Abstract
The aim of our research is to explore the various characteristics and genetic profiles of clear cell renal cell carcinoma (ccRCC) in order to discover possible predictors of prognosis and targets for treatment. By utilizing ssGSEA scores, we categorized patients with ccRCC into groups based on their phenotype, distinguishing between low and high. This categorization revealed significant variations in the expression of crucial immune checkpoint genes and Human Leukocyte Antigen (HLA) genes, suggesting the presence of a potential immune evasion tactic in different subtypes of ccRCC. A predictive model was built using genes that are expressed differently and linked to cell death, showing strong effectiveness in categorizing patient risk. Furthermore, we discovered a noteworthy correlation among risk scores, infiltration of immune cells, the expression of genes related to immune checkpoint inhibitors, and diverse clinical features. This indicates that our scoring system for risk could function as a comprehensive gauge of the severity of the disease. The examination of the mutational terrain further highlighted the predominance of particular genetic changes, including VHL and PBRM1 missense mutations. Finally, we have discovered the function of DKK1 in facilitating cell death in ccRCC, presenting an additional possibility for therapeutic intervention. The results of our study suggest the possibility of incorporating molecular information into clinical prediction, which could lead to personalized treatment approaches in ccRCC.