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Development of an Individualized Immune Prognostic Signature for Clear Cell Renal Cell Carcinoma through the Identification of Differential Immune Genes.

Jianfeng WangChaozhi TangXiaowu Liu
Published in: Journal of oncology (2021)
Increasing evidence has shown that tumor microenvironments are an important feature in clear cell renal cell carcinoma (ccRCC) carcinogenesis and therapeutic efficacy. In this study, two subtypes of ccRCC, high- and low-immune groups, were identified based on the immune gene datasets, of which the differential immune genes were identified accordingly. Furthermore, we constructed a risk prognosis model using five immune genes, specifically, AQP9, KIAA1429, HAMP, CCL13, and CCL21. This model was highly predictive of ccRCC clinical characteristics and showed potential for use in immunotherapy. Furthermore, the five identified genes were highly correlated with the abundance of B cells, CD4 T cells, CD8 T cells, macrophages, neutrophils, and dendritic cells in the tumor microenvironments. Among them, AQP9, KIAA1429, and HAMP exhibited significant prognostic potential. These findings indicate that monitoring and operating tumor microenvironments are of great significance for ccRCC prognosis and precise immunotherapy.
Keyphrases
  • genome wide
  • dendritic cells
  • genome wide identification
  • bioinformatics analysis
  • machine learning
  • dna methylation
  • transcription factor
  • deep learning
  • microbial community
  • rna seq
  • single cell