摘要
Acylation modification plays a crucial role in modulating head and neck squamous cell carcinoma (HNSCC) progression, and their specific prognostic implications in HNSCC have not been thoroughly investigated. Eleven acylation modifications (AM) (crotonylation, lactylation, succinylation, benzoylation, butyrylation, malonylation, glutarylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, palmitoylation, myristoylation, and prenylation) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, a machine learning approach was used to create AM.score. This analysis revealed 2 distinct subtypes of AMs, each characterized by unique molecular signatures. By integrating different categories of genes, including DEGs, module genes, and AM-related genes, 16 hub genes were identified, and an AM.score was developed. AM.score was rigorously validated across independent external cohorts (TCGA-HNSCC, GSE41613, GSE42743, and GSE65858) and an in-house cohort, demonstrating its reliability and potential applicability. The AM.score serves a dual purpose in its application, as it encapsulates the essential clinical context and offers valuable insights regarding the efficacy of immunotherapy treatments. In particular, patients categorized with a high AM.score displayed a TME that was more actively engaged, which corresponded with a poor prognosis. Furthermore, these patients demonstrated a high level of responsiveness to immunotherapy interventions. Furthermore, an examination using scRNA-seq indicated that patients with high AM.score exhibited heightened cell proliferation and malignancy. This novel AM.score could effectively assess the prognosis and therapeutic responses of HNSCC patients, providing new perspectives for individualized treatment for the patient population.