单细胞RNA测序鉴定了与脑膜瘤临床特征和肿瘤微环境相关的巨噬细胞特征。

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Xiaowei Zhang
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引用次数: 0

摘要

背景:脑膜瘤是常见的原发性脑肿瘤,巨噬细胞在其发育和进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,并分析其与临床特征和免疫浸润的相关性。方法:我们分析了两对脑膜瘤和正常脑膜瘤的单细胞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)来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假时间轨迹分析。利用模块基因开发了一个基于机器学习的模型来预测肿瘤等级。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。结果:脑膜瘤的巨噬细胞比例明显高于正常脑膜,包括被称为脑膜瘤相关巨噬细胞的新型巨噬细胞簇。对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、黑色和绿松石色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在免疫调节、细胞通讯和代谢途径中富集。机器学习分析确定了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准确性和稳健性的预测模型。根据模块基因,脑膜瘤分为两种亚型,具有不同的临床和肿瘤微环境特征。结论:我们的发现为脑膜瘤巨噬细胞浸润的分子特征提供了见解。巨噬细胞的分子特征显示与脑膜瘤的临床特征和免疫细胞浸润有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Single-cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas

Single-cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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