Analysis of metabolite characteristics and signaling pathways in head and neck cancer based on metabolomics

Introduction: Head and neck cancer (HNC) represents a highly heterogeneous group of malignancies, including oral, pharyngeal, and laryngeal cancers. Despite advancements in diagnosis and treatment, early detection and personalized therapeutic strategies remain challenging. Metabolomics has emerged as a valuable tool for systematically profiling tumor metabolism, revealing unique metabolic phenotypes and potential biomarkers. This study aims to characterize the metabolic landscape of HNC using salivary metabolomics and integrative pathway analysis to identify biomarkers and understand mechanisms driving tumor initiation and progression. Methods Salivary metabolomics data from the MetaboLights database (32 HNC patients and 27 healthy controls) and proteomics data from the TCGA-HNSC project were integrated. Multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA), were performed. Differential metabolites were identified based on t-tests, VIP scores, and FDR correction. KEGG pathway enrichment analysis was conducted to explore functional associations between metabolites and genes. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of candidate metabolites. Differentially expressed genes (DEGs) were screened using the Wilcoxon test and subjected to pathway enrichment and integrative metabolite–gene network analysis. Results A total of 25 significantly altered metabolites (18 upregulated, 7 downregulated) were identified. Among these, 1,4-dichlorobenzene (AUC = 0.997) and 1,2-decanediol (AUC = 0.982) exhibited excellent diagnostic potential. KEGG analysis revealed significant enrichment in protein digestion and absorption, propanoate metabolism, and sulfur metabolism pathways (P < 0.05). Among these, protein digestion and absorption was the only pathway enriched in both metabolomics and proteomics data, involving dysregulated genes such as COL1A1 and SLC7A8. Short-chain fatty acids (acetate, propionate) and alcohol metabolites were significantly upregulated in the HNC group. Proteomic analysis further revealed enrichment of signaling pathways associated with tumor invasiveness, including PI3K-AKT, focal adhesion, and cytoskeletal remodeling. Conclusion This study systematically delineates the metabolic reprogramming features and associated signaling pathways in HNC. The identified metabolites exhibit strong potential as non-invasive diagnostic biomarkers. The protein digestion and absorption pathway, involving metabolites such as acetate and phenol, and genes like COL1A1 and SLC7A8, may play a key role in remodeling the tumor microenvironment and driving cancer progression.