An angiogenesis-associated gene-based signature predicting prognosis and immunotherapy efficacy of head and neck squamous cell carcinoma patients

• Published in: Journal of cancer research and clinical oncology Vol. 150; no. 2; p. 91
• Main Authors: Chen, Bangjie, Han, Yanxun, Sheng, Shuyan, Deng, Jianyi, Vasquez, Emely, Yau, Vicky, Meng, Muzi, Sun, Chenyu, Wang, Tao, Wang, Yu, Sheng, Mengfei, Wu, Tiangang, Wang, Xinyi, Liu, Yuchen, Lin, Ning, Zhang, Lei, Shao, Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 12.02.2024; Springer Nature B.V
• Subjects: Angiogenesis; Benzoquinones; Cancer Research; Cluster analysis; Gene expression; Gene set enrichment analysis; Head & neck cancer; Head and neck carcinoma; Head and Neck Neoplasms - genetics; Head and Neck Neoplasms - therapy; Hematology; Humans; Immunohistochemistry; Immunotherapy; Internal Medicine; Lactams, Macrocyclic; Medical prognosis; Medicine; Medicine & Public Health; Microenvironments; Mutation; Nomograms; Oncology; Patients; Point mutation; Prognosis; Risk groups; Sensitivity analysis; Squamous cell carcinoma; Squamous Cell Carcinoma of Head and Neck - genetics; Squamous Cell Carcinoma of Head and Neck - therapy; Tumor Microenvironment - genetics; Prognostic signature; Diagnosis; HNSCC; Immunotherapy; Angiogenesis-associated gene
• ISSN: 1432-1335; 0171-5216; 1432-1335
• DOI: 10.1007/s00432-024-05606-8

Objectives To develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC). Materials and methods Data from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples. Results We classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation. Conclusion We have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.