et al.

The FASEB Journal, Journal Year: 2025, Volume and Issue: 39(3)

Published: Feb. 9, 2025

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

Language: Английский

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