Kailuo XIE, Wenbin LIU, Jin HU
Eurasian Journal of Medicine and Oncology - 2026;10(4):1-14
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.
