A Cuproptosis-Related MiRNAs Signature Predicts Prognosis And Immune Microenvironment For Breast Cancer

Background and Purpose of the Study:Breast cancer remains the most common cancer and the leading cause of cancer death among women worldwide.Developing biomarkers that aid in the diagnosis,prognosis,and prediction of breast cancer is crucial for timely identification and appropriate management of the disease throughout the treatment process.This study is based on the construction of a breast cancer prognosis model using cuproptosis-related microRNAs(miRNAs)to predict patient prognosis and explore its relationship with the immune microenvironment of breast cancer.Methods:The complete miRNA expression profile data,RNA-seq expression data,and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas(TCGA).A Perl language script was used to process and obtain the corresponding miRNA expression matrix,and cuproptosis-related miRNAs were selected by calculating the Pearson correlation with 19 cuproptosis-related gene sets.Differential expression analysis was then performed to obtain cuproptosis-related differentially expressed miRNAs(DEMs).Similarly,clinical information data was extracted and merged for survival analysis using single-factor Cox analysis to determine DEMs associated with overall survival(OS)of breast cancer patients.Then,multiple-factor Cox analysis was performed to construct a breast cancer cuproptosis-related miRNAs prediction model using the initially screened prognostic-related genes.Patients were divided into high-risk and low-risk groups based on the median value of the risk score,and survival differences were analyzed.The prediction performance of the model was evaluated using Kaplan-Meier curves,receiver operating characteristic(ROC)curves at 1 year,3 years,and 5 years,and risk scoring.Additionally,single-factor and multiplefactor Cox analysis were also used to determine whether the risk score could be a prognostic factor for breast cancer patients.Furthermore,to explore whether cuproptosis-related miRNAs may be involved in the progression of breast cancer,Perl language scripts were used to process breast cancer miRNAs target gene data downloaded from TargetScan,miRDB,and miRTarBase websites,intersected with differentially expressed genes,and subjected to biological pathway functional risk and enrichment analysis.The differences in the tumor microenvironment between the high-risk and low-risk groups were also evaluated by performing differential analysis of immune cells,immune function,and immune checkpoints for each sample.Result:The study identified a total of 121 differentially expressed miRNAs(92 upregulated and 29 downregulated)associated with cuproptosis in breast cancer patients,as well as 5,882 differentially expressed mRNAs.These DEMs were merged with clinical information data and analyzed using single and multiple Cox regression analysis to identify 11 cuproptosis-related miRNAs(hsa-mir-592,hsa-mir-4501,hsa-mir-7974,hsa-mir-549a,hsa-mir-4675,hsa-mir4658,hsa-mir-618,hsa-mir-1293,hsa-mir-466,hsa-mir-4533,hsa-mir-3923)that were significantly associated with overall survival(OS)in breast cancer patients.These miRNAs were used to construct a prediction model,and patients were divided into high-risk and lowrisk groups based on the risk score calculated using the median value.Survival analysis showed that patients in the high-risk group had significantly lower OS than those in the lowrisk group(P<0.05).The AUC values for the 1-year,3-year,and 5-year survival rates were 0.723,0.688,and 0.686,respectively,indicating good predictive efficacy of the model.Independent prognostic analysis indicated that the 11 miRNAs in the model were independent prognostic factors for breast cancer.Gene enrichment analysis showed that most of the target genes were involved in cancer-related pathways.Furthermore,analysis of immune cells,immune function,and immune checkpoints in high-and low-risk groups indicated that patients in the low-risk group were more likely to benefit from immune therapy.Conclusion:The 11 cuproptosis-related miRNAs(hsa-mir-592,hsa-mir-4501,hsa-mir-7974,hsa-mir-549a,hsa-mir-4675,hsa-mir-4658,hsa-mir-618,hsa-mir-1293,hsa-mir-466,hsa-mir4533,hsa-mir-3923)are potential prognostic biomarkers for breast cancer patients and can predict patient prognosis well.A risk prognostic model based on this can effectively predict patient prognosis,and patients in the low-risk group are more likely to benefit from immunotherapy.This provides important reference value for the prognosis assessment of breast cancer patients and targeted immunotherapy.