Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and has long been among the top three cancers that cause the most deaths worldwide. Therapeutic options for HCC are limited due to the pronounced tumor heterogeneity. Thus, there is a critical need to study HCC from a systems point of view to discover effective therapeutic targets, such as through the systematic study of disease perturbation in both regulation and metabolism using a unified model. Such integration makes sense for cancers as it links one of the dominant physiological features of cancers (growth, which is driven by metabolic networks) with the primary available omics data source, transcriptomics (which is systematically integrated with metabolism through the regulatory-metabolic network model). Here, we developed an integrated transcriptional regulatory-metabolic model for HCC molecular stratification and the prediction of potential therapeutic targets. To predict transcription factors (TFs) and target genes affecting tumorigenesis, we used two algorithms to reconstruct the genome-scale transcriptional regulatory networks for HCC and normal liver tissue. which were then integrated with corresponding constraint-based metabolic models. Five key TFs affecting cancer cell growth were identified. They included the regulator CREB3L3, which has been associated with poor prognosis. Comprehensive personalized metabolic analysis based on models generated from data of liver HCC in The Cancer Genome Atlas revealed 18 genes essential for tumorigenesis in all three subtypes of patients stratified based on the non-negative matrix factorization method and two other genes (ACADSB and CMPK1) that have been strongly correlated with lower overall survival subtype. Among these 20 genes, 11 are targeted by approved drugs for cancers or cancer-related diseases, and six other genes have corresponding drugs being evaluated experimentally or investigationally. The remaining three genes represent potential targets. We also validated the stratification and prognosis results by an independent dataset of HCC cohort samples (LIRI-JP) from the International Cancer Genome Consortium database. In addition, microRNAs targeting key TFs and genes were also involved in established cancer-related pathways. Taken together, the multi-scale regulatory-metabolic model provided a new approach to assess key mechanisms of HCC cell proliferation in the context of systems and suggested potential targets.

Decreased Expression of ACADSB Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma

“…Recent studies have found that ACADSB plays an important role in the development and progression of malignant diseases, such as glioma ( 2 ), colorectal cancer ( 3 ), and hepatocellular carcinoma (HCC) ( 4 , 5 ). Yu et al.…”

“…Short/branched chain acyl-CoA dehydrogenase (ACADSB) is a member of the acyl-CoA dehydrogenase family of enzymes, which is involved in the metabolism of fatty acids (FAs) and branch-chained amino acids (BCAAs) (1). Previous studies have revealed that ACADSB plays an important role in glioma, colorectal cancer (CRC), and hepatocellular carcinoma (HCC) (2)(3)(4)(5). However, the role of ACADSB in clear cell renal cell carcinoma (ccRCC) has not yet been reported.…”

Hepatocellular carcinoma after treatment of hepatitis C with direct-acting antivirals: a critical re-appraisal

“…Transcriptomics identified three subtypes associated with three hepatocellular metabolic pathways: the WNT/β-catenin pathway, telomere maintenance, and several subtype-specific genes [140,141] . Proteomics also classified subtypes of HCC while integrating with transcriptomic data and identified two genes associated with poor survival in addition to 18 tumorigenesis genes [142,143] . Integration of data also revealed that changes caused by the MYC oncogene were implicated in vascular invasion [144] .…”

Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers

“…The GI tumors are homogeneous to some extent; almost all GI cancers tend to have subtypes of mesenchymal gene expression, immune infiltration, or metabolism ( Bijlsma et al., 2017 ). On the other hand, cancer is a highly heterogeneous disease, different morphological and phenotypic features of different tumor regions resulting from the heterogeneity often cause drug resistance, fosters lethal outcomes, and the therapeutic failure ( McGranahan and Swanton, 2017 ; Sun et al., 2020 ). Patients with the same type of cancer undergoing the same treatment have been shown to experience different prognosis.…”