Department of Dermatology, PLA General Hospital, Beijing 100853, PR China.
Dermatological Department, Tianjin Fifth Centre Hospital, Tianjin 300450, PR China.
Life Sci. 2020 Sep 1;256:117906. doi: 10.1016/j.lfs.2020.117906. Epub 2020 Jun 3.
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.
头颈部鳞状细胞癌（HNSCC）是一种侵袭性强、预后异质性大的肿瘤。本研究报告称，免疫相关基因（IRGs）可有效区分具有不同预后的 HNSCC 患者。
从 ImmPort 数据库中获取了 1333 个免疫相关基因在 HNSCC 样本中的 mRNA 水平，这些数据来自癌症基因组图谱（TCGA）。使用基于机器学习的 R 包 H2o 从 IRGs 中筛选出最具代表性的基因。基于 TCGA 集随机生成的训练样本，进行单变量 Cox 回归分析，以确定与预后相关的基因。应用 LASSO Cox 回归分析构建 HNSCC 预后模型。最终保留了六个具有预后意义的 IRGs，用于 LASSO Cox 回归分析。
从 TCGA 随机生成的独立训练和测试集以及来自基因表达数据库（GEO）的另一个独立验证集中，根据预后模型将样本分为高风险和低风险组。高风险组的 HNSCC 样本的总生存（OS）明显低于低风险组。两组样本之间的基因组突变景观和肿瘤浸润免疫细胞也存在差异。此外，风险评分通过分层分析被证明是 HNSCC 的一个独立预后因素。
IRGs 是 HNSCC 的关键预后标志物，有助于临床决策。
