Identification of genes of prognostic value in the ccRCC microenvironment from TCGA database

• Main Authors: Bangbei Wan, Bo Liu, Yuan Huang, Cai Lv
• Format: Article
• Language: English
• Published: Wiley 2020-04-01
• Series: Molecular Genetics & Genomic Medicine
• Subjects: ccRCC; immune scores; microenvironment; stromal scores; TCGA database
• Online Access: https://doi.org/10.1002/mgg3.1159

Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma. Bioinformatics analyses were used to screen candidate genes associated with the prognosis and microenvironment of ccRCC and elucidate the underlying molecular mechanisms of action. Methods The gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. The ESTIMATE algorithm was used to compute the immune and stromal scores of patients. Based on the median immune/stromal scores, all patients were sorted into low‐ and high‐immune/stromal score groups. Differentially expressed genes (DEGs) were extracted from high‐ versus low‐immune/stromal score groups and were described using functional annotations and protein‒protein interaction (PPI) network. Results Patients in the high‐immune/stromal score group had poorer survival outcome. In total, 95 DEGs (48 upregulated and 47 downregulated genes) were screened from the gene expression profiles of patients with high immune and stromal scores. The genes were primarily involved in six signaling pathways. Among the 95 DEGs, 43 were markedly related to overall survival of patients. The PPI network identified the top 10 hub genes—CD19, CD79A, IL10, IGLL5, POU2AF1, CCL19, AMBP, CCL18, CCL21, and IGJ—and four modules. Enrichment analyses revealed that the genes in the most important module were involved in the B‐cell receptor signaling pathway. Conclusion This study mainly revealed the relationship between the ccRCC microenvironment and prognosis of patients. These results also increase the understanding of how gene expression patterns can impact the prognosis and development of ccRCC by modulating the tumor microenvironment. The results could contribute to the search for ccRCC biomarkers and therapeutic targets.