Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma.
Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma.
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DOI：
10.3389/fonc.2021.726745
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发表时间：
2021
影响因子：
4.7
通讯作者：
Zhao B
中科院分区：
文献类型：
--
作者：
Huang K;Yue X;Zheng Y;Zhang Z;Cheng M;Li L;Chen Z;Yang Z;Bian E;Zhao B
脑胶质瘤是中枢神经系统最常见、最具侵袭性的原发性恶性肿瘤。胶质瘤的分子亚型和预后标志物仍是一个有前途的研究领域。值得注意的是，间充质（MES）亚型相关长链非编码RNA（lncRNA）的异常表达与胶质瘤患者的预后显著相关。在这项研究中，MES相关基因从癌症基因组图谱（TCGA）和常春藤胶质母细胞瘤图谱项目（Ivy GAP）的神经胶质瘤数据集获得，并通过对这些基因进行共表达分析获得MES相关lncRNA。接下来，使用考克斯回归分析来建立预后模型，该模型整合了10个MES相关的lncRNA。根据中位风险评分将TCGA中的胶质瘤患者分为高风险组和低风险组;与低风险组相比，高风险组患者的生存时间较短。此外，我们用ROC曲线测量了我们模型的特异性和敏感性。单因素和多因素考克斯分析显示预后模式是胶质瘤的独立预后因素。为了验证这些候选lncRNA的预测能力，从中国胶质瘤基因组图谱（CGGA）下载相应的RNA-seq数据，获得了类似的结果。接下来，我们进行了两个风险组之间的患者的免疫细胞浸润概况，并进行基因集富集分析（GSEA）以检测功能注释。最后，选择保护因子DGCR 10和HAR 1B以及风险因子SNHG 18进行功能验证。敲低DGCR 10和HAR 1B促进胶质瘤的迁移和侵袭，而敲低SNHG 18抑制胶质瘤的迁移和侵袭。总的来说，我们成功地构建了一个基于10个MES相关的lncRNA标签的预后模型，这为预测胶质瘤患者的预后提供了一个新的靶点。
Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.
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