An LNM-Associated Gene Signature for Prognostic Prediction and Immune Profiling in Head and Neck Squamous Cell Carcinoma

Lymph node metastasis (LNM) plays a critical role in the prognosis of head and neck squamous cell carcinoma (HNSCC). To enhance prognostic predictions and investigate the molecular interplay between LNM and HNSCC, we developed an LNM-associated gene signature. Data was sourced from The Cancer Genome Atlas (TCGA), encompassing RNA-sequencing and clinical profiles. We stratified patients based on LNM status and identified differentially expressed genes (DEGs) between lymph node-negative (N0) and lymph node-positive (N1-3) groups. A prognostic model was then constructed while employing Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Patients were randomly allocated into training (70%) and internal validation (30%) cohorts, with an additional external dataset used for validation. The predictive model's performance was assessed through receiver operating characteristic curves and survival analyses. We identified 79 LNM-related prognostic DEGs that formed the basis of our LNM-related risk score (LNMRS). This score stratified patients into low- and high-risk categories, with those having lower LNMRS exhibiting improved survival outcomes, increased immune cell infiltration, and enhanced responses to immunotherapy (PD-1/CTLA4 inhibitors) and chemotherapy. In contrast, patients with high LNMRS showed poorer prognosis and reduced immune responsiveness. Our LNM-related model provides insights into the molecular mechanisms linking LNM and HNSCC and offers a promising tool for personalized treatment strategies. This approach underscores the potential of integrating LNM status with gene expression profiles to refine prognosis and optimize therapeutic interventions in HNSCC.