Abstract
The tumour immunological microenvironment is involved in the development of clear cell renal cell carcinoma (ccRCC). Nevertheless, the role of the immunological microenvironment in ccRCC has not been thoroughly investigated. In this study, we combined six ccRCC cohorts into a large cohort and quantified the expression matrix into 53 immunological terms using the ssGSEA algorithm. Five immune terms related to prognosis were screened through 1000 iterations of L1-penalised (lasso) estimation and Cox regression analysis for immune-related risk score (IRS) calculation. The IRS showed satisfactory prognosis prediction efficacy in ccRCC. We then compared the clinical and genomic characteristics of two IRS subgroups. Patients with low IRS showed a high level of tumour mutational burden (TMB) and a low level of copy number variation (CNV), indicating that low IRS group patients have a higher probability of responding to immunotherapy. We employed TIDE and subclass mapping analyses to corroborate our results, and the findings demonstrated that patients with a low IRS had a significantly greater percentage of immunotherapy response. According to the Genomics of Drug Sensitivity in Cancer (GDSC), patients with a high IRS had a decreased IC50 for sunitinib, which is the first-line treatment for ccRCC patients. As a result, the immune characteristics of the microenvironment of ccRCC tumours have been explored, and a signature has been constructed. Analysis demonstrated that our signature could effectively predict prognosis and immunotherapy response rate.