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Abstract

Identification of a Prognostic Radiosensitivity Gene Signature in Low-Grade Gliomas

Author(s): H. Yao and R. W. Chen*
Department of Medicine, Anhui University of Science and Technology, Huainan, Anhui 232001, China

Correspondence Address:
R. W. Chen, Department of Medicine, Anhui University of Science and Technology, Huainan, Anhui 232001, China, E-mail: chenrw0910@163.com


Low-grade gliomas are typically slow-growing tumors of the central nervous system, which can transform into more aggressive types within a decade. Radiotherapy is an effective treatment for suppressing the development of aggressive tumors. The purpose of this study was to explore the characteristics of radiosensitivity genes and the modeling of prognostic risk in patients with low-grade glioma. The data in this study come from the cancer genome atlas and prognostic assessment model was constructed based on the coefficient values of selected genes in multivariate Cox proportional hazards regression. The probability of individual survival was then predicted using a nomogram. Differences in tumor immune microenvironment between high and low-risk groups were analyzed. We constructed a prognostic radiosensitivity-related gene signature for patients with low-grade gliomas. Kaplan-Meier survival curve analysis revealed a significantly better prognosis for low-risk group than for high-risk group (p<0.001) and receiver operating characteristic curves show accuracies of 0.869, 0.912 and 0.873 for 1, 3 and 5 y, respectively. Radiosensitivity-related gene signature was identified as a single prognostic indicator with hazard ratio=1.159 and 95 % confidence interval=1.102-1.219 (p<0.001). The immune-related analysis showed radiosensitivity-related gene signature with significant differences in radiosensitivity between high and low-risk groups. We identified thymosin beta 4 X-linked, insulin-like growth factor binding protein 5, moesin, ribophorin 2, cyclin dependent kinase inhibitor 2C and prostaglandin F2 receptor negative regulator as the gene signatures for predicting the prognosis of patients receiving radiotherapy in low-grade gliomas.

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