A prognostic model correlated with fatty acid metabolism in Ewing's sarcoma based on bioinformatics analysis.

Abstract

Background: Ewing's sarcoma (EWS) is a highly aggressive malignant tumor that originates from bone or soft tissue. To date, there is no established prognostic model for EWS tumor. This study aims to identify prognostic genes and develop a predictive model associated with fatty acid metabolism in EWS using bioinformatics analysis.

Results: We analyzed the GSE17679 dataset and identified 25 differentially expressed genes related to fatty acid metabolism in EWS. A risk model composed of ACADM, ADH5, ACSL1, ELOVL4, ECI1, PPT1, and ACOT7 gene signatures was constructed. The AUC values at 3 and 5 years were both ≥0.7, indicating good predictive accuracy. GSVA analysis revealed significant differences in fatty acid metabolism pathway enrichment between high- and low-risk groups. Differential genes were primarily enriched in pathways such as fatty acid oxidation, lipid oxidation, lipid modification, and fatty acid degradation. Immune infiltration analysis showed significant differences in memory B cells, activated NK cells, and neutrophils between the two groups. Additionally, significant differences were observed in the expression of immune checkpoints such as HAVCR2 and LDHB. Immunohistochemistry and survival analysis further demonstrated that the expression of PPT1 and ACOT7 proteins was associated with the progression-free survival of EWS patients.

Conclusions: We successfully constructed a prognostic model for EWS related to fatty acid metabolism genes. PPT1 and ACOT7 may serve as promising predictors for EWS prognosis.