Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in human breast cancers
Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in human breast cancers
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DOI：
10.1016/j.ymthe.2021.03.003
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发表时间：
2021-07-07
影响因子：
12.4
通讯作者：
Di, Gen-Hong
中科院分区：
文献类型：
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作者：
Yu, Tian-Jian;Ma, Ding;Di, Gen-Hong
关于癌症代谢的一个新兴观点是，它是异质性和特定背景的，但它仍有待阐明乳腺癌。在这项研究中，我们通过基因组学、转录组学、代谢组学和单细胞转录组分析对多个数据集进行综合分析，表征了乳腺癌的能量相关代谢特征。能量相关的代谢特征被用来将乳腺肿瘤分为两个预后聚类：聚类1表现出高的糖酵解活性和降低的存活率，聚类2的特征富含脂肪酸氧化和β-氨基丁酸分解。肿瘤间代谢的异质性通过三个独立的大队列之间的聚类反映，并且在代谢物水平上进一步验证了复杂性。此外，我们发现恶性细胞的代谢状态而不是非恶性细胞的代谢状态是单细胞分辨率的主要贡献者，并且其与来自肿瘤微环境的因子的相互作用是意料之外的。值得注意的是，在具有可区分代谢特征的各种免疫细胞及其簇中，具有免疫抑制功能的免疫细胞及其簇表现出更高的代谢活性。总的来说，我们使用具有预后和治疗价值的分类器揭示了能量代谢的异质性。单细胞转录组分析提供了新的代谢见解，最终可以根据患者或细胞类型特异性癌症代谢制定治疗策略。
An emerging view regarding cancer metabolism is that it is heterogeneous and context-specific, but it remains to be elucidated in breast cancers. In this study, we characterized the energyrelated metabolic features of breast cancers through integrative analyses of multiple datasets with genomics, transcriptomics, metabolomics, and single-cell transcriptome profiling. Energy-related metabolic signatures were used to stratify breast tumors into two prognostic clusters: cluster 1 exhibits high glycolytic activity and decreased survival rate, and the signatures of cluster 2 are enriched in fatty acid oxidation and glutaminolysis. The intertumoral metabolic heterogeneity was reflected by the clustering among three independent large cohorts, and the complexity was further verified at the metabolite level. In addition, we found that the metabolic status of malignant cells rather than that of nonmalignant cells is the major contributor at the single-cell resolution, and its interactions with factors derived from the tumor microenvironment are unanticipated. Notably, among various immune cells and their clusters with distinguishable metabolic features, those with immunosuppressive function presented higher metabolic activities. Collectively, we uncovered the heterogeneity in energy metabolism using a classifier with prognostic and therapeutic value. Single-cell transcriptome profiling provided novel metabolic insights that could ultimately tailor therapeutic strategies based on patient- or cell type-specific cancer metabolism.