Co-expression network and survival analysis of breast cancer inflammation and immune system hallmark genes

Abstract

The tertiary lymphoid structure (TLS) plays a central role in cancer immune response, and its gene expression pattern, called the TLS signature, has shown prognostic value in breast cancer. The formation of TLS and tumor-associated high endothelial venules (TA-HEVs), responsible for lymphocytic infiltration within the TLS, is associated with the expression of cancer hallmark genes (CHGs) related to immunity and inflammation. In this study, we performed co-expression network analysis of immune- and inflammation-related CHGs to identify predictive genes for breast cancer. In total, 382 immune- and inflammation-related CHGs with high expression variance were extracted from the GSE86166 microarray dataset of patients with breast cancer. CHGs were classified into five modules by applying weighted gene co-expression network analysis. The survival analysis results for each module showed that one module comprising 45 genes was statistically significant for relapse-free and overall survival. Four network properties identified key genes in this module with high prognostic prediction abilities: CD34, CXCL12, F2RL2, JAM2, PROS1, RAPGEF3, and SELP. The prognostic accuracy of the seven genes in breast cancer was synergistic and exceeded that of other predictors in both small and large public datasets. Enrichment analysis predicted that these genes had functions related to leukocyte infiltration of TA-HEVs. There was a positive correlation between key gene expression and the TLS signature, suggesting that gene expression levels are associated with TLS density. Co-expression network analysis of inflammation- and immune-related CHGs allowed us to identify genes that share a standard function in cancer immunity and have a high prognostic predictive value. This analytical approach may contribute to the identification of prognostic genes in TLS.