Oral diseases · 2026
A Lipid Metabolism-Related Gene Signature Stratifies Prognosis and Reflects Metabolic-Immune Heterogeneity in Head and Neck Squamous Cell Carcinoma.
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Xie Ting-Zhi, Sun Xin-Yi, Wang Jing, Wang Zhi, Du Ning, Ge Xi-Yuan
Аннотация
OBJECTIVE: Lipid metabolism-related genes (LMRGs) are crucial in head and neck squamous cell carcinoma (HNSCC) progression. This study aimed to construct and validate a prognostic model for HNSCC based on LMRGs.
METHODS: Prognostic LMRGs were screened by analyzing RNA sequencing and clinical data from TCGA and GEO databases, and a prognostic model was constructed. Patients were divided into high- and low-risk groups according to the risk score. Further enrichment analysis, tumor microenvironment (TME) analysis, drug sensitivity analysis, and in vitro validation were performed.
RESULTS: Eleven LMRGs (PER2, CTLA4, EPHX3, ABCB1, ANO1, TRIB3, PTX3, OLR1, DKK1, FABP4, and CDKN2A) were identified. Patients in the high-risk group had worse survival, and the risk score was an independent prognostic factor. A nomogram combining risk score and clinical characteristics accurately predicted survival. TME analysis revealed immune dysregulation in the high-risk group. Drug sensitivity analysis showed that the high-risk group was more sensitive to lovastatin. In vitro, lovastatin suppressed malignant behaviors and induced a transcriptomic shift toward a low-risk signature.
CONCLUSION: The results validate the biological significance of the model, highlight potential associations between lipid metabolism and the immune microenvironment in HNSCC, and provide a theoretical basis for further investigating metabolism-related therapeutic strategies.
METHODS: Prognostic LMRGs were screened by analyzing RNA sequencing and clinical data from TCGA and GEO databases, and a prognostic model was constructed. Patients were divided into high- and low-risk groups according to the risk score. Further enrichment analysis, tumor microenvironment (TME) analysis, drug sensitivity analysis, and in vitro validation were performed.
RESULTS: Eleven LMRGs (PER2, CTLA4, EPHX3, ABCB1, ANO1, TRIB3, PTX3, OLR1, DKK1, FABP4, and CDKN2A) were identified. Patients in the high-risk group had worse survival, and the risk score was an independent prognostic factor. A nomogram combining risk score and clinical characteristics accurately predicted survival. TME analysis revealed immune dysregulation in the high-risk group. Drug sensitivity analysis showed that the high-risk group was more sensitive to lovastatin. In vitro, lovastatin suppressed malignant behaviors and induced a transcriptomic shift toward a low-risk signature.
CONCLUSION: The results validate the biological significance of the model, highlight potential associations between lipid metabolism and the immune microenvironment in HNSCC, and provide a theoretical basis for further investigating metabolism-related therapeutic strategies.