Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis

• Main Authors: Zhongyang Li, Shang Cai, Huijun Li, Jincheng Gu, Ye Tian, Jianping Cao, Dong Yu, Zaixiang Tang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-03-01
• Series: Frontiers in Oncology
• Subjects: The Cancer Genome Atlas; low-grade glioma; bioinformatics; long non-coding RNA; radiosensitivity
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.622880/full

BackgroundLower-grade glioma (LGG) is a type of central nervous system tumor that includes WHO grade II and grade III gliomas. Despite developments in medical science and technology and the availability of several treatment options, the management of LGG warrants further research. Surgical treatment for LGG treatment poses a challenge owing to its often inaccessible locations in the brain. Although radiation therapy (RT) is the most important approach in this condition and offers more advantages compared to surgery and chemotherapy, it is associated with certain limitations. Responses can vary from individual to individual based on genetic differences. The relationship between non-coding RNA and the response to radiation therapy, especially at the molecular level, is still undefined.MethodsIn this study, using The Cancer Genome Atlas dataset and bioinformatics, the gene co-expression network that is involved in the response to radiation therapy in lower-grade gliomas was determined, and the ceRNA network of radiotherapy response was constructed based on three databases of RNA interaction. Next, survival analysis was performed for hub genes in the co-expression network, and the high-efficiency biomarkers that could predict the prognosis of patients with LGG undergoing radiotherapy was identified.ResultsWe found that some modules in the co-expression network were related to the radiotherapy responses in patients with LGG. Based on the genes in those modules and the three databases, we constructed a ceRNA network for the regulation of radiotherapy responses in LGG. We identified the hub genes and found that the long non-coding RNA, DRAIC, is a potential molecular biomarker to predict the prognosis of radiotherapy in LGG.