儿童髓母细胞瘤的系统转录组分析鉴定了与分子亚型相关的N6-甲基腺苷依赖性LNCRNA特征，免疫细胞浸润和预后
Systematic transcriptomic analysis of childhood medulloblastoma identifies N6-methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis
影响因子:5.70000
分区:医学1区 Top / 神经科学1区
发表日期:2024 Aug 28
作者:
Kandarp Joshi, Menglang Yuan, Keisuke Katsushima, Olivier Saulnier, Animesh Ray, Ernest Amankwah, Stacie Stapleton, George Jallo, Michael D Taylor, Charles G Eberhart, Ranjan J Perera
摘要
髓母细胞瘤是最常见的恶性小儿脑肿瘤，分为四个主要分子亚组，但是第3组和第4组肿瘤难以进行亚分析，预后较差。需要快速的护理诊断和预后测定，以改善髓母细胞瘤的风险分层和管理。 N6-甲基腺苷（M6A）是一种常见的RNA修饰，长的非编码RNA（LNCRNA）在肿瘤进展中起着核心作用，但是它们对髓母细胞瘤中基因表达和相关临床结果的影响尚不清楚。在这里，我们分析了469个髓母细胞瘤肿瘤转录组，以鉴定与M6A调节剂共表达的LNCRNA。使用套索-Cox分析，我们确定了与总生存期显着相关的五基因M6a相关的LNCRNA特征（M6LSIG），该生存率合并为预后的临床nom图。使用67 M6a相关的LNCRNA的表达，使用XGBoost机器学习算法生成亚组分类模型，该算法的分类精度> 90％，包括3组和4个样本。所有M6LSIG基因在肿瘤微环境中至少与一种免疫细胞类型丰度显着相关，并且风险评分与CD4+幼稚的T细胞丰度呈正相关，并且与卵泡助理助理T细胞和嗜酸性粒细胞呈负相关。在3组髓母细胞瘤细胞系（D425-MED）中，关键M6A作者基因Mettl3和Mettl14的敲低降低了细胞增殖，并上调了我们在Silico分析中鉴定的许多M6LSIG基因，这表明该签名基因在髓母细胞瘤中是功能性的。这项研究强调了M6A依赖性lncRNA在髓母细胞瘤预后和免疫反应中的关键作用，并为可以在临床环境中迅速部署的实用临床工具奠定了基础。
Abstract
Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4+ naïve T cell abundance and negatively correlated with follicular helper T cells and eosinophils. Knockdown of key m6A writer genes METTL3 and METTL14 in a group 3 medulloblastoma cell line (D425-Med) decreased cell proliferation and upregulated many M6LSig genes identified in our in silico analysis, suggesting that the signature genes are functional in medulloblastoma. This study highlights a crucial role for m6A-dependent lncRNAs in medulloblastoma prognosis and immune responses and provides the foundation for practical clinical tools that can be rapidly deployed in clinical settings.