Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis

EBioMedicine. 2019 Feb;40:471-487. doi: 10.1016/j.ebiom.2018.12.057. Epub 2018 Dec 31.

Abstract

Background: Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing.

Methods: Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data.

Findings: We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs -dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups.

Interpretation: Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC.

Keywords: Cancer; Hepatocellular carcinoma; Liver cancer; Precision medicine; Redox metabolism; Systems biology; Transcriptomics.

MeSH terms

  • Algorithms
  • Biomarkers, Tumor
  • Carcinoma, Hepatocellular / genetics
  • Carcinoma, Hepatocellular / metabolism*
  • Carcinoma, Hepatocellular / mortality
  • Carcinoma, Hepatocellular / pathology
  • Computational Biology / methods
  • Disease Progression
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Humans
  • Liver Neoplasms / genetics
  • Liver Neoplasms / metabolism*
  • Liver Neoplasms / mortality
  • Liver Neoplasms / pathology
  • Metabolic Networks and Pathways
  • Models, Biological*
  • Molecular Sequence Annotation
  • Oxidation-Reduction*
  • Prognosis
  • Reproducibility of Results
  • Signal Transduction
  • Transcriptome

Substances

  • Biomarkers, Tumor