Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma
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Fengdan Ye1,2,*, Dongya Jia2,3,*, Mingyang Lu4, Herbert Levine1,2,5,6 and Michael W. Deem1,2,3,5
1Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA
2Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
3Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX 77005, USA
4The Jackson Laboratory, Bar Harbor, ME 04609, USA
5Department of Bioengineering, Rice University, Houston, TX 77005, USA
6Department of Biosciences, Rice University, Houston, TX 77005, USA
Michael W. Deem, email: [email protected]
Herbert Levine, email: [email protected]
Keywords: modularity; metabolism; hepatocellular carcinoma; HCC; prognosis
Received: September 22, 2017 Accepted: February 10, 2018 Epub: February 22, 2018 Published: March 13, 2018
Abnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumors, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patients’ survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.
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