A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction.
A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction.
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DOI：
10.3389/fonc.2020.590352
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发表时间：
2020
影响因子：
4.7
通讯作者：
Li H
中科院分区：
文献类型：
--
作者：
Niu X;Sun J;Meng L;Fang T;Zhang T;Jiang J;Li H
越来越多的研究已经证实长链非编码RNA（ncRNA）作为肿瘤诊断、治疗和预后预测的有利生物标志物的重要作用。本研究基于中国胶质瘤基因组图谱（CGGA）和美国癌症基因组图谱（TCGA）数据库，建立了一个基于多基因特征的胶质母细胞瘤（GBM）疗效和预后预测模型。使用来自TCGA和CGGA数据集的GBM的lncRNA-seq数据来鉴定与正常脑组织相比的差异表达基因（DEG）。然后将DEG用于单变量和多变量考克斯回归的生存分析。然后，我们建立了一个风险评分模型，依赖于多个生存相关DEG的基因签名。随后，Kaplan-Meier分析用于估计该模型的预后和预测作用。应用基因集富集分析（GSEA）技术，通过R软件包“cluster profile”和Wiki-pathway分析了与高风险评分相关的潜在途径。结果发现5个与GBM生存相关的lncRNA：LNC 01545、WDR 11-AS 1、NDUFA 6-DT、FRY-AS 1、TBX 5-AS 1。建立了GBM患者的风险评分模型，模型预测GBM患者的总生存期（OS），表明TCGA和CGGA队列中高风险评分与较低的OS显著相关。GSEA显示高风险评分富含PI 3 K-Akt、VEGFA-VEGFR 2、TGF-β、Notch、T细胞通路。总体而言，5种lncRNA特征衍生的风险评分在预测GBM的疗效和预后方面表现出令人满意的效果，对指导GBM的治疗策略和研究方向具有重要意义。
Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.
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影响因子：
21.3
作者：
Lin A;Li C;Xing Z;Hu Q;Liang K;Han L;Wang C;Hawke DH;Wang S;Zhang Y;Wei Y;Ma G;Park PK;Zhou J;Zhou Y;Hu Z;Zhou Y;Marks JR;Liang H;Hung MC;Lin C;Yang L
通讯作者：
Yang L
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
37.3
作者：
Peng Z;Liu C;Wu M
通讯作者：
Wu M
影响因子：
9.5
作者：
Jalili, Vahid;Matteucci, Matteo;Masseroli, Marco
通讯作者：
Masseroli, Marco
DOI：
10.1073/pnas.1424220112
发表时间：
2015-07-07
影响因子：
11.1
作者：
Pastori, Chiara;Kapranov, Philipp;Wahlestedt, Claes
通讯作者：
Wahlestedt, Claes