A Novel Prognostic Signature Based on Metabolism-Related Genes to Predict Survival and Guide Personalized Treatment for Head and Neck Squamous Carcinoma

Abstract

Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients’ prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group (p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group (p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score (p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.