Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Dysfunctional Mechanism of Liver Cancer Mediated by Transcription Factor and Non-coding RNA

Author(s): Wei Zeng, Fang Wang, Yu Ma, Xianchun Liang and Ping Chen*

Volume 14, Issue 2, 2019

Page: [100 - 107] Pages: 8

DOI: 10.2174/1574893614666181119121916

Abstract

Background: There have been numerous experiments and studies on liver cancer by biomedical scientists, while no comprehensive and systematic exploration has yet been conducted. Therefore, this study aimed to systematically dissect the transcriptional and non-coding RNAmediated mechanisms of liver cancer dysfunction.

Method: At first, we collected 974 liver cancer associated genes from the Online Mendelian Inheritance in Man (OMIM). Afterwards, their interactors were recruited from STRING database so as to identify 18 co-expression modules in liver cancer patient expression profile. Crosstalk analysis showed the interactive relationship between these modules. In addition, core drivers for modules were identified, including 111 transcription factors (STAT3, JUN and NFKB1, etc.) and 1492 ncRNAs (FENDRR and miR-340-5p, etc.).

Results: In view of the results of enrichment, we found that these core drivers were significantly involved in Notch signaling, Wnt / β-catenin pathways, cell proliferation, apoptosis-related functions and pathways, suggesting they can affect the development of liver cancer. Furthermore, a global effect on bio-network associated with liver cancer has been integrated from the ncRNA and TF pivot network, module crosstalk network, module-function/pathways network. It involves various development and progression of cancer.

Conclusion: Overall, our analysis further suggests that comprehensive network analysis will help us to not only understand in depth the molecular mechanisms, but also reveal the influence of related gene dysfunctional modules on the occurrence and progression of liver cancer. It provides a valuable reference for the design of liver cancer diagnosis and treatment.

Keywords: Transcription factors, non-coding RNAs, liver cancer, co-expression module, core driver, network analysis.

Next »
Graphical Abstract
[1]
Altekruse SF, Henley SJ, Cucinelli JE, McGlynn KA. Changing hepatocellular carcinoma incidence and liver cancer mortality rates in the United States. Am J Gastroenterol 2014; 109(4): 542-53.
[2]
Duan XY, Zhang L, Fan JG, Qiao L. NAFLD leads to liver cancer: do we have sufficient evidence? Cancer Lett 2014; 345(2): 230-4.
[3]
Pang Q, Qu K, Zhang J, et al. Cigarette smoking increases the risk of mortality from liver cancer: A clinical-based cohort and meta-analysis. J Gastroenterol Hepatol 2015; 30(10): 1450-60.
[4]
Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015; 43(Database issue): D789-98.
[5]
Wu G, Wilson G, George J, Qiao L. Modulation of Notch signaling as a therapeutic approach for liver cancer. Curr Gene Ther 2015; 15(2): 171-81.
[6]
Song B, Bian Q, Shao CH, et al. Ulinastatin reduces the resistance of liver cancer cells to epirubicin by inhibiting autophagy. PLoS One 2015; 10(3): e0120694.
[7]
Nio K, Yamashita T, Okada H, et al. Defeating EpCAM + liver cancer stem cells by targeting chromatin remodeling enzyme CHD4 in human hepatocellular carcinoma. J Hepatol 2015; 63(5): 1164-72.
[8]
Vilchez V, Turcios L, Marti F, Gedaly R. Targeting Wnt/β-catenin pathway in hepatocellular carcinoma treatment. World J Gastroenterol 2016; 22(2): 823.
[9]
Carbajopescador S, Mauriz JL. FoxO proteins: regulation and molecular targets in liver cancer. Curr Med Chem 2014; 21(10): 1231-46.
[10]
Mollinedo F, Gajate C. Lipid rafts as major platforms for signaling regulation in cancer. Adv Biol Regul 2015; 57: 130-46.
[11]
Hryniewicz-Jankowska A, Augoff K, Biernatowska A, Podkalicka J, Sikorski AF. Membrane rafts as a novel target in cancer therapy. Biochim Biophys Acta 2014; 1845(2): 155-65.
[12]
Ma DW. Lipid mediators in membrane rafts are important determinants of human health and disease. Appl Physiol Nutr Metab 2007; 32(3): 341.
[13]
Kun-Peng Z, Xiao-Long M, Chun-Lin Z. LncRNA FENDRR sensitizes doxorubicin-resistance of osteosarcoma cells through down-regulating ABCB1 and ABCC1. Oncotarget 2017; 8(42): 71881-93.
[14]
Xu TP, Huang MD, Xia R, et al. Decreased expression of the long non-coding RNA FENDRR is associated with poor prognosis in gastric cancer and FENDRR regulates gastric cancer cell metastasis by affecting fibronectin1 expression. J Hematol Oncol 2014; 7(1): 63.
[15]
Xiong Q, Wu S, Wang J, et al. Hepatitis B virus promotes cancer cell migration by downregulating miR-340-5p expression to induce STAT3 overexpression.%A Xiong Q. Cell Biosci 2017; 7: 16.
[16]
Gao B, Wang H, Lafdil F, Feng D. STAT proteins - key regulators of anti-viral responses, inflammation, and tumorigenesis in the liver. J Hepatol 2012; 57(2): 430-41.
[17]
Wilson CL, Jurk D, Fullard N, et al. NF[kappa]B1 is a suppressor of neutrophil-driven hepatocellular carcinoma. Nat Commun 2015; 6: 8411.
[18]
Cheng CW, Su JL, Lin CW, et al. Effects of NFKB1 and NFKBIA gene polymorphisms on hepatocellular carcinoma susceptibility and clinicopathological features. PLoS One 2013; 8(2): e56130.
[19]
Kuo KK, Lee KT, Chen KK, et al. Positive feedback loop of OCT4 and c-JUN expedites cancer stemness in liver cancer. Stem Cells 2016; 34(11): 2613-24.
[20]
Moles A, Butterworth JA, Sanchez A, et al. A RelA(p65) Thr505 phospho-site mutation reveals an important mechanism regulating NF-κB-dependent liver regeneration and cancer. Oncogene 2016; 35(35): 4623-32.
[21]
Moles A, Sanchez AM, Banks PS, et al. Inhibition of RelA-Ser536 phosphorylation by a competing peptide reduces mouse liver fibrosis without blocking the innate immune response. Hepatology 2013; 57(2): 817-28.
[22]
Hu B, Sun M, Liu J, Hong G, Lin Q. The preventative effect of Akt knockout on liver cancer through modulating NF-κB-regulated inflammation and Bad-related apoptosis signaling pathway. Int J Oncol 2016; 48(4): 1467-76.
[23]
Watanabe T, Suzuki T, Natsume M, et al. Discrimination of genotoxic and non-genotoxic hepatocarcinogens by statistical analysis based on gene expression profiling in the mouse liver as determined by quantitative real-time PCR. Mutat Res 2012; 747(2): 164-75.
[24]
Sun H, Gao Y, Lu K, et al. Overexpression of Klotho suppresses liver cancer progression and induces cell apoptosis by negatively regulating wnt/β-catenin signaling pathway. World J Surg Oncol 2015; 13(1): 307.
[25]
Melão A, Spit M, Cardoso BA, Barata JT. Optimal interleukin-7 receptor-mediated signaling, cell cycle progression and viability of T-cell acute lymphoblastic leukemia cells rely on casein kinase 2 activity. Haematologica 2016; 101(11): 1368-79.
[26]
Wang Z, Zhang H, Zhou J, et al. Eriocitrin from lemon suppresses the proliferation of human hepatocellular carcinoma cells through inducing apoptosis and arresting cell cycle. Cancer Chemother Pharmacol 2016; 78(6): 1-8.
[27]
Moon H, Ju HL, Chung SI, et al. Transforming Growth Factor-β Promotes Liver Tumorigenesis in Mice via Up-regulation of Snail. Gastroenterology 2017; 153(5): 1378.
[28]
Lazaris A, Amri A, Petrillo SK, et al. Vascularization of colorectal carcinoma liver metastasis: Insight into stratification of patients for anti-ngiogenic therapies. J Pathol Clin Res 2018; 4(3): 184-92.
[29]
Richter K, Paakkola T, Mennerich D, et al. USP28 Deficiency Promotes Breast and Liver Carcinogenesis as well as Tumor Angiogenesis in a HIF-independent Manner. Mol Cancer Res 2018; 16(6): 1000-12.
[30]
Liu XL, Ding J, Meng LH. Oncogene-induced senescence: a double edged sword in cancer. Acta Pharmacol Sin 2018; 39(10): 1553-8.
[31]
Huo S, Yu H, Li C, Zhang J, Liu T. Effect of halofuginone on the inhibition of proliferation and invasion of hepatocellular carcinoma HepG2 cell line. Int J Clin Exp Pathol 2015; 8(12): 15863.
[32]
Bhardwaj A, Sethi G, Vadhan-Raj S, et al. Resveratrol inhibits proliferation, induces apoptosis, and overcomes chemoresistance through down-regulation of STAT3 and nuclear factor-kappaB-regulated antiapoptotic and cell survival gene products in human multiple myeloma cells. Blood 2007; 109(6): 2293-302.
[33]
Qiang D, Xia Y, Ding S, Lu P, Liang S, Mei L. An alternatively spliced variant of CXCR3 mediates the metastasis of CD133+ liver cancer cells induced by CXCL9. Oncotarget 2016; 7(12): 14405-14.
[34]
Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life.%A Szklarczyk D. Nucleic Acids Res 2015; 43(Database issue): D447-52.
[35]
Katarzyna T, Patrycja C, Maciej W. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 2015; 19(1A): 68-77.
[36]
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9(1): 559.
[37]
Zhang Y, Fan H, Xu J, et al. Network analysis reveals functional cross-links between disease and inflammation genes. Sci Rep 2013; 3: 3426.
[38]
Artzy-Randrup Y, Fleishman SJ, Ben-Tal N, Stone L. Comment on “Network motifs: simple building blocks of complex networks” and “Superfamilies of evolved and designed networks”. Science 2004; 305(5687): 1107.
[39]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498.
[40]
Yi Y, Zhao Y, Li C, et al. RAID v2.0: an updated resource of RNA-associated interactions across organisms. Nucleic Acids Res 2017; 45(D1): D115-8.
[41]
Han H, Cho JW, Lee S, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 2018; 46(D1): D380-6.
[42]
Liu N, Li C, Huang Y, et al. A functional module-based exploration between inflammation and cancer in esophagus. Sci Rep 2015; 5: 15340.
[43]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS 2012; 16(5): 284-7.
[44]
Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005; 21(16): 3448-9.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy