Prediction of clear cell renal cell carcinoma prognosis based on an immunogenomic landscape analysis

Abstract

Immune-cell infiltration and tumor-related immune molecules play a key role in tumorigenesis and progression. It remains to be systematically studied how immune interactions influence clear cell renal cell carcinoma (ccRCC) molecular characteristics and prognosis. A machine learning algorithm was applied to transcriptome data from the Cancer Genome Atlas (TCGA) database in order to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. By using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were applied to construct a prognostic model. According to further analysis, the area under the receiver operating characteristic curve at one year was 0.927, but at three years was 0.822, and at five years, it was 0.717, indicating good predictive accuracy. It was also discovered that ccRCC immune interactions are governed by molecular regulatory networks. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we have provided an important framework for understanding ccRCC's molecular mechanisms and identifying new prognostic markers and therapeutic targets for future research.