Research Paper Volume 15, Issue 22 pp 13100—13117

Identification of aneuploidy-related gene signature to predict survival in head and neck squamous cell carcinomas

Yu Liu1, , Yonghua Yuan2, , Tao Chen1, , Hongyi Xiao3, , Xiangyu Zhang3, , Fujun Zhang3, ,

  • 1 Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
  • 2 Research Center for Pharmacodynamic Evaluation Engineering Technology of Chongqing, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
  • 3 Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

Received: July 7, 2023       Accepted: October 15, 2023       Published: November 20, 2023
How to Cite

Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: To parse the characteristics of aneuploidy related riskscore (ARS) model in head and neck squamous cell carcinomas (HNSC) and their predictive ability on patient prognosis.

Methods: Molecular subtyping of HNSC specimens was clustered by Copy Number Variation (CNV) data from The Cancer Genome Atlas (TCGA) dataset applying consistent clustering, followed by immune condition evaluation, differentially expressed genes (DEGs) analysis and DEGs function annotation. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction, Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analysis were implemented to construct an ARS model. A nomogram for clinic practice was designed by rms package. Immunotherapy evaluation and drug sensitivity prediction were also carried out.

Results: We stratified HNSC patients into three different molecular subgroups, with the best prognosis in C1 cluster among 3 clusters. C1 cluster displayed greatest immune infiltration status. The most DEGs between C1 and C2 groups, mainly enriched in cell cycle and immune function. We constructed a nine-gene ARS model (ICOS, IL21R, CCR7, SELL, CYTIP, ZAP70, CCR4, S1PR4 and CD79A) that effectively differentiates between high- and low-risk patients. Patients in low ARS group showed a higher sensitivity to immunotherapy. A nomogram built by integrating ARS and clinic-pathological characteristics helped predict clinic survival benefit. Drug sensitivity evaluation found that 4/9 inhibitor drugs (MK-8776, AZD5438, PD-0332991, PHA-665752) acted on the cell cycle.

Conclusions: We classified 3 molecular subtypes for HNSC patients and established an ARS prognostic model, which offered a prospective direction for prognosis in HNSC.


AUC: area under concentration-time curve; ARS: aneuploidy related riskscore; CCLE: Cancer Cell Line Encyclopedia; CDF: cumulative distribution function; CNV: Copy Number Variation; DEGs: differentially expressed genes; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; FDR: false discovery rate; GDSC: Genomics of Drug Sensitibity in Cancer; GEO: Gene Expression Omnibus; GO: Gene Ontology; HNSC: head and neck squamous cell carcinomas; HR: hazard ratio; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; OS: overall survival; PPI: protein-protein interaction; WGCNA: Weighted gene co-expression network analysis; ROC: receiver operating characteristic analysis; TCGA: The Cancer Genome Atlas.