J Cancer 2020; 11(11):3207-3215. doi:10.7150/jca.37285 This issue
1. Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
2. Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
*Equal contributions to this study.
The tumor immune microenvironment in clear cell Renal Cell Carcinoma (ccRCC) still remains poorly understood. Previous methods to study the tumor immune microenvironment have a limitation when accounting for the functionally distinct cell types. In this study, we investigated the differently infiltrated immune cells and their clinical significance in ccRCC for the purpose of shedding some important light on the complex immune microenvironment in ccRCC. The devolution algorithm (CIBERSORT) was applied to infer the proportion of 22 immune infiltrating cells based on gene expression profiles of ccRCC bulk tissue, which were downloaded from TCGA and GEO databases. As a result, we observed considerable differences in immune cells percentage between ccRCC tumor tissue and paired normal tissue; meanwhile, we uncovered their internal correlations and associations with Fuhrman grade. Moreover, dendritic cells resting, dendritic cells activated, mast cells resting, mast cells activated and eosinophils were associated with favorable prognosis, whereas B cells memory, T cells follicular helper and T cells regulatory (Tregs) were correlated with poorer outcome.
Keywords: devolution algorithm, tumor microenvionment, clear cell renal cell carcinoma (ccRCC), genomic signature