Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

Cancers

Edoardo Saccenti¹  Derek van Tilborg¹ 

[1] Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng, 6708 WE Wageningen, The Netherlands;

关键词: association networks;    cancer metabolism;    biological networks analysis;    gaussian graphical models;    pathway analysis;   

DOI  :  10.3390/cancers13030393

来源: DOAJ

【 摘 要 】

One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

【 授权许可】

Unknown