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Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes

Journal article
Authors G. Bidkhori
R. Benfeitas
Martina Klevstig
C. Zhang
J. Nielsen
M. Uhlen
Jan Borén
A. Mardinoglu
Published in Proceedings of the National Academy of Sciences of the United States of America
Volume 115
Issue 50
Pages E11874-E11883
ISSN 0027-8424
Publication year 2018
Published at Institute of Medicine, Department of Molecular and Clinical Medicine
Pages E11874-E11883
Language en
Keywords hepatocellular carcinoma, biological networks, personalized medicine, genome-scale metabolic, gene-expression, pathway, catenin, classification, activation, prediction, survival, biology, model
Subject categories Medical Genetics, Cancer and Oncology


Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/beta-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

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