Identification of an RNA-Binding-Protein-Based Prognostic Model for Ewing Sarcoma

被引:11
|
作者
Chen, Yi [1 ,2 ]
Su, Huafang [3 ]
Su, Yanhong [1 ]
Zhang, Yifan [1 ,2 ]
Lin, Yingbo [1 ]
Haglund, Felix [1 ,2 ]
机构
[1] Karolinska Inst, Dept Oncol Pathol, S-17176 Stockholm, Sweden
[2] Karolinska Univ Hosp, Clin Pathol & Canc Diagnost, S-17176 Stockholm, Sweden
[3] Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiat & Med Oncol, Wenzhou 325000, Peoples R China
关键词
Ewing sarcoma; RNA-binding proteins; regulation network; prognosis prediction; risk model; GENE-EXPRESSION; CYTOSCAPE; TUMORS; TRANSLOCATION; TRANSCRIPTION; SURVIVAL; FAMILY; EWS;
D O I
10.3390/cancers13153736
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary Ewing sarcoma (ES) is an aggressive childhood tumor for which response to chemotherapy is central to long-term prognosis, but few prognostic markers have been identified. RNA-binding proteins (RBPs) are strong regulators of cell behavior, working, for example, through post-translational modifications of mRNA. In this study, we investigated whether patterns in the RBP levels were related to outcomes in ES patients. A total of three distinct patterns were recognized, and additional modelling suggested that 10 RPBs had predictive value, suggesting that this model could be used in a clinical setting to identify patients with a higher risk of mortality. RNA-binding proteins (RBPs) are important transcriptomic regulators and may be important in tumorigenesis. Here, we sought to investigate the clinical impact of RBPs for patients with Ewing sarcoma (ES). ES transcriptome signatures were characterized from four previously published cohorts and grouped into new training and validation cohorts. A total of three distinct subtypes were identified and compared for differences in patient prognosis and RBP signatures. Next, univariate Cox and Lasso regression models were used to identify hub prognosis-related RBPs and construct a prognostic risk model, and prediction capacity was assessed through time-dependent receiver operating characteristics (ROCs), Kaplan-Meier curves, and nomograms. Across the three RBP subtypes, 29 significant prognostic-associated RBP genes were identified, of which 10 were used to build and validate an RBP-associated prognostic risk model (RPRM) that had a stable predictive value and could be considered valuable for clinical risk-stratification of ES. A comparison with immunohistochemistry validation showed a significant association between overall survival and NSUN7 immunoreactivity, which was an independent favorable prognostic marker. The association of RBP signatures with ES clinical prognosis provides a strong rationale for further investigation into RBPs molecular mechanisms.
引用
收藏
页数:15
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