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Comprehensive Multi-Omics Identification of Interferon-γ Response Characteristics Reveals That RBCK1 Regulates the Immunosuppressive Microenvironment of Renal Cell Carcinoma.

Abstract
Interferon-gamma (IFN-γ) has a complex role in modulating the tumor microenvironment (TME) during renal cell carcinoma (RCC) development. To define the role of IFN-γ response genes in RCC progression, we characterized the differential gene expression, prognostic implications, and DNA variation profiles of selected IFN-γ response signatures, which exhibited a significant hazard ratio for the overall survival (OS) and progression-free survival (PFS) of papillary, chromophobia, and clear cell RCC (ccRCC) patients (n = 944). Prognostic nomograms were constructed to predict the outcomes for ccRCC patients, highlighting the prognostic implications of RANBP2-type and C3HC4-type zinc finger containing 1 (RBCK1). Interestingly, large-scale pan-cancer samples (n = 12,521) and three single-cell RNA datasets revealed that RBCK1 showed markedly differential expression between cancer and normal tissues and significantly correlated with tumor-infiltrating immune cells, tumor purity, and immune checkpoint molecules, such as PD-L1, CTLA-4, LAG-3, and TIGIT in pan-cancer samples. Notably, the TIDE score was significantly higher in the RBCK1high group compared with the RBCK1low group in both ccRCC and RCC cohorts. Besides, immunohistochemistry staining showed significantly elevated RBCK1 expression in tumors (n = 50) compared with kidney samples (n = 40) from a real-world cohort, Fudan University Shanghai Cancer Center (FUSCC, Shanghai). After RBCK1 expression was confirmed in ccRCC, we found a significantly decreased number of infiltrating CD4+ T cells, CD4+ FOXP3+ Treg cells, M1 macrophages, and CD56bight/dim NK cells in the immune-cold RBCK1high group. In addition to the distinct heterogeneous immune microenvironment, the increased expression of RBCK1 predicted a prominently worse prognosis than the RBCK1low group for 232 ccRCC patients in the FUSCC proteomic cohort. Furthermore, after transfected with siRNA in human ccRCC cells, extraordinarily decreased cell proliferation, migration capacities, and prominently elevated apoptosis tumor cell proportion were found in the siRNA groups compared with the negative control group. In conclusion, this study identified IFN-γ response clusters, which might be used to improve the prognostic accuracy of immune contexture in the ccRCC microenvironment. Immune-cold RBCK1high patients have pro-tumorigenic immune infiltration and significantly worse outcomes than RBCK1low patients based on results from multi-omics to real-world data. Our discovery of novel independent prognostic indicators for RCC highlights the association between tumor alterations and immune phenotype.
AuthorsWenhao Xu, Juli Tao, Wenkai Zhu, Wangrui Liu, Aihetaimujiang Anwaier, Xi Tian, Jiaqi Su, Guohai Shi, Haineng Huang, Gaomeng Wei, Chuanyu Li, Yuanyuan Qu, Hailiang Zhang, Dingwei Ye
JournalFrontiers in immunology (Front Immunol) Vol. 12 Pg. 734646 ( 2021) ISSN: 1664-3224 [Electronic] Switzerland
PMID34795663 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
CopyrightCopyright © 2021 Xu, Tao, Zhu, Liu, Anwaier, Tian, Su, Shi, Huang, Wei, Li, Qu, Zhang and Ye.
Chemical References
  • Biomarkers, Tumor
  • IFNG protein, human
  • Proteome
  • Transcription Factors
  • Interferon-gamma
  • RBCK1 protein, human
  • Ubiquitin-Protein Ligases
Topics
  • Biomarkers, Tumor (genetics, metabolism)
  • Carcinoma, Renal Cell (enzymology, genetics, immunology, therapy)
  • Cell Line, Tumor
  • Databases, Genetic
  • Decision Support Techniques
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks
  • Genomics
  • Humans
  • Interferon-gamma (genetics, metabolism)
  • Kidney Neoplasms (enzymology, genetics, immunology, therapy)
  • Lymphocytes, Tumor-Infiltrating (immunology, metabolism)
  • Nomograms
  • Phenotype
  • Progression-Free Survival
  • Protein Interaction Maps
  • Proteome
  • Proteomics
  • RNA-Seq
  • Signal Transduction
  • Single-Cell Analysis
  • Time Factors
  • Transcription Factors (genetics, metabolism)
  • Transcriptome
  • Tumor Microenvironment
  • Tumor-Associated Macrophages (immunology, metabolism)
  • Ubiquitin-Protein Ligases (genetics, metabolism)

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