A novel seven-immune-genes-based model to improve prognosis prediction of clear cell renal cell carcinoma

Abstract

IntroductionThe aim of our study is to investigate the correlation of immune-related genes with clear cell renal cell carcinoma (ccRCC) prognosis and the role of immune-related genes in tumor immune microenvironment (TIME) and to build a new prognostic model and prognostic scoring system for renal cancer.Material and methodsWe downloaded the mRNA expression data of 610 samples (538 ccRCC and 72 normal tissues) from TCGA database and constructed a 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).ResultsThe prognostic model constructed for ccRCC includes 7 genes (KLRC2, PGLYRP2, AGER, CHGA, AVPR1B, IL20RB, LAT). And it was proven to have good prognostic performance through the KM 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.ConclusionsThe results suggest that the newly developed 7 immune-related genes model can be a practical and reliable prognostic tool for ccRCC. It also shows T cell infiltration characteristics in TIME can therefore be used as an immune biomarker for the diagnosis and treatment of ccRCC.