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.
Copyright © 2020. Published by Elsevier Inc.