头颈部鳞状细胞癌
肿瘤科
比例危险模型
内科学
医学
头颈部癌
预测模型
单变量分析
生存分析
免疫系统
多元分析
癌症
总体生存率
免疫学
作者
Jingrun Yang,Kuixia Xie,Chang-Jiu Li
出处
期刊:Life Sciences
[Elsevier]
日期:2020-09-01
卷期号:256: 117906-117906
被引量:9
标识
DOI:10.1016/j.lfs.2020.117906
摘要
Head and neck squamous cell carcinoma (HNSCC) is an highly aggressive tumor with heterogeneous prognosis. We here report that immune-related genes (IRGs) could effectively distinguish prognostically different HNSCC patients. MRNA levels of 1333 IRGs that from ImmPort database in HNSCC samples were acquired from the Cancer Genome Atlas (TCGA). H2o, a machine learning-based R package, was used for screening the top most representative genes from the IRGs. Univariate Cox-regression analysis was performed to identify prognostically-related genes based on the randomly generated training samples from TCGA set. LASSO Cox-regression analysis was applied for the construction of prognostic model for HNSCC. A total of six IRGs were finally retained for their prognostic significance and used for LASSO Cox-regression analysis. Samples from exclusive training and testing set that randomly generated from TCGA, and another independent validation set from the Gene Expression Omnibus (GEO) were divided into high- and low-risk groups according to the prognostic model. HNSCC samples within high-risk groups have significantly inferior overall survival (OS) compared with those within low-risk groups. Differences in genomic mutation landscape and tumor infiltration immune cells also exist between the two sample groups. What's more, risk score was proved to be an independent prognostic factor for HNSCC by stratification analysis. IRGs are pivotal HNSCC prognostic signatures and should be helpful for its clinical decision-making.
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