• Yushen Li, Lu Tao, Jiajun Xin, Yifei Dai, Xiantao Chen, Jiatong Zou, Rui Wang, Bowei Wang, and Zhihui Liu.
• Hospital of Stomatology, Jilin University, Changchun, People's Republic of China.
• Medicine (Baltimore). 2024 Mar 22; 103 (12): e37308e37308.

AbstractDisulfidptosis is a newly discovered cell death pattern that has been less studied in head and neck squamous carcinoma (HNSCC). Exploring the molecular features of different subtypes of HNSCC based on disulfidptosis-associated genes (DAGs) is important for HNSCC. In addition, immunotherapy plays a pivotal role in the treatment of HNSCC. Exploring the sensitivity of immunotherapies and developing predictive models is essential for HNSCC. We analyzed the expression and mutational status of DAGs in 790 HNSCC patients and correlated the dates with clinical prognosis. HNSCC patients were divided into 2 groups based on their DAG expression. The relationship between DAGs, risk genes, and the immune microenvironment was analyzed using the CIBERSORT algorithm. A disulfidptosis risk model was constructed based on 5 risk genes using the LASSO COX method. To facilitate the clinical applicability of the proposed risk model, we constructed column line plots and performed stem cell correlation analysis and antitumor drug sensitivity analysis. Two different disulfidptosis-associated clusters were identified using consistent unsupervised clustering analysis. Correlations between multilayer DAG alterations and clinical characteristics and prognosis were observed. Then, a well-performing disulfidptosis-associated risk model (DAG score) was developed to predict the prognosis of HNSCC patients. We divided patients into high-risk and low-risk groups based on the DAG score and found that patients in the low-risk group were more likely to survive than those in the high-risk group (P < .05). A high DAG score implies higher immune cell infiltration and increased mutational burden. Also, univariate and multivariate Cox regression analyses revealed that the DAG score was an independent prognostic predictor for patients with HNSCC. Subsequently, a highly accurate predictive model was developed to facilitate the clinical application of DAG scores, showing good predictive and calibration power. Overall, we present a comprehensive overview of the DAG profile in HNSCC and develop a new risk model for the therapeutic status and prognosis of patients with HNSCC. Our findings highlight the potential clinical significance of DAG and suggest that disulfidptosis may be a potential therapeutic target for patients with HNSCC.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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