An Eight-Gene Metabolic Prognostic Signature in Head and Neck Squamous Cell Carcinoma: Cross-Cohort Evaluation, Single-Cell Analysis, and Preliminary Functional Evaluation of PYGL and G6PD.

Background: Head and neck squamous cell carcinoma (HNSCC) is the most prevalent pathological subtype of head and neck malignancies. Although metabolic reprogramming contributes to malignant progression and adaptation within the tumor microenvironment in HNSCC, its biological and prognostic implications remain incompletely understood. Objective: To develop and evaluate a metabolism-related prognostic score for head and neck squamous cell carcinoma (HNSCC) across multiple cohorts, identify its cellular sources at single-cell resolution, and perform preliminary Methods: Metabolism-related differentially expressed genes were screened in The Cancer Genome Atlas Head and Neck Squamous Cell Carcinoma cohort (TCGA-HNSC). A total of 506 patients with survival data were divided into a training set (n = 355) and an internal test set (n = 151) at a 7:3 ratio. Training-only repeated nested cross-validation benchmarked 126 algorithmic configurations. Survival-SVM had the highest mean out-of-fold C-index, whereas an eight-gene LASSO + StepCox model was retained for interpretability and cross-cohort transportability. External cohort evaluation of overall survival (OS) was performed in GSE65858 (n = 270), while GSE117973 (n = 77), which used disease-specific survival (DSS), was treated as an exploratory cohort. By integrating 54,163 single cells from the GSE150321 and GSE172577 datasets, analyses, including inferCNV, 12 predefined metabolic modules, CellChat, and scTenifoldKnk, were conducted to investigate the cellular origins of the risk score and candidate regulatory networks; additionally, PYGL- and G6PD-targeting siRNAs were evaluated for their effects on target protein expression and wound closure Results: We developed a metabolic prognostic model that included HPRT1, ADA, PYGL, P4HA1, LDHA, G6PD, NT5E, and FTH1. The concordance indices (C-indices) were 0.638 in the TCGA training set, 0.651 in the internal test set, and 0.604 in GSE65858. In a complete-case clinical cohort of 473 TCGA patients, a model combining the risk score with age, sex, and T, N, and M categories achieved C-indices of 0.693 in TCGA and 0.677 in GSE65858. The corresponding 3-year areas under the receiver operating characteristic curve (AUCs) were 0.735 and 0.700. Single-cell analysis showed that the eight genes were distributed across malignant epithelial, proliferating, immune, endothelial, and stromal populations. The 16,240 copy number variation-high (CNV-high) epithelial cells had higher eight-gene scores and higher transcriptional scores for glycogen metabolism, the pentose phosphate pathway, glycolysis, and redox-related programs. Small interfering RNA (siRNA)-mediated silencing of G6PD or PYGL reduced relative wound closure at 12 h in TU686 and SCC25 cells. Conclusion: The eight-gene metabolic score provided moderate and cohort-dependent prognostic discrimination in the TCGA internal test set and GSE65858. Its bulk transcriptomic signal arose from several cell populations. Silencing PYGL or G6PD reduced target protein expression and wound closure. These findings provide preliminary