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Updated: Jun 18, 2026
Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
Published on: December 23, 2022
A prognostic model for head and neck squamous cell carcinoma based on eosinophil extracellular trap related genes
Chuyu Han1, Xuecheng Luo1, Shouyin Xiao1
1School of Innovation and Entrepreneurship, Shanxi Medical University, Taiyuan, China.
Background:
Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors worldwide and presents significant challenges due to its high rates of recurrence, metastasis, and poor prognosis. Emerging evidence suggests that eosinophil extracellular traps (EETs)-related genes may play a crucial role in tumor progression and aggressiveness. Consequently, investigating the intersection between HNSCC and EETs-related genes and constructing a prognostic model may offer valuable clinical insights. This study aims to identify the key genes that play a significant role in the eosinophil extracellular trap of HNSCC, and to construct a prognostic model to guide treatment.
Methods:
We systematically analysed transcriptomic data from The Cancer Genome Atlas (TCGA) alongside clinical datasets to identify differentially expressed genes (DEGs) in HNSCC patients. Through comprehensive bioinformatics approaches, we identified genes intersecting between HNSCC DEGs and EETs-related genes. A prognostic model was constructed used the random forest algorithm and externally validated with data from the Gene Expression Omnibus (GEO). We further assessed the model's relationship with the tumor microenvironment (TME) and its association with drug sensitivity.
Results:
A total of 57 overlapping genes were identified. Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analysis, five key prognostic genes (ANXA5, CCL26, CXCL8, PDIA3, and ZAP70) were selected to build the predictive model. This model demonstrated strong performance, with area under the curve (AUC) values exceeding 0.81 and 0.72 in the training and validation cohorts, respectively.
Conclusions:
Patients stratified by risk score exhibited distinct immune cell infiltration patterns and drug sensitivity profiles. The EETs-related gene model may serve as a valuable biomarker for predicting prognosis and informing therapeutic strategies in HNSCC patients.
