Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA - Li, Linrong; Li, Lin; Liu, Mohan; Li, Yan; Sun, Qiang (Frontiers in Genetics, 2023)
This study aimed to construct an immune-related prognostic model and nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. 1053 breast cancer samples from the Cancer Genome Atlas (TCGA) database were assigned into high and low immune cell infiltration clusters by single sample gene set enrichment analysis. The R packages “GSVA”, “hclust”, “estimate”, “CIBERSORT” and GSEA software were used in cluster construction and validation. Next, 42 prognostic immune-related genes were identified by Immport, univariate Cox regression analysis and Venn analysis. TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6-57, CXCL1, IGHD, AIMP1, and CXCL13 were eventually selected for model construction by least absolute shrinkage and selection operator regression analysis. Kaplan-Meier curves of both train and validation sets indicated that patients in the low-risk group had better OS than the high-risk group (p < 0.05). The areas under curves (AUCs) of the model at 1, 3, and 5 years were 0.697, 0.710, 0.675 for the train set, and 0.930, 0.688, 0.712 for the validation set, respectively. Effective differentiation was achieved between high- and low-risk groups regarding clinicopathological characteristics, genes related to breast cancer, and tumor mutational burden. Finally, a nomogram integrating the risk model and clinicopathological factors was constructed with the “rms” package of R software. The 1-, 3-, and 5-year AUCs of the nomogram were 0.828, 0.783, and 0.751, respectively. In all, our study built an immune-related model and nomogram that could reliably predict OS for patients with breast cancer, and offered new perspectives in tumor immune and pathological mechanisms.