Identification and Validation of a Lipid Metabolism-Related Gene Signature for Predicting Prognosis and Immunotherapy Response in Oral Squamous Cell Carcinoma

Background/Objectives: Lipid metabolism plays a critical role in tumor progression and immunotherapy efficacy in oral squamous cell carcinoma (OSCC). However, clinically applicable lipid metabolism-based models for predicting prognosis and immunotherapy response remain limited. This study aimed to develop and validate such a model in OSCC. Methods: Using transcriptomic data of OSCC from the TCGA database and a set of lipid metabolism-related genes (LMRGs), we constructed an LMRG-based risk score model via LASSO regression to predict patient survival. This model was subsequently validated using the independent GEO dataset GSE41613. Results: Patients in the high-risk group exhibited significantly poorer overall survival than those in the low-risk group (training cohort: p < 0.0001; validation cohort: p = 0.0086). We also developed a nomogram incorporating the risk score and clinical characteristics, and the risk score was identified as an independent prognostic factor for OSCC patients. Furthermore, the risk score was significantly associated with the tumor immune microenvironment; samples with a lower risk score showed elevated CD8+ T cell infiltration and a better response to immunotherapy. Additionally, the high-risk group exhibited an increased tumor mutation burden and resistance to most chemotherapeutic agents. Notably, several drugs (e.g., obatoclax mesylate) showed significant efficacy in the high-risk group, representing promising therapeutic candidates. Conclusions: This study reveals that the LMRG signature could serve as a valuable tool for prognosis assessment, risk stratification, and therapy guidance in OSCC.