4.8 Article

MYCN amplification is associated with repressed cellular immunity in neuroblastoma: an In Silico immunological analysis of TARGET Database

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FRONTIERS IN IMMUNOLOGY
卷 8, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2017.01473

关键词

MYCN; immunesuppression; neuroblastoma; cellular immunity; in silico analysis

资金

  1. Board of Visitors at the Children's National Medical Center
  2. Michael Sandler Cancer Research Fund
  3. National Institutes of Health [CA1711972, AI064350]

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Purpose: RNA and DNA sequencing data are traditionally used to discern intrinsic cellular pathways in cancer pathogenesis, their utility for investigating the tumor microenvironment (TME) has not been fully explored. This study explores the use of sequencing data to investigate immunity within the TME. Experimental design: Here, we use immune cell fraction estimation analysis to determine the immune profiles in the microenvironment of neuroblastoma (NB) based on RNA-seq data in the TARGET database. The correlation between immune cell transcripts and prognosis in pediatric NB is also investigated. Results: In silico analysis revealed a strong inverse correlation between MYCN amplification and leukocyte infiltration. This finding was validated by immunohistochemistry analysis in tumor samples. Moreover, the abundance of CD4 T cells strongly associated with better patient survival regardless of MYCN gene amplification, while those of CD8 T cells, NK or B cells do not. Based on characteristic cytokine expression of CD4 subsets in tumors, the Th2 rather than Th1 levels were associated with better prognosis. Conclusion: We found that the in silico analysis of TARGET database reflected tumor immunity and was validated by the immunohistochemical tumor data. Our results reveal the association of MYCN amplification with repressed cellular immunity and the potential prognostic value of infiltrating CD4 T cell transcripts in pediatric NB. This analysis illustrates the potential role of MYCN in NB as a regulator of immune privilege and characterizes the power of in silico analysis for delineating cancer immunology and risk stratification.

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