Molecular Evolutionary Landscape of the Immune Microenvironment of Head and Neck Cancer

Head and neck cancer is a highly heterogeneous malignant tumor. Numerous studies have shown that the immune microenvironment of head and neck cancer has a significant impact on its occurrence and development, as well as its prognosis. However, there have been fewer studies related to the accurate immunophenotyping of head and neck cancer. In this study, we used gene expression profile information and clinical information from the TCGA-HNSC cohort (502 samples) and the GSE655858 cohort (270 samples) to identify and independently validate three immune subtypes (Cluster1-Cluster3) with different immune-related molecular profiles and clinical outcomes. Cluster2, which is mainly dominated by B-lymphocyte infiltration, was found to have the best prognosis. In addition, a support vector machine (SVM)-based classifier was constructed, which could accurately classify HNSC based on 19 genes. Furthermore, the results of the prognostic analysis showed activation of antibody-secreting B-lymphocyte function, which showed a good prognostic effect in all three immune subtypes of HNSC. Finally, the immune evolutionary landscape of HNSC was constructed in an attempt to explain the evolutionary pattern of the immune subtypes of HNSC. In summary, we provide a conceptual framework for understanding the tumor immune microenvironment in HNSC and demonstrate the importance of immune infiltration of B lymphocytes in HNSC. Further research is needed to assess the importance of these immunophenotypes in combination drug therapy and to provide a basis for screening appropriate patients for immunotherapy.

Automated summary

In this study, the immune microenvironment of head and neck cancer was analyzed using gene expression profiles and clinical information from two cohorts. Three immune subtypes with different molecular profiles and clinical outcomes were identified and validated. B-lymphocyte infiltration was found to be associated with the best prognosis. Furthermore, a classifier based on 19 genes was constructed to accurately classify head and neck cancer.