Correlation of disulfidptosis and periodontitis: New insights and clinical significance

Abstract

Objectives

This study aims to investigate and predict the therapeutic agents associated with disulfidptosis in periodontitis.

Design

The dataset GSE10334 was downloaded from the Gene Expression Omnibus (GEO) database and used to train a least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM–RFE) algorithm to identify genes associated with disulfidptosis in periodontitis. GSE16134 validation sets, polymerase chain reaction (PCR), and gingival immunofluorescence were used to verify the results.Single-gene Gene Set Enrichment Analysis (GSEA) was performed to explore the potential mechanisms and functions of the characterized genes. Immune infiltration and correlation analyses were performed, and competing endogenous RNA (ceRNA) networks were constructed. Effective therapeutic drugs were then predicted using the DGIdb database, and molecular docking was used to validate binding affinity.

Results

Six genes (SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1) associated with disulfidptosis in periodontitis were obtained. Validation results from external datasets and experiments were consistent with the screening results. Single-gene GSEA analysis was mainly enriched for antigen presentation and immune-related pathways and functions.Immune infiltration and correlation analyses revealed significant regulatory relationships between these genes and plasma cells, resting dendritic cell, and activated NK cells. The ceRNA network was visualized. And ME-344, NV-128, and RILUZOLE, which have good affinity to target genes, were identified as promising agents for the treatment of periodontitis.

Conclusions

SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1 are targets associated with disulfidptosis in periodontitis, and ME-344, NV-128, and RILUZOLE are promising agents for the treatment of periodontitis.