Abstract
BACKGROUND: Reprogramming of amino acid metabolism (AAM) has been established as conducive to tumor cell proliferation and modulation of cellular oxidation-reduction dynamics. However, the intricate characteristics of amino acid metabolism-related genes (AAMRGs) in laryngeal squamous cell carcinoma (LSCC) remain incompletely understood. This study seeks to elucidate these characteristics and construct a prognostic risk model based on AAMRGs to facilitate the development of personalized treatment strategies. METHODS: Differentially expressed AAMRGs were identified from comprehensive datasets including The Cancer Genome Atlas (TCGA) database, GSE27020 dataset from Gene Expression Omnibus (GEO), and Molecular Signatures Database (MSigDB). Subsequently, protein-protein interaction network analysis and functional enrichment assessment of differentially expressed AAMRGs were conducted. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) regression analysis followed by Cox regression analysis. The predictive performance of this risk signature was evaluated using Kaplan-Meier curves and receiver operating characteristic (ROC) curves in both the training and validation sets. Furthermore, the independent prognostic factors were validated using univariate and multivariate Cox regression analyses, and a nomogram was constructed based on these results. Immunohistochemical staining, western blotting, and real-time quantitative polymerase chain reaction (RT-qPCR) were employed to validate the expression of key genes in LSCC clinical samples and cell lines. The effects of SHMT1 overexpression on TU686 cell line was evaluated by CCK-8, wound healing, transwell assay, and flow cytometry. RESULTS: From 45 differentially expressed AAMRGs, SMS, SHMT1, and GPT were identified to compose a prognostic risk scoring model. The high-risk group demonstrated a significantly worse prognosis in both the training and validation sets. Additionally, a nomogram based on independent prognostic factors was developed. Notably, data mining of public databases and analysis of cellular and clinical specimens using IHC, RT-qPCR, and WB demonstrated that SHMT1 expression was decreased in LSCC. Moreover, SHMT1 not only correlated with prognosis but also exhibited associations with the clinical stage of LSCC. We also found that SHMT1 could inhibit the proliferation, migration, and invasion of laryngeal squamous cell carcinoma cells while promoting cell apoptosis by constructing SHMT1-overexpressing cell lines. CONCLUSIONS: This study presents a validated prognostic risk score model comprising AAMRGs in LSCC. Furthermore, our findings highlight the downregulation of SHMT1 in LSCC and its significant association with patient prognosis and tumor stage.