Comprehensive Exploration of Tumor Microenvironment Modulation Based on the ESTIMATE Algorithm in Bladder Urothelial Carcinoma Microenvironment

Abstract

Recently, the tumor microenvironment (TME) has been reported to be closely related to the tumor initiation, progression, and prognosis. Bladder urothelial carcinoma (BLCA), one of the most common subtypes of bladder cancer worldwide, has been associated with increased morbidity and mortality in the past decade. However, whether the TME status of BLCA contributes to the prediction of BLCA prognosis still remains uncertain. In this study, the ESTIMATE algorithms were used to estimate the division of immune and stromal components in 406 BLCA samples downloaded from The Cancer Genome Atlas database (TCGA). Based on the comparison between ESTIMATE scores, the differentially expressed genes (DEGs) were selected. Using the univariate Cox regression analysis, prognosis-related DEGs were further identified (p < 0.05). The LASSO regression analysis was then used to screen 11 genes that were highly related to the TME of BLCA to generate a novel prognostic gene signature. The following survival analyses showed that this signature could effectively predict the prognosis of BLCA. The clinical value of this signature was further verified in an external cohort obtained from the First Affiliated Hospital of Wenzhou Medical University (n = 120). Based on the stage-correlation analysis and differential expression analysis, IGF1 and MMP9 were identified as the hub genes in the signature. Additionally, using CIBERSORT algorithms, we found that both IGF1 and MMP9 were significantly associated with immune infiltration. Collectively, a novel TME-related prognostic signature contributes to accurately predict the prognosis of BLCA.