Comprehensive immunogenomic landscape analysis of prognosis-related genes in head and neck cancer
Comprehensive immunogenomic landscape analysis of prognosis-related genes in head and neck cancer
复制标题
头颈癌预后相关基因的综合免疫基因组图谱分析。
DOI：
10.1038/s41598-020-63148-8
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发表时间：
2020-04-14
影响因子：
4.6
通讯作者：
He, Yong-Wen
中科院分区：
文献类型：
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作者：
Li, Lei;Wang, Xiao-Li;He, Yong-Wen
头颈癌是世界上第六大最常见的恶性肿瘤，90%的病例是鳞状细胞癌。在这项研究中，我们对免疫基因组学进行了系统的调查，以确定头颈部鳞状细胞癌（HNSCC）的预后生物标志物。我们通过查询来自癌症基因组图谱（TCGA）数据集中527例HNSCC患者的RNA-seq数据，包括41例HPV+和486例HPV-样本，分析了免疫相关基因（IRGs）的表达谱和临床特征。通过比较肿瘤和正常样本的基因表达差异并进行生存分析，我们发现差异表达的免疫基因与HNSCC患者预后密切相关。通过基因本体（GO）和京都基因与基因组百科全书（KEGG）通路分析，对差异表达的免疫基因组预后相关基因的生物学功能进行注释。另外两个来自Oncomine数据库的队列被用于验证。65,56个差异表达的IRGs分别与总样本和HPV样本的临床预后相关。此外，我们分别从65,56个差异表达的irg中提取了10,11个与预后相关的irg。与临床预后有显著相关性，可用于构建预后预测模型。使用多变量ROC曲线（特别是AUC）来衡量预后模型的准确性。这些基因主要富集在与免疫细胞迁移、受体和配体活性相关的几个基因本体（GO）术语中。KEGG通路分析显示，与细胞因子-细胞因子受体相互作用相关的通路富集，这些通路主要参与生物过程。此外，我们从4784个差异表达基因中鉴定出63个差异表达转录因子（TFs），并在差异表达tf和高危生存相关IRGs之间的调控网络中形成了16个边缘，涉及18个节点。无论HPV状态如何，B细胞和CD4 T细胞浸润水平与预后相关免疫基因的表达均呈显著负相关。总之，这项综合分析确定了预后IRGs作为潜在的生物标志物，并且本研究中生成的模型可以准确预测生存。
Head and neck cancer is the sixth most common malignancy around the world, and 90% of cases are squamous cell carcinomas. In this study, we performed a systematic investigation of the immunogenomic landscape to identify prognostic biomarkers for head and neck squamous cell carcinoma (HNSCC). We analyzed the expression profiles of immune-related genes (IRGs) and clinical characteristics by interrogating RNA-seq data from 527 HNSCC patients in the cancer genome atlas (TCGA) dataset, including 41 HPV+ and 486 HPV- samples. We found that differentially expressed immune genes were closely associated with patient prognosis in HNSCC by comparing the differences in gene expression between cancer and normal samples and performing survival analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the biological functions of the differentially expressed immunogenomic prognosis-related genes. Two additional cohorts from the Oncomine database were used for validation. 65, 56 differentially expressed IRGs was associated with clinical prognosis in total and HPV- samples, respectively. Furthermore, we extracted 10, 11 prognosis-related IRGs from 65, 56 differentially expressed IRGs, respectively. They were significantly correlated with clinical prognosis and used to construct the prognosis prediction models. The multivariable ROC curves (specifically, the AUC) were used to measure the accuracy of the prognostic models. These genes were mainly enriched in several gene ontology (GO) terms related to immunocyte migration and receptor and ligand activity. KEGG pathway analysis revealed enrichment of pathways related to cytokine-cytokine receptor interactions, which are primarily involved in biological processes. In addition, we identified 63 differentially expressed transcription factors (TFs) from 4784 differentially expressed genes, and 16 edges involving 18 nodes were formed in the regulatory network between differentially expressed TFs and the high-risk survival-associated IRGs. B cell and CD4 T cell infiltration levels were significantly negatively correlated with the expression of prognosis-related immune genes regardless of HPV status. In conclusion, this comprehensive analysis identified the prognostic IRGs as potential biomarkers, and the model generated in this study may enable an accurate prediction of survival.