Identification of hypoxia-immune-related signatures for predicting immune efficacy in triple-negative breast cancer

Abstract Objectives The therapeutic effect against triple-negative breast cancer (TNBC) varies among individuals. Finding signatures to predict immune efficacy is particularly urgent. Considering the connection between the microenvironment and hypoxia, hypoxia-related signatures could be more effective. Therefore, in this study, we aimed sought to construct a hypoxia-immune-related prediction model for breast cancer and identify therapeutic targets. Methods Immune and hypoxia status in the TNBC microenvironment were investigated using single-sample Gene Set Enrichment Analysis (ssGSEA) and Uniform Manifold Approximation and Projection (UMAP). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were employed to build a prognostic model based on hypoxia-immune-related differentially expressed genes. The Cancer Genome Atlas (TCGA) cohort, real-time quantitative polymerase chain reaction (qRT-PCR), and immunofluorescence staining were utilized to analyze the expression differences. Tumor immune dysfunction and exclusion indexes were used to indicate the effect of immunotherapy. Results We identified 11 signatures related to hypoxia and immunity. Among these genes, C-X-C motif chemokine ligand (CXCL) 9, 10, and 11 were up-regulated in TNBC tissues compared to normal tissues. Furthermore, CXCL9, 10, 11, and 13 were found to enhance the effect of immunotherapy. Conclusions These findings suggest the value of the hypoxia-immune-related prognostic model for estimating the risk in patients with TNBC, and CXCL9, 10, 11, and 13 are potential targets to overcome immune resistance in TNBC.