Bioinformatics Analysis to Screen the Key Prognostic Genes in Tumor Microenvironment of Bladder Cancer.

Zhao Zhang,Zhao Liu,Dongshan Chen,Lei Yan,Zhonghua Xu,Zeyan Li

doi:10.1155/2020/6034670

Abstract

Bladder cancer (BLCA) is the fifth most common cancer and has the features of low survival rate and high morbidity and mortality. The Cancer Genome Atlas (TCGA) is a pool of global gene expression profile and contains huge amounts of cancer genomics data, which makes it possible to inquire the relationship between gene expression and prognosis of a series of malignant tumors including BLCA. Immune and stromal cells are two major components of tumor microenvironment (TME) which play an important role in judging the prognosis of tumor and influencing the progression of malignant, inflammatory, and metabolic disorders. In our study, we conducted a quantitative analysis of immune and stromal elements based on the ESTIMATE algorithm and thus divided BLCA cases into high and low groups. Then the differentially expressed genes closely related to tumor prognosis between groups were identified and had been shown to correlate with immune response and stromal alterations, which was further confirmed by functional enrichment analysis and protein-protein interaction networks. We validated those genes through BLCA dates downloaded from ArrayExpress and thus got the marker genes to predict prognosis of BLCA. Additionally, immune cell infiltration analysis explored the correlation between the verified genes and immune cells. In conclusion, we identified a series of TME-related genes that assess the prognosis and explored the interaction between TME and tumor prognosis to guide clinical individualized treatment.

Highlights

Bladder cancer is the most common malignancy of the urinary tract, and the diagnostics, treatment, and five-year survival rates for bladder cancer are largely unchanged since the 1990s [1]
An algorithm called ESTIMATE designed by Yoshihara et al was used to determine the expression of certain genes of stromal cells and immune cells and calculate immune and stromal scores to infer the fraction of stromal and immune cells in tumor samples and predict the infiltration of nontumor cells [10, 11]. e previous studies have shown that the ESTIMATE algorithm based on big data is demonstrated effective in numerous cancer tissues, such as prostate cancer [12], breast cancer
[13], colon cancer [14], and glioblastoma [11]. ough widely applied in varieties of cancer, prognostic evaluation of the ESTIMATE algorithm on Bladder cancer (BLCA) has not yet been completely clarified. erefore, it provides new opportunities to identify gene expression profile associated with BLCA prognosis

Summary

Introduction

Bladder cancer is the most common malignancy of the urinary tract, and the diagnostics, treatment, and five-year survival rates for bladder cancer are largely unchanged since the 1990s [1]. E tumor microenvironment, which is associated with tumor progression and metastasis [7, 8], is comprised of tumor cells and surroundings such as blood vessels, the extracellular matrix, and other nonmalignant cells such as tumor-associated macrophages (TAMs), mesenchymal stem/stromal cells, fibroblasts, pericytes, and immune cells [9] Among those nonmalignant cells, stromal cells and immune cells play an important role in the whole process of tumors from happening to transferring and have definite clinical significance for diagnosis and prognosis of tumors. We took advantage of BLCA cohorts downloaded from TCGA database and ESTIMATE algorithm-derived stromal and immune scores to predict the prognosis of BLCA by a list of microenvironment-associated genes. Another cohort of BLCA from ArrayExpress proved the prognostic value of those genes. To further elucidate related immunological mechanisms, we explored the role of the immune microenvironment in the development and prognosis of BLCA by immune cell infiltration analysis

Materials and Methods

Result

Discussion

Findings

Conclusions