Prognosis Implication of a Novel Metabolism-Related Gene Signature in Ewing Sarcoma

Glycolysis-Metabolism-Related Prognostic Signature for Ewing Sarcoma Patients

Ewing sarcoma (EwS) is a malignant sarcoma which occurs in bone and soft tissues commonly happening in children with poor survival rates. Changes in cell metabolism, such as glycolysis, may provide the environment for the transformation and progression of tumors. We aimed to build a model to predict prognosis of EwS patients based on glycolysis and metabolism genes. Candidate genes were obtained by differential gene expression analysis based on GSE17679, GSE17674 and ICGC datasets. We performed GO and KEGG pathway enrichment analysis on candidate genes. Univariate Cox and LASSO Cox regression analyses were conducted to construct a model to calculate the Risk Score. GSEA was done between high-risk and low-risk groups. CIBERSORT was applied to analyze the immune landscape. We got 295 candidate glycolysis-metabolism-related genes which were enriched in 620 GO terms and 18 KEGG pathways. 12 Genes were selected by univariate Cox model and 5 of them were determined by LASSO Cox regression analysis to be used in the construction of the Risk Score model. The Risk Score could be considered as an independent prognosis factor. The immune landscape and immune checkpoints' expression significantly differed between high- and low-risk groups. Our research constructed a new glycolysis-metabolism-related genes (FABP5, EMILIN1, GLCE, PHF11 and PALM3) based prognostic signature for EwS patients and assisted in gaining insight into prognosis to improve therapies further.

The prognostic value of autophagy related genes with potential protective function in Ewing sarcoma

Background

Ewing sarcoma (ES) is the second most common primary malignant bone tumor mainly occurring in children, adolescents and young adults with high metastasis and mortality. Autophagy has been reported to be involved in the survival of ES, but the role remains unclear. Therefore, it’s necessary to investigate the prognostic value of autophagy related genes using bioinformatics methods.

Results

ATG2B, ATG10 and DAPK1 were final screened genes for a prognostic model. KM and risk score plots showed patients in high score group had better prognoses both in training and validation sets. C-indexes of the model for training and validation sets were 0.68 and 0.71, respectively. Calibration analyses indicated the model had high prediction accuracy in training and validation sets. The AUC values of ROC for 1-, 3-, 5-year prediction were 0.65, 0.73 and 0.84 in training set, 0.88, 0.73 and 0.79 in validation set, which suggested high prediction accuracy of the model. Decision curve analyses showed that patients could benefit much from the model. Differential and functional analyses suggested that autophagy and apoptosis were upregulated in high risk score group.

Conclusions

ATG2B, ATG10 and DAPK1 were autophagy related genes with potential protective function in ES. The prognostic model established by them exhibited excellent prediction accuracy and discriminatory capacities. They might be used as potential prognostic biomarkers and therapeutic targets in ES.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04849-x.