| Breast cancer is the most common invasive cancer in women,and it also has the highest incidence and mortality rate.Despite significant improvements in breast cancer diagnosis and treatment,morbidity and mortality rates remain high.Assessing the patient's prognosis accurately will provide patients with appropriate treatment choices,lowering patient mortality.Previous research has shown that immune-related genes have a high potential for prognostic markers in various cancers.Single-cell RNA-seq technology could investigate cell types associated with tumorigenesis and metastasis in breast cancer.As a result,we are attempting to use the single-cell RNA-seq methods to create prognostic markers of immune-related genes to predict breast cancer patient's prognostication.To accurately understand the immune-related genes related to breast cancer,select the ENA-SRP067248 primary breast cancer single-cell data study in the Single Cell Expression Atlas database.Analyze single-cell data through Seurat,and obtain six cell types through data quality control and standardization,dimensionality reduction and clustering,and cell-type identification analysis.Through the analysis of epithelial cells to study the heterogeneity of breast cancer,while the analysis of immune cells to research breast cancer immune-related genes.Screening for immune-related genes,the immune-related genes in the Imm Port database were obtained and crossed with the marker genes obtained by immune cell analysis,and 305 immune-related marker genes were obtained.After that,we constructed immune prognostic markers by collecting the gene expression profiles and clinical information of 3310 primary breast cancer patients from the TCGA and METABRIC databases.We then used TCGA and METABRIC breast cancer samples as training and verification sets,respectively.Simultaneously,we chosed genes with TCGA and METABRIC median absolute deviations more significant than 0.5 to reduce the downstream study's interference and measurement.We then intersected with the acquired 305 immune-related marker genes to identify 120 highly variable genes.Immune-related gene pairs were generated using the relative expression levels of 120 immune-related genes from the training and validation sets,7140 immune-related gene pairs with high were formed.We looked for immune-related gene pairs that are associated with prognosis by using survival analysis.To prevent over-fitting in the construction of immune prognostic markers,we refined the prognosis model by using Lasso regression analysis and eventually describing the prognostic model,which constitutes 17 immune-related gene pairs.We then stratified the risk of breast cancer patients in the training set and the validation set according to the optimal cut-off value of the training set time-dependent ROC curve in 3 years.The results show that breast cancer patient's prognosis in the high and low-risk groups is significantly differentiated in different data sets.To confirm that the model could be an independent factor for breast cancer prognosis with univariate and multivariate Cox proportional hazards analysis.By comparing immune infiltration outcomes in different risk classes,we discovered that M0 macrophages and M2 macrophages could encourage breast cancer incidence and growth.In contrast,M1 macrophages and cytotoxic T cells exhibit antitumor immunity and inhibit breast cancer progression.GSEA research was used to investigate the possible biological importance of various risk classes.The findings revealed that the chemokine signaling pathway,cytokine and cytokine receptor interaction,JAK-STAT signaling pathway,T cell receptor signaling pathway,and natural killer cell signaling pathway were all involved in inhibiting tumor development.Cell-mediated cytotoxicity was substantially more prevalent in the low-risk community.In this paper,we have successfully constructed immune markers that can predict breast cancer prognosis,providing new insights for the diagnosis of breast cancer. |
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