Construction And Analysis Of Breast Cancer Prognostic Model Based On Cuproptosis And MYC-related Genes

Background:Breast cancer is the most commonly diagnosed cancer in women worldwide and poses a serious threat to women’s health.Cuproptosis,a newly discovered regulated form of cell death,involves the direct binding of copper ions to the lipid components of the tricarboxylic acid(TCA)cycle inside the cell,leading to the aggregation of acylated proteins and loss of iron-sulfur cluster proteins,inducing protein toxicity stress and ultimately causing cell death.MYC is a common oncogenic transcription factor closely associated with various tumors.Previous studies have shown a connection between MYC and cuproptosis.However,the combined prognostic effect of cuproptosis and MYC in breast cancer remains underexplored.By using bioinformatics methods to analyze genes related to cuproptosis and MYC scores in breast cancer,constructing a prognostic model,and drawing an alignment diagram,this study provides a reference for breast cancer prognosis prediction and treatment.Methods:Based on breast cancer expression data from The Cancer Genome Atlas(TCGA)database,differential gene analysis was performed using DESeq2 to find differentially expressed genes between breast cancer and normal tissue samples.The single-sample gene set enrichment analysis(ss GSEA)was employed to score cuproptosis and MYC in the breast cancer samples.Weighted gene co-expression network analysis(WGCNA)was used to screen for genes related to cuproptosis and MYC scores,and then intersected with differentially expressed genes.The intersecting genes were then subject to Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.Subsequently,a prognostic model was constructed using univariate Cox regression,LASSO regression,and multivariate Cox regression.The external dataset GSE20685 was utilized for validation,and the accuracy and effectiveness of the prognostic model were assessed using Kaplan-Meier(KM)survival curves and Receiver Operating Characteristic(ROC)curves.The nomogram was generated by combining the prognostic model with clinical-pathological factors to assess its clinical value.Furthermore,we explored the relationship between the prognostic model and immune cell infiltration,immune checkpoints,tumor immune dysfunction and exclusion(TIDE),tumor mutational burden(TMB),and drug sensitivity.Results:By performing WGCNA analysis,we identified 595 genes that were associated with Cuproptosis and MYC scores.After intersection analysis with the differentially expressed genes,a total of 214 intersecting genes were obtained.Gene enrichment analysis revealed that these intersecting genes were mainly enriched in cell division,chromosome separation,mitosis,and the cell cycle process.Then,univariate Cox regression,LASSO regression,and multivariate Cox regression were performed on the TCGA training set,and a breast cancer prognostic model based on four genes(FABP6,GABRQ,NTRK2,SLC1A1)was constructed.Based on the median risk score of the prognostic model,samples were divided into high-risk and low-risk groups.Kaplan-Meier survival curves showed that the overall survival rate of the high-risk group in the TCGA training set was lower,which was also observed in the TCGA validation set and external validation set(GSE20685).ROC curves indicated that the AUC for 1 year,3 years,5 years,and 10 years in the TCGA training set were 0.710,0.622,0.640,and 0.677,respectively.Similar results were also observed in the TCGA validation set and the GEO external validation set,indicating that the prognostic model had good accuracy.A nomogram combining clinical-pathological data in this study had an AUC of 0.836,0.754,0.746,and 0.696 for1 year,3 years,5 years,and 10 years,respectively,indicating that the model has high clinical value.Additionally,our findings suggested differences between high-risk and low-risk groups in terms of immune microenvironment,TIDE,TMB,and sensitivity to anti-cancer chemotherapy.Conclusion:In conclusion,we have established a breast cancer prognostic model based on four genes(FABP6,GABRQ,NTRK2,SLC1A1)and constructed a nomogram diagram that can accurately predict breast cancer prognosis.Our findings also revealed differences in immune microenvironment and drug sensitivity between high-risk and low-risk groups.These findings have the potential to facilitate prognostication and treatment strategies for breast cancer patients,while also providing novel avenues for future research.