Prognostic evaluation and immune microenvironment profiling of Ewing sarcoma through characterization of lipid metabolism-related genes

Abstract

Introduction: Lipid metabolism contributes to tumor progression and immune regulation in Ewing sarcoma (EwS), but its relationship with prognosis and tumor immune microenvironment (TIME) remains inadequately clarified. Objectives: This study investigated lipid metabolism-related gene (LMRG) subtypes, their associations with immune microenvironmental characteristics, and the prognostic value of an LMRG-based risk model in EwS. Methods: Transcriptomic datasets of EwS from GEO (GSE17679; training cohort) and ICGC (validation cohort) were analyzed using computational techniques. LMRG subtypes were identified by consensus clustering. The TIME was inferred using ESTIMATE, TIMER algorithm, and single-sample gene set enrichment analysis (ssGSEA). A multigene risk score model was developed through LASSO and multivariable Cox regression, evaluated with Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram was constructed based on the model integrated with clinicopathologic variables. Results: Two molecular subtypes showed distinct survival; the immune-enriched, low-purity subtype had poorer outcomes. A five-gene signature (TXNRD1, FABP5, ORMDL1, RAB5A, DBI) stratified patients into high- and low-risk groups, with AUCs of 0.90–0.94 in the training cohort and 0.58–0.85 in the validation cohort. The risk score was associated with increased ESTIMATE and immune scores, along with reduced tumor purity. The integrated nomogram achieved a C-index of 0.759 with acceptable calibration. Conclusion: Dysregulated lipid metabolism is intricately linked to the TIME and patient prognosis in EwS. The LMRG-based risk model provides a potentially useful tool for prognostic stratification and highlights potential therapeutic avenues targeting lipid metabolism and immune modulation.