Comprehensive integration of single-cell and bulk transcriptome to reveal plasma cell heterogeneity and a prognosis signature in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy with a low five-year survival rate, emphasising the urgent need for effective prognostic biomarkers to guide patient stratification and personalised treatment. Plasma cells (PCs), a subset of antitumor effector B cells, are known for their antibody production to target tumour-associated antigens. However, their role in HNSCC remains insufficiently characterised. In this study, we integrated single-cell RNA sequencing and spatial transcriptomic datasets to investigate PC heterogeneity in HNSCC. Using the Scissor algorithm, we identified IgA⁺ PCs as a key prognostic protective factor. Comparative analyses of IgA⁺ and IgG⁺ PCs revealed differences in pathway activity, cell communication, and transcription factor activity. Pseudotime trajectory analysis further underscored the role of the IGKV gene family in regulating PC functions related to prognosis. Additionally, a robust prognostic model leveraging IgA⁺ PC-associated genes was developed using multiple machine-learning methods. This model demonstrated superior predictive performance compared to 26 existing models across TCGA-HNSC, GSE65858, GSE41613, and GSE27020 cohorts. Stratification analysis revealed significant differences between high-risk and low-risk groups in survival outcomes, pathway activity, cell infiltration, and therapeutic sensitivity. In conclusion, our comprehensive investigation reveals the critical role of IgA⁺ PCs in HNSCC progression and proposes a PC-based prognostic model for patient stratification and personalised treatment, offering valuable insights into PC heterogeneity and potential therapeutic targets.