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Exploring Metabolic Consequences of CPS1 and CAD Dysregulation in Hepatocellular Carcinoma by Network Reconstruction

Authors Dumenci OE, U AMR, Khan SA, Holmes E, Taylor-Robinson SD

Received 18 November 2019

Accepted for publication 19 December 2019

Published 7 January 2020 Volume 2020:7 Pages 1—9


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Ahmed Kaseb

Ozbil E Dumenci, Abellona MR U, Shahid A Khan, Elaine Holmes, Simon D Taylor-Robinson

Imperial College London, London, UK

Correspondence: Abellona MR U
Imperial College London, 660 Sir Alexander Fleming Building, London SW7 2AZ, UK
Tel +44 7428 274231

Purpose: Hepatocellular carcinoma (HCC) is the fourth commonest cause of cancer-related mortality; it is associated with various genetic alterations, some involved in metabolic reprogramming. This study aimed to explore the potential metabolic impact of Carbamoyl Phosphate Synthase I (CPS1) and carbamoyl phosphate synthetase/aspartate transcarbamoylase/dihydroorotase (CAD) dysregulation through the reconstruction of a network that integrates information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Human Metabolome Database (HMDB) and Human Protein Atlas (HPA).
Methods and results: Existing literature was used to determine the roles of CPS1 and CAD in HCC. CPS1 downregulation is thought to play a role in hepatocarcinogenesis through an increased glutamine availability for de novo pyrimidine biosynthesis, which CAD catalyzes the first three steps for. KEGG, HMDB and HPA were used to reconstruct a network of relevant pathways, demonstrating the relationships between genes and metabolites using the MetaboSignal package in R. The network was filtered to exclude any duplicates, and those greater than three steps away from CPS1 or CAD. Consequently, a network of 18 metabolites, 28 metabolic genes and 1 signaling gene was obtained, which indicated expression profiles and prognostic information of each gene in the network.
Conclusion: Information from different databases was collated to form an informative network that integrated different “-omics” approaches, demonstrating the relationships between genetic and metabolic components of urea cycle and the de novo pyrimidine biosynthesis pathway. This study paves the way for further research by acting as a template to investigate the relationships between genes and metabolites, explore their potential roles in various diseases and aid the development of new screening and treatment methods through network reconstruction.

Keywords: metabonomics, reprogramming, bioinformatics, liquid biopsy, hepatocytes

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