Lipid Metabolism And Nets Genes In Periodontitis: Mechanisms

To comprehensively elucidate the interplay between lipid metabolism-associated genes (LMAGs) and neutrophil extracellular trap (NETs)-related genes (NRGs) in periodontitis (PD). Based on the PD dataset GSE16134, differentially expressed genes (DEGs) were screened, followed by protein-protein interaction (PPI) network construction and functional enrichment analysis (COOKEGC) to identify core genes. Candidate targets were prioritized by integrating LMAGs and NRGs. Feature genes were optimized using LASSO and SVM-RFE algorithms. Subsequent analyses included immune infiltration, gene set enrichment analysis (GSEA), molecular network modeling, and drug prediction. Differential expression analysis identified 1,634 DEGs (983 upregulated, 651 downregulated) Elevated NETs-RG scores guided WGCNA, revealing two co-expression modules (brown: 312 genes, blue: 287 genes). Stringent filtering (gene significance > 0.2, module membership > 0.8) identified 89 NRGs, which intersected with DEGs and LMAGs to yield 15 high-confidence candidates. Machine learning refined these to five hub genes (INPP5D, CYP2C9, AKR1B1, NEU1, PIP5K1B), validated by ROC analysis (AUC > 0.85). Immune correlation analysis demonstrated PIP5K1B’s strong positive association and CYP2C9’s negative correlation with plasma cell infiltration. Five biomarkers (INPP5D, CYP2C9, AKR1B1, NEU1, PIP5K1B) linking LMAGs-NRGs crosstalk in PD were validated (LASSO/SVM-RFE/ROC), with PIP5K1B/CYP2C9 showing immune correlations, proposing diagnostic/therapeutic targets.