A novel prognostic model based on immunogenomics for clear cell renal cell carcinoma

Abstract

BackgroundImmune cell infiltration into tumor tissue is closely related to the clinical outcomes of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to screen out potential immune genes associated with ccRCC, analyze their relationships with clinical outcomes, and construct a signature to predict ccRCC. MethodsThe transcriptome RNA-sequencing data in 539 ccRCC and 72 adjacent normal tissues were obtained from TCGA database. Biomedical computational algorithms were conducted to identify immune-related differential expressed genes (IRDGs) and enriched pathways. Then, LASSO Cox and multivariate Cox analyses were performed to screen out genes that were then used to construct the prognostic model. ResultsA total of 116 down-regulated and 565 up-regulated IRDGs were identified. Pathway enrichment analysis suggested that IRDGs was mainly enriched in the pathway of “cytokines and cytokine receptors”. The entire data of ccRCC were randomly divided into the training set and the test set with a ratio of 1:1. A 4-gene signature was then constructed using LASSO Cox analysis and multivariate Cox analysis in the training set. This prognostic signature could stratify patients into high- and low-risk groups successfully, and serve as an independent predictor when adjusted with clinical factors by univariate and multivariate Cox regression analysis. These results were verified in the test set and the entire set. Besides, the abundance of CD4 + T cells and dendritic cells increased in the high-risk group. Finally, we built a nomogram incorporating risk score and clinical factors to predict the overall survival of ccRCC patients. ConclusionsThese findings may contribute to the research of ccRCC in immunization part.