Comprehensive Analysis of Immune Characteristics of Fluorosis and Cuprotosis-Related Genes in Fluorosis Targeted Drugs

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.