School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran.
PLoS One. 2024 Jul 26;19(7):e0306220. doi: 10.1371/journal.pone.0306220. eCollection 2024.
Understanding the specific type of brain malignancy, source of brain metastasis, and underlying transformation mechanisms can help provide better treatment and less harm to patients. The tumor microenvironment plays a fundamental role in cancer progression and affects both primary and metastatic cancers. The use of single-cell RNA sequencing to gain insights into the heterogeneity profiles in the microenvironment of brain malignancies is useful for guiding treatment decisions. To comprehensively investigate the heterogeneity in gliomas and brain metastasis originating from different sources (lung and breast), we integrated data from three groups of single-cell RNA-sequencing datasets obtained from GEO. We gathered and processed single-cell RNA sequencing data from 90,168 cells obtained from 17 patients. We then employed the R package Seurat for dataset integration. Next, we clustered the data within the UMAP space and acquired differentially expressed genes for cell categorization. Our results underscore the significance of macrophages as abundant and pivotal constituents of gliomas. In contrast, lung-to-brain metastases exhibit elevated numbers of AT2, cytotoxic CD4+ T, and exhausted CD8+ T cells. Conversely, breast-to-brain metastases are characterized by an abundance of epithelial and myCAF cells. Our study not only illuminates the variation in the TME between brain metastasis with different origins but also opens the door to utilizing established markers for these cell types to differentiate primary brain metastatic cancers.
了解脑恶性肿瘤的具体类型、脑转移的来源以及潜在的转化机制,可以帮助为患者提供更好的治疗和更少的伤害。肿瘤微环境在癌症进展中起着根本作用,影响原发性和转移性癌症。使用单细胞 RNA 测序来深入了解脑恶性肿瘤微环境中的异质性特征,有助于指导治疗决策。为了全面研究源自不同来源(肺和乳腺)的脑胶质瘤和脑转移的异质性,我们整合了来自 GEO 的三组单细胞 RNA 测序数据集的数据。我们收集并处理了来自 17 名患者的 90168 个细胞的单细胞 RNA 测序数据。然后,我们使用 R 包 Seurat 进行数据集整合。接下来,我们在 UMAP 空间内对数据进行聚类,并获取差异表达基因进行细胞分类。我们的研究结果强调了巨噬细胞作为脑胶质瘤中丰富且重要组成部分的重要性。相比之下,肺到脑的转移中存在更多的 AT2、细胞毒性 CD4+T 和耗竭 CD8+T 细胞。相反,乳腺到脑的转移则以大量上皮细胞和肌成纤维细胞为主。我们的研究不仅揭示了不同来源的脑转移中 TME 的变化,还为利用这些细胞类型的已建立标记物来区分原发性脑转移性癌症开辟了道路。