The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
IET Syst Biol. 2023 Oct;17(5):259-270. doi: 10.1049/syb2.12074. Epub 2023 Jul 29.
Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma-associated macrophages and analyse their correlation with clinical features and immune infiltration.
We analysed single-cell RNA sequencing (scRNA-seq) data from two paired meningioma and normal meninges to identify meningioma-associated macrophages. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was employed to identify module genes linked to these macrophages, followed by functional enrichment and pseudotime trajectory analyses. A machine learning-based model using the module genes was developed to predict tumour grades. Finally, meningiomas were classified into two molecular subtypes based on the module genes, followed by a comparison of clinical characteristics and immune cell infiltration.
Meningiomas exhibited a significantly higher proportion of macrophages than normal meninges, including novel macrophage clusters referred to as meningioma-associated macrophages. The hdWGCNA analysis of macrophages within meningiomas unveiled 12 distinct modules, with the blue, black, and turquoise modules closely correlated with the meningioma-associated macrophages. Hub genes within these modules were enriched in immune regulation, cellular communication, and metabolism pathways. Machine learning analysis identified 13 module genes (RSBN1, TIPRL, ATIC, SPP1, MALSU1, CDK1, MGP, DDIT3, SUPT16H, NFKBIA, SRSF5, ATXN2L, and UBB) strongly correlated with meningioma grade and constructed a predictive model with high accuracy and robustness. Based on the module genes, meningiomas were classified into two subtypes with distinct clinical and tumour microenvironment characteristics.
Our findings provide insights into the molecular characteristics underlying macrophage infiltration in meningiomas. The molecular signatures of macrophages demonstrate correlations with clinical features and immune cell infiltration in meningiomas.
脑膜瘤是常见的原发性脑肿瘤，巨噬细胞在其发生和发展中起着至关重要的作用。本研究旨在鉴定与脑膜瘤相关巨噬细胞相关的模块基因，并分析它们与临床特征和免疫浸润的相关性。
我们分析了来自两个配对的脑膜瘤和正常脑膜的单细胞 RNA 测序 (scRNA-seq) 数据，以鉴定脑膜瘤相关巨噬细胞。采用高维加权基因共表达网络分析 (hdWGCNA) 鉴定与这些巨噬细胞相关的模块基因，然后进行功能富集和伪时间轨迹分析。基于模块基因开发了一种机器学习模型来预测肿瘤分级。最后，根据模块基因将脑膜瘤分为两种分子亚型，然后比较临床特征和免疫细胞浸润。
脑膜瘤中巨噬细胞的比例明显高于正常脑膜，包括称为脑膜瘤相关巨噬细胞的新型巨噬细胞簇。脑膜瘤内巨噬细胞的 hdWGCNA 分析揭示了 12 个不同的模块，其中蓝色、黑色和绿松石模块与脑膜瘤相关巨噬细胞密切相关。这些模块中的枢纽基因富集在免疫调节、细胞通讯和代谢途径中。机器学习分析鉴定出 13 个与脑膜瘤分级密切相关的模块基因 (RSBN1、TIPRL、ATIC、SPP1、MALSU1、CDK1、MGP、DDIT3、SUPT16H、NFKBIA、SRSF5、ATXN2L 和 UBB)，并构建了一个具有高精度和稳健性的预测模型。基于模块基因，脑膜瘤被分为具有不同临床和肿瘤微环境特征的两种亚型。
我们的研究结果提供了对脑膜瘤中巨噬细胞浸润的分子特征的深入了解。巨噬细胞的分子特征与脑膜瘤的临床特征和免疫细胞浸润相关。
