Construction of a prognostic signature in Ewing's sarcoma: Based on metabolism-related genes
| Autor: | Mingxi Liu, Dong Zhu, Zhaoyu Fu, Bo Wu, Hongyu Wang, Yuting Jiang, Yuanyuan Hou, Bo Yu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Multivariate statistics Framingham Risk Score business.industry Proportional hazards model Bioinformatics Univariate Ewing's sarcoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease Prognosis Internal medicine Linear regression medicine Sarcoma business Survival rate Metabolism-related genes RC254-282 Original Research |
| Zdroj: | Translational Oncology, Vol 14, Iss 12, Pp 101225-(2021) Translational Oncology |
| ISSN: | 1936-5233 |
| Popis: | Highlight • This study conducted a battery of bioinformation analysis to calculate novel prognostic markers based on metabolism-related DEGs and verified the new prognostic index on the basis of the prognostic models. • We use a series of algorithms and analyses to develop prognostic index based on MRGs analysis in Ewing's sarcoma, and ultimately identified 74 differentially expressed MRGs and 25 differentially expressed MRGs affecting prognosis. • We found the 18-MRGs markers can help judge the prognosis of Ewing's sarcoma clinically, thereby helping to individualize the treatment of patients in different groups, and provide a solid foundation for the treatment and prognosis of Ewing's sarcoma. Objective By combining the expression profiles of metabolism-related genes (MRGS) with clinical information, the expression quantities of MRGS and the influence on development and prognosis were systematically analyzed, so as to provide a theoretical basis for the clinical study on the prognosis of Ewing's sarcoma. Methods MRGs expression profiles of 64 patients with Ewing's sarcoma were obtained from GEO dataset. Univariate Cox regression analysis was used to identify metabolization-related differentially expressed genes (DEGs) related with prognosis in Ewing's sarcoma patients. Then, multivariate Cox analysis was used to calculate novel prognostic markers based on metabolism-related DEGs. Besides, We validate the model using ICGC datasets. Finally, the new prognostic index was verified on the basis of the prognostic models. Results Multivariate Cox regression analysis identified 74 metabolization-related DEGs, 25 of which were associated with Ewing's sarcoma patients' overall survival. Subsequently, we used 25 DEGs to construct metabolism-related prognostic signature for patients with Ewing's sarcoma. Based on the 18 DEGs regression coefficient, we propose the formula of each patient's risk score, and then divided the patients into high-risk group and low-risk group. The results indicated that the survival rate and survival time were higher in the low-risk group and lower in the high-risk group. Multivariate Cox analysis showed that risk score index was an independent prognostic factor for Ewing's sarcoma. Conclusion The experimental results suggest that the 18 metabolism-related DEGs marker may be effective in predicting the prognosis of Ewing's sarcoma to some extent, helping to individualize treatment of patients at different risks. |
| Databáze: | OpenAIRE |
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