Identification of 4-Genes Model in Papillary Renal Cell Tumor Microenvironment Based on Comprehensive Analysis
Abstract Background: The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods: PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. 323 samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression were conducted. Cross genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS). Finally, univariate and multivariate cox regression analysis were employed to construct survival model and predict prognosis. Results: We found immune/stromal score was correlated with T pathological grade and PRCC subtypes. 989 differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Conclusions: Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.