AUTHOR=Chen Yongqin , Zhang Wencan , Xu Xiao , Xu Biteng , Yang Yuxuan , Yu Haozhi , Li Ke , Liu Mingshan , Qi Lei , Jiao Xiejia 

TITLE=Gene signatures of copper metabolism related genes may predict prognosis and immunity status in Ewing’s sarcoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1388868

DOI=10.3389/fonc.2024.1388868

ISSN=2234-943X

ABSTRACT=Cuproptosis is a new type of cell death caused by copper. Studies have demonstrated that copper metabolism related genes (CMRGs) can be used to assess the prognosisout of tumors. This study aimed to investigate the impact of CMRGs on the infiltration of tumor microenvironment (TME) cells in Ewing's sarcoma (ES).The mRNA expression profiles and clinical features were downloaded from the GEO and ICGC databases. In the GSE17674 dataset, 22 prognostic-related copper metabolism related genes (PR-CMRGs) were identified through univariate regression analysis. Subsequently, Kaplan-Meier analysis was conducted to compare survival rates between groups with high and low expression of these PR-CMRGs, and correlations among them were examined. Functional enrichment analysis was utilized to explore potential underlying mechanisms, while GSVA was applied to evaluate enriched pathways in the ES (Expression Set). Through an unsupervised clustering algorithm, samples were classified into two clusters, revealing significant differences in survival rates and levels  of immune infiltration. Using Lasso and step regression methods, five genes (TFRC, SORD, SLC11A2, FKBP4, and AANAT) were selected as risk signatures. Kaplan-Meier survival analysis demonstrated that individuals in the high-risk group experienced considerably worse survival rates compared to those in the low-risk group (p=6.013e-09). The AUC values for the ROC curve were 0.876, 0.883, and 0.979 for 1, 3, and 5 years, respectively. The risk model was further validated in additional datasets, namely GSE63155, GSE63156, and the ICGC datasets. To aid in outcome prediction, a nomogram was developed that incorporated risk levels and clinical features. This nomogram's performance was effectively validated through calibration curves. Additionally, variations in immune infiltration between high/low risk groups and high/low expression groups were assessed. Importantly, several drugs were identified that displayed sensitivity, offering potential therapeutic options for ES.The findings above strongly indicate that CMRGs play crucial roles in predicting prognosis and immune status in ES.