Cellular Neuroscience, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
Charité-Universitätsmedizin Berlin, Berlin, Germany.
Cell Mol Neurobiol. 2021 Mar;41(2):365-375. doi: 10.1007/s10571-020-00857-8. Epub 2020 May 14.
Glioma is the most common and fatal primary brain tumor in human. Long non-coding RNA (lncRNA), which are characterized by regulation of gene expression and chromatin recombination play an important role in glioma, and immunotherapy is a promising cancer treatment. Therefore, it is necessary to identify Immune-related lncRNAs in glioma. In this study,we collected and evaluated the RNA-seq data of The Cancer Genome Atlas (TCGA, https://www.ncbi.nlm.nih.gov/ ) and Chinese Glioma Genome Atlas (CGGA, https://www.cgga.org.cn/ ) glioma patients and immune-related lncRNAs were screened. Cox regression and LASSO analysis were performed to construct a risk score formula to explor the different overall survival between high- and low-risk groups in TCGA and verified with CGGA. Gene ontology (GO) and pathway-enrichment analysis (KEGG) were performed to identify the function of screened genes. Co-expression network were performed of these genes for further analysis. Eleven immune-related lncRNAs were concerned to be involved in survival and adopted to construct the risk score formula. Patients with high-risk score held poor survival both in TCGA and CGGA. Compared with current clinical data, the Area Under Curve (AUC) of different years and Principal components analysis (PCA) suggested that the formula had better predictive power. Functional Annotation of immune-related lncRNAs showed that the differences overall survival of high and low RS group might be caused by the cell differentiation, microtubule polymerization, etc. We successfully constructed an immune-related lncRNAs formula with powerful predictive function, which provides certain guidance value to the analysis of glioma pathogenesis and clinical treatment, and potential therapeutic targets for glioma treatment.
神经胶质瘤是人类最常见和致命的原发性脑肿瘤。长链非编码 RNA（lncRNA）通过调节基因表达和染色质重组发挥重要作用，在神经胶质瘤中发挥重要作用，免疫疗法是一种有前途的癌症治疗方法。因此，有必要鉴定神经胶质瘤中的免疫相关 lncRNA。在这项研究中，我们收集和评估了癌症基因组图谱（TCGA，https://www.ncbi.nlm.nih.gov/）和中国神经胶质瘤基因组图谱（CGGA，https://www.cgga.org.cn/）神经胶质瘤患者的 RNA-seq 数据，并筛选了免疫相关 lncRNA。进行 Cox 回归和 LASSO 分析以构建风险评分公式，以探索 TCGA 中高低风险组之间的不同总生存率，并与 CGGA 进行验证。进行基因本体论（GO）和途径富集分析（KEGG）以确定筛选基因的功能。对这些基因进行共表达网络分析以进行进一步分析。有 11 个免疫相关 lncRNA 被认为与生存有关，并被采用来构建风险评分公式。TCGA 和 CGGA 中高风险评分的患者生存率均较差。与当前的临床数据相比，不同年份的曲线下面积（AUC）和主成分分析（PCA）表明该公式具有更好的预测能力。免疫相关 lncRNA 的功能注释表明，高和低 RS 组之间总体生存率的差异可能是由细胞分化、微管聚合等引起的。我们成功构建了一个具有强大预测功能的免疫相关 lncRNA 公式，为神经胶质瘤发病机制和临床治疗分析提供了一定的指导价值，并为神经胶质瘤治疗提供了潜在的治疗靶点。
