Huang Jieying, Zou Yaokun, Deng Huizhi, Zha Jun, Pathak Janak Lal, Chen Yaxin, Ge Qing, Wang Liping
Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou 510182, China.
Int J Mol Sci. 2025 May 1;26(9):4306. doi: 10.3390/ijms26094306.
Peri-implantitis (PI) is a chronic inflammatory disease that ultimately leads to the dysfunction and loss of implants with established osseointegration. Ferroptosis has been implicated in the progression of PI, but its precise mechanisms remain unclear. This study explores the molecular mechanisms of ferroptosis in the pathology of PI through bioinformatics, offering new insights into its diagnosis and treatment. The microarray datasets for PI (GSE33774 and GSE106090) were retrieved from the GEO database. The differentially expressed genes (DEGs) and ferroptosis-related genes (FRGs) were intersected to obtain PI-Ferr-DEGs. Using three machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Boruta, we successfully identified the most crucial biomarkers. Additionally, these key biomarkers were validated using a verification dataset (GSE223924). Gene set enrichment analysis (GSEA) was also utilized to analyze the associated gene enrichment pathways. Moreover, immune cell infiltration analysis compared the differential immune cell profiles between PI and control samples. Also, we targeted biomarkers for drug prediction and conducted molecular docking analysis on drugs with potential development value. A total of 13 PI-Ferr-DEGs were recognized. Machine learning and validation confirmed toll-like receptor-4 (TLR4) and FMS-like tyrosine kinase 3 (FLT3) as ferroptosis biomarkers in PI. In addition, GSEA was significantly enriched by the biomarkers in the cytokine-cytokine receptor interaction and chemokine signaling pathway. Immune infiltration analysis revealed that the levels of B cells, M1 macrophages, and natural killer cells differed significantly in PI. Ibudilast and fedratinib were predicted as potential drugs for PI that target TLR4 and FLT3, respectively. Finally, the occurrence of ferroptosis and the expression of the identified key markers in gingival fibroblasts under inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in PI, unraveling potential novel targets to treat PI.
种植体周围炎（PI）是一种慢性炎症性疾病，最终会导致已建立骨整合的种植体功能障碍和丧失。铁死亡与PI的进展有关，但其确切机制仍不清楚。本研究通过生物信息学探索铁死亡在PI病理中的分子机制，为其诊断和治疗提供新的见解。从GEO数据库中检索PI的微阵列数据集（GSE33774和GSE106090）。对差异表达基因（DEGs）和铁死亡相关基因（FRGs）进行交叉分析，以获得PI-Ferr-DEGs。使用三种机器学习算法，即最小绝对收缩和选择算子（LASSO）、支持向量机递归特征消除（SVM-RFE）和Boruta，我们成功鉴定出了最关键的生物标志物。此外，使用验证数据集（GSE223924）对这些关键生物标志物进行了验证。基因集富集分析（GSEA）也用于分析相关基因富集途径。此外，免疫细胞浸润分析比较了PI与对照样本之间的差异免疫细胞谱。此外，我们针对生物标志物进行药物预测，并对具有潜在开发价值的药物进行分子对接分析。共识别出13个PI-Ferr-DEGs。机器学习和验证证实Toll样受体4（TLR4）和FMS样酪氨酸激酶3（FLT3）是PI中铁死亡的生物标志物。此外，细胞因子-细胞因子受体相互作用和趋化因子信号通路中的生物标志物使GSEA显著富集。免疫浸润分析显示，PI中B细胞、M1巨噬细胞和自然杀伤细胞的水平存在显著差异。异丁司特和非达替尼分别被预测为靶向TLR4和FLT3的PI潜在药物。最后，通过RT-qPCR和免疫荧光分析验证了炎症条件下牙龈成纤维细胞中铁死亡的发生以及所鉴定关键标志物的表达。本研究将TLR4和FLT3鉴定为PI中铁死亡和免疫细胞浸润的特征，揭示了治疗PI的潜在新靶点。
