Xiao Yong, Zhao Mengjie, Wang Ran, Liu Liang, Xiang Chong, Li Taiping, Qian Chunfa, Xiao Hong, Liu Hongyi, Zou Yuanjie, Tang Xianglong, Yang Kun
Department of Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China.
Department of Neuro-Psychiatric Institute, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China.
Heliyon. 2024 Dec 21;11(1):e41241. doi: 10.1016/j.heliyon.2024.e41241. eCollection 2025 Jan 15.
Emerging perspectives on tumor metabolism reveal its heterogeneity, a characteristic yet to be fully explored in gliomas. To advance therapies targeting metabolic processes, it is crucial to uncover metabolic differences and identify distinct metabolic subtypes. Therefore, we aimed to develop a classification system for gliomas based on the enrichment levels of four key metabolic pathways: glutaminolysis, glycolysis, the pentose phosphate pathway, and fatty acid oxidation.
Energy-related features of glioma were characterized through integrative analyses of multiple datasets, including bulk, single-cell, and spatial transcriptome profiling. The glioma energy metabolic subtypes were constructed using the R package ConsensusClusterPlus. Kaplan-Meier analysis was conducted to compare clinical outcomes between different metabolic groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to elucidate the biological functions of genes of interest. Cell-cell communication analysis was performed at single-cell resolution using the R package CellChat and at spatial resolution using the standard stLearn pipeline.
Glioma samples were stratified into two prognostic subtypes. Group 1, enriched in the glutaminolysis pathway, had better clinical outcomes. In contrast, Group 2 exhibited high activities in glycolysis, the pentose phosphate pathway, and fatty acid oxidation, correlating with decreased survival time. Group 1 samples were predominantly located in the peripheral region and had a high composition of neuron cells. Group 2, however, had increased infiltration of tumor-promoting immune cells, such as M2 macrophages, and was characterized by traits of invasion, hypoxia, and immunity. Lastly, cell-cell communications were compared across different tumor regions, and the / ligand-receptor pair was validated using spatial transcriptomic data.
Our work revealed the metabolic heterogeneity in glioma by developing a new classification system with significant prognostic and therapeutic value. Single-cell transcriptional profiles offer novel insights into tumor metabolic reprogramming, which could enhance therapies tailored to cell- or patient-specific metabolic patterns.
肿瘤代谢的新观点揭示了其异质性，这一特征在胶质瘤中尚未得到充分探索。为了推进针对代谢过程的治疗，揭示代谢差异并确定不同的代谢亚型至关重要。因此，我们旨在基于谷氨酰胺分解、糖酵解、磷酸戊糖途径和脂肪酸氧化这四种关键代谢途径的富集水平，开发一种胶质瘤分类系统。
通过对多个数据集进行综合分析来表征胶质瘤的能量相关特征，这些数据集包括批量、单细胞和空间转录组分析。使用R包ConsensusClusterPlus构建胶质瘤能量代谢亚型。进行Kaplan-Meier分析以比较不同代谢组之间的临床结果。采用基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析来阐明感兴趣基因的生物学功能。使用R包CellChat在单细胞分辨率下以及使用标准的stLearn管道在空间分辨率下进行细胞间通讯分析。
胶质瘤样本被分为两种预后亚型。第1组富含谷氨酰胺分解途径，具有较好的临床结果。相比之下，第2组在糖酵解、磷酸戊糖途径和脂肪酸氧化方面表现出高活性，与生存时间缩短相关。第1组样本主要位于周边区域，神经元细胞组成比例高。然而，第2组促进肿瘤的免疫细胞（如M2巨噬细胞）浸润增加，其特征为侵袭、缺氧和免疫相关特征。最后，比较了不同肿瘤区域的细胞间通讯，并使用空间转录组数据验证了配体-受体对。
我们的工作通过开发一种具有显著预后和治疗价值的新分类系统，揭示了胶质瘤中的代谢异质性。单细胞转录谱为肿瘤代谢重编程提供了新见解，这可能会增强针对细胞或患者特异性代谢模式的治疗。
