Nguyen Quang-Huy, Nguyen Tin, Le Duc-Hau
School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.
Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States.
Front Mol Biosci. 2022 Feb 14;9:801931. doi: 10.3389/fmolb.2022.801931. eCollection 2022.
It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A-lncRNAs in light of three state-of-the-art tools WGCNA, iWGCNA, and oCEM, and interrogated their prognostic values in brain low-grade gliomas (LGG). Of the 13 hub m6A-lncRNAs, we further detected three hub m6A-lncRNAs as independent prognostic risk factors, including and . Then, the m6ALncSig model was built based on these three hub m6A-lncRNAs. Patients with LGG next were divided into two groups, high- and low-risk, based on the median m6ALncSig score. As predicted, the high-risk group was more significantly related to mortality. The prognostic signature of m6ALncSig was validated using internal and external cohorts. In summary, our work introduces a high-confidence prognostic prediction signature and paves the way for using m6A-lncRNAs in the signature as new targets for treatment of LGG.
很明显，N6-甲基腺苷（m6A）修饰的长链非编码RNA（m6A-lncRNAs）参与调控多种癌症类型的肿瘤发生、侵袭和转移。在本研究中，我们试图根据三种先进工具WGCNA、iWGCNA和oCEM，通过计算筛选出一组13个枢纽m6A-lncRNAs，并研究它们在脑低级别胶质瘤（LGG）中的预后价值。在这13个枢纽m6A-lncRNAs中，我们进一步检测到三个枢纽m6A-lncRNAs作为独立的预后危险因素，包括 和 。然后，基于这三个枢纽m6A-lncRNAs构建了m6ALncSig模型。接下来，根据m6ALncSig评分中位数，将LGG患者分为高风险和低风险两组。正如预测的那样，高风险组与死亡率的相关性更显著。使用内部和外部队列验证了m6ALncSig的预后特征。总之，我们的工作引入了一个高可信度的预后预测特征，并为将特征中的m6A-lncRNAs用作LGG治疗的新靶点铺平了道路。
