Profiles of tumor‑infiltrating immune cells in renal cell carcinoma and their clinical implications

Tumor-infiltrating immune cells (TIICs) are crucial for the clinical outcome of renal cell carcinoma (RCC), as they regulate cancer progression. TIICs have therefore the potential to become novel targets of immunotherapies. The present study used CIBERSORT analytical tool, which is a deconvolution algorithm, to comprehensively analyze the composition of immune cells in RCC and normal tissues from The Cancer Genome Atlas (TCGA) cohort, and to determine the prognostic value of TIICs in RCC. A landscape of infiltrating immune cells was determined as containing 13 subpopulations of immune cells, with significant differences between normal and tumor tissues. Subsequently, Kaplan-Meier analysis and log-rank test were used to estimate the prognostic value of TIICs in RCC. The results demonstrated that a higher proportion of regulatory T cells (Tregs) [hazard ratio (HR)=1.596; 95% confidence interval (CI), 1.147–2.222; P=0.006] and follicular helper T cells (HR=1.516; 95% CI, 1.089–2.111; P=0.014) were associated with poor outcome in patients with RCC. Conversely, resting mast cells (HR=0.678; 95% CI, 0.487–0.943; P=0.021) and monocytes (HR=0.701; 95% CI, 0.503–0.977; P=0.036) were associated with a favorable prognosis in patients with RCC. Furthermore, the results from multivariate Cox regression analysis indicated that Tregs and monocytes represented independent risk factors for prognosis in patients with RCC. These findings demonstrated that gene profiling deconvolution by CIBERSORT served to determine the composition of immune cells infiltrated in RCC and may provide some crucial information for the development of immunotherapies.