系统性转录组分析儿童髓母细胞瘤，识别与分子亚型、免疫细胞浸润和预后相关的N6-甲基腺苷依赖性lncRNA签名
Systematic transcriptomic analysis of childhood medulloblastoma identifies N6-methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis
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影响因子:5.7
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
DOI:
10.1186/s40478-024-01848-2
摘要
髓母细胞瘤是最常见的恶性儿童脑肿瘤，分为四个主要的分子亚组，但第3组和第4组肿瘤难以细分类别，预后较差。急需快速的临床诊断和预后检测方法，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，长链非编码RNA(lncRNA)在肿瘤进展中起核心作用，但其在髓母细胞瘤中的基因表达和临床结局影响尚不清楚。本研究分析了469例髓母细胞瘤的转录组数据，鉴定出与m6A调控蛋白共表达的lncRNA。采用LASSO-Cox分析，筛选出一组与总生存期显著相关的五基因m6A相关lncRNA签名(M6LSig)，并结合其建立预后临床列线图。利用67个m6A相关lncRNA的表达，采用XGBoost机器学习算法建立亚组分类模型，分类准确率超过90%，包括3组和4组样本。所有M6LSig基因均与肿瘤微环境中的至少一种免疫细胞类型的丰度显著相关，风险评分与CD4+ naïve T细胞正相关，与滤泡辅助T细胞和嗜酸性粒细胞负相关。敲低关键的m6A写入酶METTL3和METTL14在3组髓母细胞瘤细胞系(D425-Med)中不仅减少了细胞增殖，还上调了多种在体内分析中发现的M6LSig基因，提示这些签名基因在髓母细胞瘤中具有功能意义。本研究强调了m6A依赖性lncRNA在髓母细胞瘤预后和免疫反应中的关键作用，为临床快速应用的实际工具奠定了基础。
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.