| Objective(s): Copper metabolism maintains important physiological functions of organism,and aberrant copper metabolism promotes tumorigenesis and development by activating tumor proliferation related signal pathways,regulating tumor angiogenesis,remodeling matrix and inflammatory microenvironment Therefore,it is of great significance to construct a risk signature related to copper metabolism to predict the recurrence and treatment response of PCa.Methods: 1.First,three prostate cancer datasets and their corresponding clinical data were downloaded from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)database,conduct gene difference analysis on all samples,and then intersect with the copper metabolism gene set to obtain the differentially expressed copper metabolism genes in PCa,and then use these genes for GO and KEGG function enrichment analysis.2.Through the use of univariate,LASSO and multivariate COX regression analysis,the copper metabolism genes that have a greater impact on the progression-free survival(PFS)of PCa patients were identified to construct the risk signature.3.The risk score of all samples was calculated using the constructed risk signature related to copper metabolism,and all samples were divided into high and low risk groups by the median of the risk score.Then K-M survival analysis and receiver operating characteristic curve(ROC)were performed to verify the accuracy and specificity of the risk signature.4.Next,all samples were grouped by common clinical characteristics(age,TNM stage,Gleason score,etc.)to evaluate the correlation between clinical characteristics and risk score and verify the application of risk signature in each subgroup.5.Gene set enrichment analysis(GSEA)was used to analyze the molecular mechanism and gene mutation differences between high-risk and low-risk groups.6.The infiltration abundance and immune activity of 22 kinds of immune cells in all samples were analyzed by "CIBERSORT" algorithm and single sample gene set enrichment analysis(ss GSEA).7.Use q RT-PCR to measure the relative expression of copper metabolism genes in the copper metabolism risk signature in clinical samples,and use Human Protein Atlas(HPA)to measure the protein expression level of these key genes in tissues.Results: 1.2628 differential expressed genes were obtained from the prostate cancer datasets of TCGA and GEO database,and 55 differential genes of copper metabolism were obtained after intersecting with copper metabolism gene set.The functional enrichment analysis found that these genes were mainly related to fatty acid and glutathione metabolism.2.Six copper metabolism genes(EZH2,PIK3R1,LIX1,GLYATL1 and NEK5)were identified by univariate,LASSO and multivariate COX regression analysis to construct a copper metabolism risk signature.3.K-M survival analysis found that the risk signature has the ability to predict the prognosis of high and low risk groups,and the ROC curve also verified that the risk signature has good specificity and sensitivity.4.Higher risk score was associated with higher T stage,Gleason Score,nodal metastasis and distant metastasis in PCa patients,and the risk signature also displayed good predictive efficacy in each subgroup.5.The molecular mechanism of the high-risk group is mainly enriched in the pathway related to cell proliferation,and the gene mutation abundance is higher than that of the low-risk group.6.Compared with the low risk group,the high risk group has a higher proportion of infiltration of immunosuppressive cells and decreased immune activity.7.q RT-PCR and HPA verified that the key genes in the copper metabolism risk signature were significantly different in prostate cancer tissue and normal prostate tissue.Conclusion(s): Our risk signature related to copper metabolism can be used as a clinical feature to accurately evaluate the prognosis,and the immune cell infiltration and immune activity of patients with different risk groups are significantly different,which can guide patients with PCa to choose appropriate treatment. |
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