The
mRNA vaccines have been considered effective for combating
cancer. However, the core components of the
mRNA vaccines against
head and neck squamous cell carcinoma (
HNSCC) and the effects remain unclear. Our study aims to identify effective
antigens in
HNSCC to develop
mRNA vaccines for corresponding potential patients. Here, we analyzed alternative splicing and mutation of genes in TCGA-
HNSCC samples and identified seven potential
tumor antigens, including SREBF1, LUC7L3, LAMA5, PCGF3, HNRNPH1, KLC4, and OFD1, which were associated with nonsense-mediated mRNA decay factor expression, overall survival prognosis and the infiltration of antigen-presenting cells. Furthermore, to select suitable patients for vaccination, immune subtypes related to
HNSCC were identified by consensus clustering analysis, and visualization of the
HNSCC immune landscape was performed by graph-learning-based dimensionality reduction. To address the heterogeneity of the population that is suitable for vaccination, plot cell trajectory and WGCNA were also utilized.
HNSCC patients were classified into three prognostically relevant immune subtypes (Cluster 1, Cluster 2, and Cluster 3) possessing different molecular and cellular characteristics, immune modulators, and mutation statuses. Cluster 1 had an immune-activated phenotype and was associated with better survival, while Cluster 2 and Cluster 3 were immunologically cold and linked to increased
tumor mutation burden. Therefore,
HNSCC patients with immune subtypes Cluster 2 and Cluster 3 are potentially suitable for
mRNA vaccination. Moreover, the prognostic module hub genes screened seven genes, including IGKC, IGHV3-15, IGLV1-40, IGLV1-51, IGLC3, IGLC2, and CD79A, which could be potential
biomarkers to predict prognosis and identify suitable patients for
mRNA vaccines. Our findings provide a theoretical basis for further research and the development of anti-
HNSCC mRNA vaccines and the selection of suitable patients for vaccination.