Abstract

Background Head and neck squamous cell carcinoma (HNSCC) exhibits intensive chemoresistance (CR), leading to frequent recurrence and poor prognosis; however, actionable biomarkers and therapeutic options remain limited. Methods By utilizing bulk profiles of HNSCC patients (TCGA-HNSCC cohort and GSE6631 ) from TCGA and GEO databases, we identified CR-associated DEGs via Limma and WGCNA frameworks. Importantly, LASSO–Cox regression was utilized for the construction of a predictive model and identification of the CR-associated hub gene in TCGA-HNSCC cohort. In addition, predictive model performance was validated in the HNSCC patient bulk profile ( GSE65858 ). Furthermore, the molecular and immune characteristics of the hub gene were estimated at HNSCC patient bulk (TCGA-HNSCC cohort) and single-cell ( GSE163872 ) levels, especially in artificial intelligence (AI)-empowered virtual cells. Specifically, AI-driven therapeutic framework (RefLector) and molecular docking were performed for the recognition of an optimal therapeutic framework for the treatment of HNSCC by targeting the hub gene. Finally, the cariogenic role of the hub gene was evaluated in an in vitro study. Results CR-associated DEGs can guide the risk stratification of HNSCC patients. ANXA1 was identified as a downregulated, malignancy-distributed, prognostic, and druggable biomarker for HNSCC patients, which was also associated with HNSCC progression. BRD-K10482608 can be considered a potential therapeutic agent for the treatment of HNSCC. Conclusion Our study highlighted the CR in risk stratification for HNSCC patients and ANXA1 in the pathogenesis of HNSCC, which can guide personalized and precision medicine for HNSCC patients.