Glycolysis-Metabolism-Related Prognostic Signature for Ewing Sarcoma Patients.

IF 2.4 4区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Molecular Biotechnology Pub Date : 2024-10-01 Epub Date: 2023-09-29 DOI:10.1007/s12033-023-00899-5

Fusen Jia, Lei Liu, Qi Weng, Haiyang Zhang, Xuesheng Zhao

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Abstract

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.

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尤因肉瘤患者糖酵解代谢相关预后特征。

尤因肉瘤是一种发生在骨骼和软组织中的恶性肉瘤，常见于存活率低的儿童。细胞代谢的变化，如糖酵解，可能为肿瘤的转化和进展提供环境。我们的目的是建立一个基于糖酵解和代谢基因预测EwS患者预后的模型。通过基于GSE17679、GSE17674和ICGC数据集的差异基因表达分析获得候选基因。我们对候选基因进行了GO和KEGG途径富集分析。单变量Cox和LASSO Cox回归分析用于构建计算风险评分的模型。GSEA在高危组和低危组之间进行。CIBERSORT应用于免疫景观分析。我们得到了295个候选糖酵解代谢相关基因，这些基因在620个GO术语和18个KEGG途径中富集。通过单变量Cox模型选择12个基因，其中5个基因通过LASSO Cox回归分析确定用于构建风险评分模型。风险评分可以被认为是一个独立的预后因素。高风险组和低风险组的免疫景观和免疫检查点的表达存在显著差异。我们的研究构建了一个新的基于糖酵解代谢相关基因（FABP5、EMILIN1、GLCE、PHF11和PALM3）的EwS患者预后标志，并有助于深入了解预后，以进一步改进治疗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular Biotechnology 医学-生化与分子生物学

CiteScore

4.10

自引率

3.80%

发文量

165

审稿时长

6 months

期刊介绍： Molecular Biotechnology publishes original research papers on the application of molecular biology to both basic and applied research in the field of biotechnology. Particular areas of interest include the following: stability and expression of cloned gene products, cell transformation, gene cloning systems and the production of recombinant proteins, protein purification and analysis, transgenic species, developmental biology, mutation analysis, the applications of DNA fingerprinting, RNA interference, and PCR technology, microarray technology, proteomics, mass spectrometry, bioinformatics, plant molecular biology, microbial genetics, gene probes and the diagnosis of disease, pharmaceutical and health care products, therapeutic agents, vaccines, gene targeting, gene therapy, stem cell technology and tissue engineering, antisense technology, protein engineering and enzyme technology, monoclonal antibodies, glycobiology and glycomics, and agricultural biotechnology.