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Prognostic value of GLCE and infiltrating immune cells in Ewing sarcoma

BACKGROUND: The prognostic value of D-glucuronyl C5-epimerase (GLCE) and mast cell infiltration in Ewing sarcoma (ES) has not been well specified and highlighted, which may facilitate survival prediction and treatment. METHODS: Several qualified datasets were downloaded from the GEO website. Common...

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Detalles Bibliográficos
Autores principales: Wen, Jian, Yi, Lijun, Wan, Lijia, Dong, Xieping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474439/
https://www.ncbi.nlm.nih.gov/pubmed/37662777
http://dx.doi.org/10.1016/j.heliyon.2023.e19357
Descripción
Sumario:BACKGROUND: The prognostic value of D-glucuronyl C5-epimerase (GLCE) and mast cell infiltration in Ewing sarcoma (ES) has not been well specified and highlighted, which may facilitate survival prediction and treatment. METHODS: Several qualified datasets were downloaded from the GEO website. Common differentially expressed genes between normal subjects and ES patients in GSE17679, GSE45544, and GSE68776 were identified and screened by multiple algorithms to find hub genes with prognostic value. The prognostic value of 64 infiltrating cells was also explored. A prognostic model was established and then validated with GSE63155 and GSE63156. Finally, functional analysis was performed. RESULTS: GLCE and mast cell infiltration were screened as two indicators for a prognostic model. The Kaplan‒Meier analysis showed that patients in the low GLCE expression, mast cell infiltration and risk score groups had poorer outcomes than patients in the high GLCE expression, mast cell infiltration and risk score groups, both in the training and validation sets. Scatter plots and heatmaps also indicated the same results. The concordance indices and calibration analyses indicated a high prediction accuracy of the model in the training and validation sets. The time-dependent receiver operating characteristic analyses suggested high sensitivity and specificity of the model, with area under the curve values between 0.76 and 0.98. The decision curve analyses suggested a significantly higher net benefit by the model than the treat-all and treat-none strategies. Functional analyses suggested that glycosaminoglycan biosynthesis-heparan sulfate/heparin, the cell cycle and microRNAs in cancer were upregulated in ES patients. CONCLUSIONS: GLCE and mast cell infiltration are potential prognostic indicators in ES. GLCE may affect the proliferation, angiogenesis and metastasis of ES by affecting the biosynthesis of heparan sulfate and heparin.