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Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

ABSTRACT: Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

SUBMITTER: Li C

PROVIDER: S-EPMC5896492 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

Li Chaoxing C Liu Li L Dinu Valentin V

PeerJ 20180409

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a norma ...[more]

PMID: 29666752

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Project description:Background:Hepatocellular carcinoma (HCC) is one of the most common malignant cancer worldwide. Importantly, the precise mechanisms causing HCC pathogenicity are still unknown. The identification of potential oncogenes plays significant roles in finding novel therapeutic targets for human HCC. Purpose:WDR12 (WD repeat protein 12), a member of WD repeats family, plays crucial roles in the ribosome biogenesis pathway. However, Whether WDR12 contributes to HCC development remains unknown. The objective of this study was to elucidate the role of WDR12 in HCC development. Methods:The expression level of WDR12 in HCC tissues and adjacent non-tumor tissues were detected form Gene Expression Omnibus (GEO) database. The expression level of WDR12 in HCC cell lines were examined by RT-PCR and western blot. Kaplan-Meier analysis were used to analyze the effect of WDR12 level on overall and disease-free survival of HCC patients. To examine whether WDR12 supports development of HCC, we inhibited expression of WDR12 by using an shRNA-encoding lentivirus system. Effects of WDR12 knockdown were evaluated on cell-growth, cell-proliferation and cell-migration. The mechanisms involved in HCC cells growth, proliferation and migration were analyzed by western blot assay. Results:In silico analysis of HCC data sets showed that elevated expression of WDR12 correlated with high serum AFP level, high vascular invasion, high histologic grade and high TNM stage in HCC patients. Furthermore, up-regulated expression of WDR12 significantly correlated with the short overall survival and recurrence time of HCC patients. The shRNA-mediated knockdown of WDR12 expression resulted in reduced proliferation and migration of HepG2 and Huh-7 cells. Notably, inhibition of WDR12 resulted in decreased phosphorylation of AKT, mTOR and S6K1. Conclusion:Our study indicates that WDR12 contributes to HCC propagation, and indicates that suppression of WDR12 may be a potential strategy for human HCC treatment.

Project description:BackgroundHepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses.MethodsIn the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC.ResultsFirst, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival.ConclusionsThe present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

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Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

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Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

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