Bioinformatic identification of renal cell carcinoma microenvironment-associated biomarkers with therapeutic and prognostic value.

Renal cell carcinoma (RCC) is the ninth most prevalent form of malignancy worldwide. The tumor microenvironment significantly affects gene expression in tumor tissues, which subsequently impacts the prognosis of RCC patients. Available datasets such as The Cancer Genome Atlas (TCGA) can be utilized to improve diagnostic methods and search for novel tumor therapeutic targets and prognostic biomarkers. The current study used the ESTIMATE algorithm to explore the immune and stromal components in RCC. Differentially expressed genes (DEGs) were identified by comparing the gene expression patterns in groups with high and low immune/stromal scores. Functional enrichment analysis was conducted and Kaplan-Meier survival curves were plotted to explore the functions of the DEGs in the tumorigenesis, progression, and prognosis of RCC. Our results revealed that immune and stromal scores are associated with specific clinicopathologic variables in RCC. These variables include gender, tumor grade, tumor stage, tumor size, distant metastasis and prognosis. A total of 48 upregulated and 47 downregulated genes were obtained. Functional enrichment analysis demonstrated a correlation between DEGs and the tumor microenvironment, tumor immune response and RCC tumorigenesis. Kaplan-Meier survival curves showed that 43 out of the 48 identified tumor microenvironment related genes are involved in the prognosis of RCC. Three genes, IL10, IGLL5 and POU2AF1, were selected as the hub genes, and their kinase targets were identified as MAPK1 and PPKCA. A positive correlation was obtained between the expression of IL/POU2AF1 and the abundance of six immune cells. Our study provides potential biomarkers for the therapy and prognosis of RCC.