Abstract
Breast cancer represents a substantial contributor to mortality rates among women with cancer. Chemical dynamic therapy is a promising anticancer strategy that utilizes the Fenton reaction to transform naturally occurring hydrogen peroxide (H2O2) into hydroxyl radicals (•OH). Additionally, cancer immunotherapy using immune drugs, such as imiquimod (R837), has shown promise in activating T cells to kill tumor cells. In this study, we proposed a Fe3O4@R837 smart nanoplatform that can trigger the Fenton reaction and induce immune responses in breast cancer treatment. Furthermore, we performed transcriptome sequencing on breast cancer samples and used the R package (limma) to analyze differential expression profiles and select differentially expressed genes (DEGs). We obtained clinical information and RNA expression matrix data from The Cancer Genome Atlas database to perform survival analysis and identify prognostic-related genes (PRGs) and molecular subtypes with distinct prognoses. We used the TIMER 2.0 web and other methods to determine the tumor immune microenvironment and immune status of different prognostic subtypes. We identified DPGs by taking the intersection of DEGs and PRGs and performed functional analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, to elucidate potential mechanisms. Subsequently, we constructed a protein-protein interaction network using the STRING database to visualize the interactions between the DPGs. We screened hub genes from the DPGs using the Cytoscape plugin and identified six hub genes: CD3E, GZMK, CD27, SH2D1A, ZAP70, and TIGIT. Our results indicate that these six key genes regulate immune cell recruitment to increase T-cell cytotoxicity and kill tumors. Targeting these key genes can enhance immunotherapy and improve the breast cancer prognosis.