OMICS. 2022 Apr 07.
A major problem in medicine and oncology is cancer recurrence through the activation of dormant cancer cells. A system scale examination of metabolic dysregulations associated with the cancer dormancy offers promise for the discovery of novel molecular targets for cancer precision medicine, and importantly, for the prevention of cancer recurrence. In this study, we mapped the total mRNA sequencing-based transcriptomic data from dormant cancer cell lines and nondormant cancer controls onto a human genome-scale metabolic network by using a graph-based approach, and two mass balance-based approaches with one based on reaction activity/inactivity and the other one on flux changes. The gene expression datasets were accessed from Gene Expression Omnibus (GSE83142 and GSE114012). This analysis included two diverse cancer types, a liquid and a solid cancer, namely, acute lymphoblastic leukemia and colorectal cancer. For the dormant cancer state, we observed changes in major adenosine triphosphate-producing pathways, including the citric acid cycle, oxidative phosphorylation, and glycolysis/gluconeogenesis, indicating a reprogramming in the metabolism of dormant cells away from Warburg-based energy metabolism. All three computational approaches unanimously predicted that folate metabolism, pyruvate metabolism, and glutamate metabolism, as well as valine/leucine/isoleucine metabolism are likely dysregulated in cancer dormancy. These findings provide new insights and molecular pathway targets on cancer dormancy, comprehensively catalog dormancy-associated metabolic pathways, and inform future research aimed at prevention of cancer recurrence in particular. This work does not include any human subjects. We used data from literature, and they were cell-line data. Therefore, we do not have any IRB or Clinical Registration.
Keywords: bioinformatics; cancer dormancy; cancer drug discovery; metabolic network models; preventive medicine; transcriptome