The aim of our study was to investigate the correlation of immune-related genes with clear cell renal cell carcinoma (ccRCC) prognosis and the role of immune-related genes in the tumor immune microenvironment (TIME) and to build a new prognostic model and prognostic scoring system for renal cancer.

Material and methods:
We downloaded the mRNA expression data of 610 samples (538 ccRCC and 72 normal tissues) from the TCGA database and constructed an immune-related prognostic model using Cox regression analysis and LASSO analysis. Then we internally verified the scientific validity and accuracy of the model using Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves. Subsequently, Cytoscape was used to construct a TF-miRNA-mRNA network. The “CIBERSORT” package was used to perform the immune infiltration analysis. Finally, validation of key gene expression was performed by immunohistochemistry (IHC) and quantitative reverse transcription-PCR (qRT-PCR).

The prognostic model constructed for ccRCC includes 7 genes (KLRC2, PGLYRP2, AGER, CHGA, AVPR1B, IL20RB, LAT). It was proven to have good prognostic performance through the K analysis and the ROC curves. We also constructed an accurate prognostic predictive scoring system by establishing a nomogram. Furthermore, the TF-miRNA-mRNA network revealed the potential mechanism of the model and the immune infiltration analysis revealed a correlation between this model and TIME.

The results suggest that the newly developed 7-immune-related-gene model can be a practical and reliable prognostic tool for ccRCC. It also shows T cell infiltration characteristics in TIME and can therefore be used as an immune biomarker for the diagnosis and treatment of ccRCC.