Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, and its carcinogenesis and progression are influenced by a complex network of gene interactions. A weighted gene co-expression network was constructed to identify gene modules associated with the clinical traits in HCC (n = 214). Among the 13 modules, high correlation was only found between the red module and metastasis risk (classified by the HCC metastasis gene signature) (R2 = −0.74). Moreover, in the red module, 34 network hub genes for metastasis risk were identified, six of which (ABAT, AGXT, ALDH6A1, CYP4A11, DAO and EHHADH) were also hub nodes in the protein-protein interaction network of the module genes. Thus, a total of six hub genes were identified. In validation, all hub genes showed a negative correlation with the four-stage HCC progression (P for trend < 0.05) in the test set. Furthermore, in the training set, HCC samples with any hub gene lowly expressed demonstrated a higher recurrence rate and poorer survival rate (hazard ratios with 95% confidence intervals > 1). RNA-sequencing data of 142 HCC samples showed consistent results in the prognosis. Gene set enrichment analysis (GSEA) demonstrated that in the samples with any hub gene highly expressed, a total of 24 functional gene sets were enriched, most of which focused on amino acid metabolism and oxidation. In conclusion, co-expression network analysis identified six hub genes in association with HCC metastasis risk and prognosis, which might improve the prognosis by influencing amino acid metabolism and oxidation.

[1]  Jianru Xiao,et al.  Hypoxia activates Wnt/β-catenin signaling by regulating the expression of BCL9 in human hepatocellular carcinoma , 2017, Scientific Reports.

[2]  Stephanie Roessler,et al.  A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. , 2010, Cancer research.

[3]  Ahmedin Jemal,et al.  Global Cancer Incidence and Mortality Rates and Trends—An Update , 2015, Cancer Epidemiology, Biomarkers & Prevention.

[4]  J. Lasker,et al.  Metabolism of arachidonic acid to 20-hydroxy-5,8,11, 14-eicosatetraenoic acid by P450 enzymes in human liver: involvement of CYP4F2 and CYP4A11. , 1998, The Journal of pharmacology and experimental therapeutics.

[5]  O. Lingjærde,et al.  AGXT and ERCC2 polymorphisms are associated with clinical outcome in metastatic colorectal cancer patients treated with 5-FU/oxaliplatin , 2015, The Pharmacogenomics Journal.

[6]  H. Maeda,et al.  Tumor-targeted delivery of polyethylene glycol-conjugated D-amino acid oxidase for antitumor therapy via enzymatic generation of hydrogen peroxide. , 2002, Cancer research.

[7]  T. Roskams,et al.  Predictive value of liver cell dysplasia for development of hepatocellular carcinoma in patients with non‐cirrhotic and cirrhotic chronic viral hepatitis , 2001, Histopathology.

[8]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[9]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[10]  R. Montioli,et al.  The N-terminal extension is essential for the formation of the active dimeric structure of liver peroxisomal alanine:glyoxylate aminotransferase. , 2012, The international journal of biochemistry & cell biology.

[11]  L. Pollegioni,et al.  Relevance of weak flavin binding in human D‐amino acid oxidase , 2009, Protein science : a publication of the Protein Society.

[12]  Andy M. Yip,et al.  Gene network interconnectedness and the generalized topological overlap measure , 2007, BMC Bioinformatics.

[13]  Zhenqing Feng,et al.  Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. , 2007, Oncology reports.

[14]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[15]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

[16]  M. Hayakari,et al.  Decreased expression of the peroxisomal bifunctional enzyme and carbonyl reductase in human hepatocellular carcinomas , 1999, Journal of Cancer Research and Clinical Oncology.

[17]  B. Sitek,et al.  Differential proteomic and tissue expression analyses identify valuable diagnostic biomarkers of hepatocellular differentiation and hepatoid adenocarcinomas , 2015, Pathology.

[18]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[19]  Angela P. Presson,et al.  Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome , 2008, BMC Systems Biology.

[20]  U. Hofmann,et al.  Activating and Inhibitory Functions of WNT/β-Catenin in the Induction of Cytochromes P450 by Nuclear Receptors in HepaRG Cells , 2015, Molecular Pharmacology.

[21]  X. Wang,et al.  Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning , 2003, Nature Medicine.

[22]  T. Ried,et al.  cDNA cloning of the human peroxisomal enoyl-CoA hydratase: 3-hydroxyacyl-CoA dehydrogenase bifunctional enzyme and localization to chromosome 3q26.3-3q28: a free left Alu Arm is inserted in the 3' noncoding region. , 1994, Genomics.

[23]  Sahil Mittal,et al.  Epidemiology of hepatocellular carcinoma: consider the population. , 2013, Journal of clinical gastroenterology.

[24]  S. Kim,et al.  Cysteine-321 of human brain GABA transaminase is involved in intersubunit cross-linking. , 2004, Molecules and cells.

[25]  J. Walker.  Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles , 2007 .

[26]  S. Ferdinandusse,et al.  Mutations in ALDH6A1 encoding methylmalonate semialdehyde dehydrogenase are associated with dysmyelination and transient methylmalonic aciduria , 2013, Orphanet Journal of Rare Diseases.