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Background Renal cell carcinoma (RCC) is a common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state. Methods In this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. We selected the most relevant genes from each signature to construct a prognostic risk model to predict prognosis via Kaplan-Meier (KM) survival analysis. And subgroups of the TCGA samples and external data from International Cancer Genome Consortium (ICGC) database were used to verify predictive stability of the model. We performed landscape analysis to assess the difference of gene mutant based on the data from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform. Results We found that the four prognostic risk models constructed by the signatures all could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. And the prognostic risk model was constructed by 8 candidate genes (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) which divided the advanced RCC samples from TCGA database into high-risk and low-risk groups. And there was a significant difference in overall survival (OS) between the two groups. The validity of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC. Conclusions Sum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC. |
