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

## TL;DR

This study identifies copper metabolism genes that predict survival and immune response in Ewing’s sarcoma patients.

## Contribution

A novel risk model using five copper metabolism genes is developed to predict prognosis and immune infiltration in Ewing’s sarcoma.

## Key findings

- Five genes (TFRC, SORD, SLC11A2, FKBP4, AANAT) were selected as a risk signature with strong survival prediction (AUC 0.979 at 5 years).
- High-risk patients had significantly lower survival rates compared to low-risk patients (p=6.013e-09).
- The model showed variations in immune infiltration and drug sensitivity across risk groups.

## Abstract

Cuproptosis is copper-induced cell death. Copper metabolism related genes (CMRGs) were demonstrated that used to assess the prognosis out of tumors. In the study, CMRGs were tested for their effect on TME cell infiltration in Ewing’s sarcoma (ES).

The GEO and ICGC databases provided the mRNA expression profiles and clinical features for downloading. In the GSE17674 dataset, 22prognostic-related copper metabolism related genes (PR-CMRGs) was identified by using univariate regression analysis. Subsequently, in order to compare the survival rates of groups with high and low expression of these PR-CMRGs,Kaplan-Meier analysis was implemented. Additionally, correlations among them were examined. The study employed functional enrichment analysis to investigate probable underlying pathways, 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. According to the Kaplan-Meier survival analysis, the high-risk group had considerably lower survival rates than the low-risk group(p=6.013e-09). The area under the curve (AUC) values for the receiver operating characteristic (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, the study evaluated the variations in immune infiltration across different risk groups, as well as high-expression and low-expression groups. 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.

Flow chart. PR-CMRGs: prognostic-related copper metabolism related genes. DEGs, differentially expressed genes; GSVA, Gene Set Variation Analysis; PPI, protein-protein interaction.