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Establishment and validation of immune microenvironmental gene signatures for predicting prognosis in patients with head and neck squamous cell carcinoma

International Immunopharmacology(2021)

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摘要
Tumor cells influencing the microenvironment are essential for restrained immunity in head and neck squamous cell carcinoma (HNSCC). There has been considerable progress in the research on monoclonal antibodies for antigen-specific immunotherapy that overcome immunosuppressive checkpoint receptor/ligand signaling in patients with HNSCC. However, alteration of immunogenicity and formation of neoantigens that lead to dysregulation and immunosuppression in the HNSCC microenvironment is not well-defined. The aim of this study was to quantify the Immune, Stromal, and ESTIMATE scores based on the gene matrix of patients with HNSCC reported in The Cancer Genome Atlas (TCGA). We examined the association of the Immune, Stromal, and ESTIMATE scores with the pathologic characteristics of patients with HNSCC, using weighted gene co-expression network (WGCNA) and protein-protein interaction (PPI) analyses, and selected 17 hub gene signatures from the key gene module that was mostly correlated to immunocyte infiltration. Gene functional enrichment showed that this key gene module was closely related to the regulation of immune cell activation and its relevant pathways. In the prognostic analysis, high expression of CD3E, SASH3, CD2, SIRPG, UBASH3A, IKZF1, SPN, IL10RA, SLA, and CD3G was significantly associated with a good prognosis. Consequently, these prognosisrelated genes were validated via analysis of mRNA expression in tumor-infiltrating lymphocytes (TILs) and matched peripheral blood lymphocytes (PBLs) in ten patients with HNSCC, and the expression of these genes was significantly higher in TILs compared to that in PBLs. These findings provide a novel understanding of the tumor immune targets for improved therapeutic regimes in patients with HNSCC.
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关键词
Head and neck squamous cell carcinoma,Immune score,Stromal score,ESTIMATE score,TCGA dataset
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