Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy.
Int J Mol Sci. 2022 Mar 31;23(7):3867. doi: 10.3390/ijms23073867.
The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).
代谢组学和转录组学在癌细胞内相互交流，这种相互作用转化为基因表达和代谢物丰度水平之间存在可量化的相关结构。研究这些相关性可以为理解癌症以及发现新的生物标志物和药理学策略提供新的途径，同时也为利用更广泛的转录组学数据信息来预测代谢物数量奠定基础。在本文中，我们研究了癌症细胞系百科全书数据集（Cancer Cell Line Encyclopedia dataset）中基因表达和代谢物水平之间的相关性，在这两种分子组合之间构建了一个直接的相关网络。我们表明，代谢物/转录物相关网络可用于预测不同样本和数据集（如 NCI-60 癌细胞系数据集）中的代谢物水平，既可以进行逐个样本的预测，也可以进行差异对比的预测。我们还表明，在理论上，对于任何有转录组学数据的样本和数据集，如癌症基因组图谱（Cancer Genome Atlas，TCGA），都可以预测代谢物水平。
