A Ten-Gene Prognostic Signature Associated with Cold-Hot Tumor Typing in Clear Cell Renal Carcinoma

Abstract

Background: Understanding the molecular differences between cold and hot tumors and selecting biomarkers associated with clear-cell renal carcinoma (ccRCC) may provide a reference for evaluating the efficiency of immunotherapy and the prognosis of ccRCC patients. The purpose of this study was to analyze cold-hot tumor typing in ccRCC. Methods: The Cancer Genome Atlas (TCGA) and European Bioinformatics Institute (EBI) ArrayExpress databases were used to download the mRNA expression data of ccRCC, and the cell infiltration proportion and tumor subtypes were evaluated using single sample Gene Set Enrichment Analysis (ssGSEA) and ConsensusClusterPlus, respectively. Subsequently, the immune scores classified all the ccRCC samples into “cold tumors” and “hot tumors”. Then, differentially expressed genes (DEGs) and checkpoint genes were analyzed in cold and hot tumor samples. Finally, a prognosis model was built using the optimized genes, and gene expression levels were validated using the E-MTAB-3267 dataset. Result: Patients with cold tumors have a better clinical prognosis than those with hot tumors. After comparison, 707 DEGs were identified between cold and hot tumor samples, and 10 immune checkpoint genes in hot tumors were found to be upregulated. Additionally, 10 optimized genes, PPARGC1A, SLC22A8, and IL20RB, were screened to build a risk score (RS) prognostic prediction model, and the expression tendencies of the 10 genes (except for DLX4) in TCGA were consistent with those in the E-MTAB-3267 dataset. Conclusions: The 10 important genes may be used as biomarkers for cold and hot tumors in ccRCC, and their related RS model could be used for risk assessment and prognosis prediction of ccRCC.