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

Abstract

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/β-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.

Identification of a Subtype of Hepatocellular Carcinoma with Poor Prognosis Based on Expression of Genes within the Glucose Metabolic Pathway

“…However, Cluster2 tumors had decreased activation of pathways involved in cell death and apoptosis, fatty acid metabolism, amino acid metabolism, peroxisome metabolism, propanoate metabolism, drug metabolism, and other metabolic pathways. System biology analysis by Bidkhori et al based on TCGA data also identified a subtype of HCC patients (iHCC3) with worse survival [63], which had similar properties to our Cluster2 patients since both exhibited activation of pathways associated with tumor aggressiveness and down-regulation of multiple common metabolic pathways. Therefore, the deregulated gene signature in cluster 2 could be prognostic markers and therapeutic targets for highly aggressive HCCs.…”

The complexity of the serine glycine one-carbon pathway in cancer

“…Other enzymes of the SGOCP play a key role in tumorigenesis. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is consistently up-regulated in many cancer types, and its expression significantly correlates with poor clinical outcome in breast cancer, pancreatic carcinomas, renal cell carcinoma, and leukemia and in a particularly aggressive metabolic subtype of hepatocellular carcinoma (HCC; Bidkhori et al, 2018;Lehtinen et al, 2013;Lin et al, 2018;Liu et al, 2014;Nilsson et al, 2014;Noguchi et al, 2018;Reina-Campos et al, 2019;Tedeschi et al, 2015). MTHFD2 is a dual-action enzyme (dehydrogenase and cyclohydrolase) that catalyzes the reversible conversion of 5,10methylene-THF into 10-formyl-THF in the mitochondria, while MTHFD1, its cytosolic counterpart, catalyzes an extra reaction (synthetase) to convert 10-formyl-THF into THF and formate ( Fig.…”

“…MTHFD1L activity is important for embryogenesis and neural tube closure (Momb et al, 2013;Parle-McDermott et al, 2009) and seems to confer a metabolic advantage in HCC. But the extent to which this reflects a coordinated action with MTHFD2 is not yet known (Bidkhori et al, 2018;Lee et al, 2017). An intriguing aspect of MTHFD2 is its ability to use both NAD + and NADP + as cofactors to generate mitochondrial NADH and NADPH, respectively, while MTHFD1 can only use NADP + (Shin et al, 2017b; Fig.…”

Integrated regulatory-metabolic network model reveals critical mechanism and potential targets for Hepatocellular Carcinoma

“…The recent study by Bidkhori et al took a new beginning for HCC metabolism study, which utilized metabolic network topology analysis to divide 179 TCGA-LIHC samples into three subtypes and identify potential subtype-specific therapeutic targets (9). However, tumor development is far more complex which requires understanding of the complicated multi-level mechanisms including both regulation and metabolism.…”