Identification of molecular classification and gene signature for predicting prognosis and immunotherapy response in HNSCC using cell differentiation trajectories

Abstract

Abstract Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis. This article aims to explore the clinical significance of cell differentiation trajectory in HNSCC, identify different molecular subtypes by consensus clustering analysis, and develop a prognostic risk model on the basis of differentiation-related genes (DRGs) for predicting the prognosis of HNSCC patients. Firstly, cell trajectory analysis was performed on single-cell RNA sequencing (scRNA-seq) data, four molecular subtypes were identified from bulk RNA-seq data, and the molecular subtypes were predictive of patient survival, clinical features, immune infiltration status, and expression of immune checkpoint genes (ICGs)s. Secondly, we developed a 10-DRG signature for predicting the prognosis of HNSCC patients by using weighted correlation network analysis (WGCNA), differential expression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. Then, a nomogram integrating the risk assessment model and clinical features can successfully predict prognosis with favorable predictive performance and superior accuracy. We projected the response to immunotherapy and the sensitivity of commonly used antitumor drugs between the different groups. Finally, we used the quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) analysis and western blot to verify the signature. In conclusion, we identified distinct molecular subtypes by cell differentiation trajectory and constructed a novel signature based on differentially expressed prognostic DRGs, which could predict the prognosis and response to immunotherapy for patients and may provide valuable clinical applications in the treatment of HNSCC.