Abstract：
OBJECTIVE: This study aims to investigate and predict the therapeutic agents associated with disulfidptosis in periodontitis.
METHODS: 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.
METHODS: 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.
摘要：
目的：本研究旨在研究和预测牙周炎中与二硫键下垂相关的治疗药物。
方法：从基因表达综合（GEO）数据库下载数据集GSE10334，并用于训练最小绝对收缩和选择算子（LASSO）回归和支持向量机递归特征消除（SVM-RFE）算法，以识别与牙周炎中的二硫键下垂相关的基因。GSE16134验证集，聚合酶链反应（PCR），和牙龈免疫荧光对结果进行验证。进行单基因基因集富集分析（GSEA）以探索表征基因的潜在机制和功能。进行免疫浸润和相关性分析，并构建了竞争内源性RNA(ceRNA)网络。然后使用DGIdb数据库预测有效的治疗药物，和分子对接用于验证结合亲和力。
结果：六个基因（SLC7A11，SLC3A2，RPN1，NCKAP1，LRPPRC，和NDUFS1）与牙周炎中的二硫键下垂相关。来自外部数据集和实验的验证结果与筛选结果一致。单基因GSEA分析主要针对抗原呈递和免疫相关途径和功能进行富集。免疫浸润和相关性分析揭示了这些基因和浆细胞之间的显著调控关系，静息树突状细胞，和激活的NK细胞。将ceRNA网络可视化。还有ME-344NV-128和Riluzole,对目标基因有很好的亲和力，被确定为治疗牙周炎的有希望的药物。
结论：SLC7A11,SLC3A2,RPN1,NCKAP1,LRPPRC,和NDUFS1是与牙周炎中的二硫键下垂相关的目标，和ME-344，NV-128和RILUZOLE是治疗牙周炎的有前途的药物。
方法：从基因表达综合（GEO）数据库下载数据集GSE10334，并用于训练最小绝对收缩和选择算子（LASSO）回归和支持向量机递归特征消除（SVM-RFE）算法，以识别与牙周炎中的二硫键下垂相关的基因。GSE16134验证集，聚合酶链反应（PCR），和牙龈免疫荧光对结果进行验证。进行单基因基因集富集分析（GSEA）以探索表征基因的潜在机制和功能。进行免疫浸润和相关性分析，并构建了竞争内源性RNA(ceRNA)网络。然后使用DGIdb数据库预测有效的治疗药物，和分子对接用于验证结合亲和力。
结果：六个基因（SLC7A11，SLC3A2，RPN1，NCKAP1，LRPPRC，和NDUFS1）与牙周炎中的二硫键下垂相关。来自外部数据集和实验的验证结果与筛选结果一致。单基因GSEA分析主要针对抗原呈递和免疫相关途径和功能进行富集。免疫浸润和相关性分析揭示了这些基因和浆细胞之间的显著调控关系，静息树突状细胞，和激活的NK细胞。将ceRNA网络可视化。还有ME-344NV-128和Riluzole,对目标基因有很好的亲和力，被确定为治疗牙周炎的有希望的药物。
结论：SLC7A11,SLC3A2,RPN1,NCKAP1,LRPPRC,和NDUFS1是与牙周炎中的二硫键下垂相关的目标，和ME-344，NV-128和RILUZOLE是治疗牙周炎的有前途的药物。
