Mining TCGA database for screening and identification of hub genes in kidney renal clear cell carcinoma microenvironment. Issue 8 (7th November 2019) - Li, Song; Xu, Weibo
Abstract: To evaluate the diagnosis and prognosis of the tumor microenvironment (immunization and stromal cells) in kidney renal clear cell carcinoma (KIRC), KIRC cases selected from The Cancer Genome Atlas database were divided into two groups according to the ESTIMATE algorithm‐derived immune scores. Our data suggested that the Von Hippel‐Lindau mutations and pathologic grades are associated with immune scores. Importat ntly, we identified 173 differential expression genes (DEGs) associated with prognosis in patients with KIRC. Consequently, Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these DEGs, which included immune response, defense response, intrinsic to the plasma membrane, positive regulation of immune system process, and cytokine binding. Next, the protein‐protein interaction network of DEGs and the most significant module was constructed. Five hub genes were identified and analyzed using biological analysis. The survival analysis of the hub genes showed that KIRC patients with high gene expression of C2, MXRA8, TNFSF13B, and X‐linked inhibitor of apoptosis protein‐associated factor 1 (XAF1) had worse overall survival, and MXRA8, TNFSF13B, and XAF1 alteration were significantly associated with disease‐free survival (DFS). In addition, high gene expression of XAF1 alteration showed better DFS. Conclusion: we identified a list of microenvironment‐related genes that are useful for understanding the molecular mechanisms and prognosis of KIRC. Abstract : We identified 173 DEGs associated with prognosis in patients with kidney renal clear cell carcinoma (KIRC) based on the ESTIMATE algorithm‐derived immune scores. Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these DEGs. We identified a list of microenvironment‐related genes that are useful for understanding the molecular mechanisms and prognosis of KIRC.