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Comprehensive analysis of T cell exhaustion related signature for predicting prognosis and immunotherapy response in HNSCC.
Discover. Oncology. 2024 March 3
BACKGROUND: T cell exhaustion (TEX) signifies a condition of T cell disorder which implicate the therapeutic benefits and prognostic significance in patients with cancer. However, its role in the Head and Neck Squamous Carcinoma (HNSCC) remains incompletely understood.
METHODS: The detailed data of HNSCC samples were obtained from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets. We computed the expression scores of four TEX-related pathways and detected gene modules closely linked to these pathways, indicating prognostic significance. Following this, regression analyses were performed to select eight genes for the development of a predictive signature. The predictive capacity of this signature was evaluated. Additionally, we examined the relationships between TEX-related signature risk scores and the effectiveness of immunotherapy as well as drug sensitivity.
RESULTS: A novel prognostic model, comprising eight TEX-related genes, was established for patients with HNSCC. The prognostic value was further confirmed using additional GEO datasets: GSE65858 and GSE27020. This signature enables the stratification of patients into high- and low- risk groups, each showing distinct survival outcomes and responsiveness to immunotherapy. The low-risk group demonstrated improved prognosis and enhanced efficacy of immunotherapy. In addition, AZD6482, TAF1, Ribociclib, LGK974, PF4708671 and other drugs showed increased sensitivity in the high-risk group based on drug sensitivity values, offering tailored therapeutic recommendations for individuals with various risks profiles.
CONCLUSION: In conclusion, we developed a novel T cell exhaustion-associated signature, which holds considerable predictive value for both the prognosis of patients with HNSCC and the effectiveness of tumor immunotherapy.
METHODS: The detailed data of HNSCC samples were obtained from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets. We computed the expression scores of four TEX-related pathways and detected gene modules closely linked to these pathways, indicating prognostic significance. Following this, regression analyses were performed to select eight genes for the development of a predictive signature. The predictive capacity of this signature was evaluated. Additionally, we examined the relationships between TEX-related signature risk scores and the effectiveness of immunotherapy as well as drug sensitivity.
RESULTS: A novel prognostic model, comprising eight TEX-related genes, was established for patients with HNSCC. The prognostic value was further confirmed using additional GEO datasets: GSE65858 and GSE27020. This signature enables the stratification of patients into high- and low- risk groups, each showing distinct survival outcomes and responsiveness to immunotherapy. The low-risk group demonstrated improved prognosis and enhanced efficacy of immunotherapy. In addition, AZD6482, TAF1, Ribociclib, LGK974, PF4708671 and other drugs showed increased sensitivity in the high-risk group based on drug sensitivity values, offering tailored therapeutic recommendations for individuals with various risks profiles.
CONCLUSION: In conclusion, we developed a novel T cell exhaustion-associated signature, which holds considerable predictive value for both the prognosis of patients with HNSCC and the effectiveness of tumor immunotherapy.
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