Ba Ruijie, Liu Bin, Feng Zichen, Wang Guoqing, Niu Shu, Wang Yan, Jiao Xuecheng, Wu Cuiping, Yu Fangfang, Zhou Guoyu, Ba Yue
Department of Environmental Health, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, P. R. China.
Department of Endemic Disease, Puyang Center for Disease Control and Prevention, Puyang, 457000, Henan, China.
Biol Trace Elem Res. 2025 Jan 21. doi: 10.1007/s12011-025-04517-0.
This study aims to investigate the role of cuprotosis in fluorosis and identify potential targeted drugs for its treatment. The GSE70719 and GSE195920 datasets were merged using the inSilicoMerging package. DEGs between the exposure and control groups were found using R software. Overlapping genes of DEG and cuprotosis-related genes (CRGs) were obtained by Venn diagram and were enriched by GO and KEGG. Hub genes were identified using PPI networks and enriched by GSEA. ROC curves, the xCell algorithm, and consensus cluster analysis were utilized to evaluate diagnostic efficacy, examine immune cell infiltration, and identify cuproptosis subtypes, respectively. The GSE53937 dataset was used for external validation. The DSigDB database was used to predict small molecule drugs. Molecular docking was used to validate the relationship between small molecule drugs and hub genes. A total of 1522 DEGs (743 upregulated genes and 779 downregulated genes) and 33 overlapping genes of DEGs and CRGs were obtained. The 33 overlapping genes were enriched in ribosomal biogenesis and oxidative phosphorylation pathways. The hub genes DNTTIP2, GTPBP4, IMP4, MRPL12, MRPL13, MRPL2, MRPS2, MRPS22, NOP2, RSL1D1, and SURF6 were identified, demonstrating great diagnostic ability with AUC > 0.8. These hub genes were associated with immune response and inflammation. Two cuproptosis patterns were established based on 33 CRGs. Mepacrine was screened as a potential drug and demonstrated stability in docking with IMP4. In summary, the current study identified several CRGs that may serve as potential biomarkers for diagnosing fluorosis and are involved in fluoride-induced immune responses. Additionally, mepacrine was screened as a potential treatment for fluorosis by targeting CRGs.
本研究旨在探讨铜死亡在氟中毒中的作用，并确定其潜在的靶向治疗药物。使用inSilicoMerging软件包合并GSE70719和GSE195920数据集。使用R软件找出暴露组和对照组之间的差异表达基因（DEG）。通过韦恩图获得DEG与铜死亡相关基因（CRG）的重叠基因，并通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）进行富集分析。利用蛋白质-蛋白质相互作用（PPI）网络鉴定枢纽基因，并通过基因集富集分析（GSEA）进行富集。分别利用ROC曲线、xCell算法和一致性聚类分析评估诊断效能、检测免疫细胞浸润和识别铜死亡亚型。使用GSE53937数据集进行外部验证。利用DSigDB数据库预测小分子药物。通过分子对接验证小分子药物与枢纽基因之间的关系。共获得1522个DEG（743个上调基因和779个下调基因）以及33个DEG与CRG的重叠基因。这33个重叠基因在核糖体生物合成和氧化磷酸化途径中富集。鉴定出枢纽基因DNTTIP2、GTPBP4、IMP4、MRPL12、MRPL13、MRPL2、MRPS2、MRPS22、NOP2、RSL1D1和SURF6，其曲线下面积（AUC）>0.8，显示出良好的诊断能力。这些枢纽基因与免疫反应和炎症相关。基于33个CRG建立了两种铜死亡模式。筛选出米帕林作为潜在药物，其与IMP4对接时显示出稳定性。总之，本研究确定了几个可能作为氟中毒诊断潜在生物标志物的CRG，它们参与了氟诱导的免疫反应。此外，通过靶向CRG筛选出米帕林作为氟中毒的潜在治疗药物。
