Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis.
Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis.
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使用共表达和 ceRNA 网络分析开发 lncRNA 特征来预测低级别胶质瘤的放射治疗反应
DOI：
10.3389/fonc.2021.622880
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发表时间：
2021
影响因子：
4.7
通讯作者：
Tang Z
中科院分区：
文献类型：
--
作者：
Li Z;Cai S;Li H;Gu J;Tian Y;Cao J;Yu D;Tang Z
背景低级别胶质瘤是一种中枢神经系统肿瘤，包括WHO II级和III级胶质瘤。尽管医学科学和技术的发展和几种治疗方案的可用，但对LGG的管理需要进一步研究。LGG治疗的外科治疗是一个挑战，因为它通常无法到达大脑中的位置。虽然放射治疗(RT)是治疗这种疾病最重要的方法，与手术和化疗相比具有更多的优势，但它也有一定的局限性。根据遗传差异，每个人的反应可能会有所不同。非编码RNA和放射治疗反应之间的关系，特别是在分子水平上的关系，仍然是未知的。方法利用肿瘤基因组图谱数据集和生物信息学方法，确定参与低级别胶质瘤放射治疗反应的基因共表达网络，并基于3个RNA相互作用的数据库构建放射治疗反应的CENA网络。接下来，对共表达网络中的HUB基因进行生存分析，确定可以预测接受放射治疗的LGG患者预后的高效生物标志物。结果我们发现共表达网络中的一些模块与LGG患者的放射治疗反应有关。基于这些模块中的基因和三个数据库，我们构建了一个用于调节LGG放射治疗反应的CENA网络。我们鉴定了HUB基因，发现长非编码RNA DRAIC是预测LGG放射治疗预后的潜在分子生物学标志物。
Background Lower-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. Methods In 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. Results We 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.
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影响因子：
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DOI：
10.1016/j.nicl.2018.10.014
发表时间：
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期刊：
NeuroImage. Clinical
影响因子：
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影响因子：
48
作者：
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