Generic placeholder image

Current Bioinformatics

Editor-in-Chief

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

Research Article

Biomarker Identification for Liver Hepatocellular Carcinoma and Cholangiocarcinoma Based on Gene Regulatory Network Analysis

Author(s): Qiuyan Huo, Yuying Ma, Yu Yin and Guimin Qin*

Volume 16, Issue 1, 2021

Published on: 17 March, 2020

Page: [31 - 43] Pages: 13

DOI: 10.2174/1574893615666200317115609

Price: $65

Abstract

Background: Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are two main histological subtypes of primary liver cancer with a unified molecular landscape, and feed-forward loops (FFLs) have been shown to be relevant in these complex diseases.

Objective: To date, there has been no comparative analysis of the pathogenesis of LIHC and CHOL based on regulatory relationships. Therefore, we investigated the common and distinct regulatory properties of LIHC and CHOL in terms of gene regulatory networks.

Methods: Based on identified FFLs and analysis of pathway enrichment, we constructed pathwayspecific co-expression networks and further predicted biomarkers for these cancers by network clustering.

Results: We identified 20 and 36 candidate genes for LIHC and CHOL, respectively. The literature from PubMed supports the reliability of our results.

Conclusion: Our results indicated that the hsa01522-Endocrine resistance pathway was associated with both LIHC and CHOL. Additionally, six genes (SPARC, CTHRC1, COL4A1, EDIL3, LAMA4 and OLFML2B) were predicted to be highly associated with both cancers, and COL4A2, CSPG4, GJC1 and ADAMTS7 were predicted to be potential biomarkers of LIHC, and COL6A3, COL1A2, FAP and COL8A1 were predicted to be potential biomarkers of CHOL. In addition, we inferred that the Collagen gene family, which appeared more frequently in our overall prediction results, might be closely related to cancer development.

Keywords: Hepatocellular carcinoma, cholangiocarcinoma, feed-forward loops, gene regulatory network, transcription factor, clusterone.

Graphical Abstract
[1]
Berretta M, Cavaliere C, Alessandrini L, et al. Serum and tissue markers in hepatocellular carcinoma and cholangiocarcinoma: clinical and prognostic implications. Oncotarget 2017; 8(8): 14192-220.
[http://dx.doi.org/10.18632/oncotarget.13929] [PMID: 28077782]
[2]
Ally A, Balasundaram M, Carlsen R, et al. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 2017; 169(7): 1327-1341.e23.
[http://dx.doi.org/10.1016/j.cell.2017.05.046] [PMID: 28622513]
[3]
Zhang Y, Guo X, Xiong L, et al. Comprehensive analysis of microRNA-regulated protein interaction network reveals the tumor suppressive role of microRNA-149 in human hepatocellular carcinoma via targeting AKT-mTOR pathway. Mol Cancer 2014; 13(1): 253.
[http://dx.doi.org/10.1186/1476-4598-13-253] [PMID: 25424347]
[4]
Zhong W, Dai L, Liu J, Zhou S. Cholangiocarcinoma associated genes identified by integrative analysis of gene expression data. Mol Med Rep 2018; 17(4): 5744-53.
[http://dx.doi.org/10.3892/mmr.2018.8594] [PMID: 29436659]
[5]
Yang W, Li Y, Song X, Xu J, Xie J. Genome-wide analysis of long noncoding RNA and mRNA co-expression profile in intrahepatic cholangiocarcinoma tissue by RNA sequencing. Oncotarget 2017; 8(16): 26591-9.
[http://dx.doi.org/10.18632/oncotarget.15721] [PMID: 28427159]
[6]
Chaisaingmongkol J, Budhu A, Dang H, et al. Common molecular subtypes among asian hepatocellular carcinoma and cholangiocarcinoma. Cancer Cell 2017; 32(1): 57-70.e3.
[http://dx.doi.org/10.1016/j.ccell.2017.05.009] [PMID: 28648284]
[7]
Raza K. Reconstruction, topological and gene ontology enrichment analysis of cancerous gene regulatory network modules. Curr Bioinform 2016; 11(2): 243-58.
[http://dx.doi.org/10.2174/1574893611666160115212806]
[8]
Chai LE, Mohamad MS, Deris S, et al. Current development and review of dynamic bayesian network-based methods for inferring gene regulatory networks from gene expression data. Curr Bioinform 2014; 9(5): 531-9.
[http://dx.doi.org/10.2174/1574893609666140421210333]
[9]
Hobert O. Gene regulation by transcription factors and microRNAs. Science 2008; 319(5871): 1785-6.
[http://dx.doi.org/10.1126/science.1151651] [PMID: 18369135]
[10]
Yan Z, Shah PK, Amin SB, et al. Integrative analysis of gene and miRNA expression profiles with transcription factor-miRNA feed-forward loops identifies regulators in human cancers. Nucleic acids research 2012; 40(17): e135.
[http://dx.doi.org/10.1093/nar/gks395]
[11]
Zhang HM, Kuang S, Xiong X, Gao T, Liu C, Guo AY. Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Brief Bioinform 2015; 16(1): 45-58.
[http://dx.doi.org/10.1093/bib/bbt085] [PMID: 24307685]
[12]
Li K, Li Z, Zhao N, et al. Functional analysis of microRNA and transcription factor synergistic regulatory network based on identifying regulatory motifs in non-small cell lung cancer. BMC Syst Biol 2013; 7: 122.
[http://dx.doi.org/10.1186/1752-0509-7-122] [PMID: 24200043]
[13]
Qin S, Ma F, Chen L. Gene regulatory networks by transcription factors and microRNAs in breast cancer. Bioinformatics 2015; 31(1): 76-83.
[http://dx.doi.org/10.1093/bioinformatics/btu597] [PMID: 25189779]
[14]
Xiong L, Jiang W, Zhou R, Mao C, Guo Z. Identification and analysis of the regulatory network of Myc and microRNAs from high-throughput experimental data. Comput Biol Med 2013; 43(9): 1252-60.
[http://dx.doi.org/10.1016/j.compbiomed.2013.06.002] [PMID: 23930820]
[15]
Lin Y, Sibanda VL, Zhang H-M, Hu H, Liu H, Guo AY. MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction. Sci Rep 2015; 5: 9653.
[http://dx.doi.org/10.1038/srep09653] [PMID: 25867756]
[16]
Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 2015; 19(1A): A68-77.
[http://dx.doi.org/10.5114/wo.2014.47136] [PMID: 25691825]
[17]
Bø TH, Dysvik B, Jonassen I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res 2004; 32(3): e34.
[http://dx.doi.org/10.1093/nar/gnh026]
[18]
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[19]
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.
[http://dx.doi.org/10.1093/nar/gkx1013] [PMID: 29087512]
[20]
Bovolenta LA, Acencio ML, Lemke N. HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics 2012; 13(1): 405.
[http://dx.doi.org/10.1186/1471-2164-13-405] [PMID: 22900683]
[21]
Tong Z, Cui Q, Wang J, Zhou Y. TransmiR v2.0: an updated transcription factor-microRNA regulation database. Nucleic Acids Res 2019; 47(D1): D253-8.
[PMID: 30371815]
[22]
Hua X, Tang R, Xu X, et al. mirTrans: a resource of transcriptional regulation on microRNAs for human cell lines. Nucleic Acids Res 2018; 46(D1): D168-74.
[http://dx.doi.org/10.1093/nar/gkx996] [PMID: 29077896]
[23]
Agarwal V, Bell GW, Nam J-W, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs eLife 2015.: 4e05005.
[http://dx.doi.org/10.7554/eLife.05005] [PMID: 26267216]
[24]
Chou C-H, Shrestha S, Yang C-D, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018; 46(D1): D296-302.
[http://dx.doi.org/10.1093/nar/gkx1067] [PMID: 29126174]
[25]
Lai X, Wolkenhauer O, Vera J. Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Res 2016; 44(13): 6019-35.
[http://dx.doi.org/10.1093/nar/gkw550] [PMID: 27317695]
[26]
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28(1): 27-30.
[http://dx.doi.org/10.1093/nar/28.1.27] [PMID: 10592173]
[27]
Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[28]
Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37(1): 1-13.
[http://dx.doi.org/10.1093/nar/gkn923] [PMID: 19033363]
[29]
Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 Nucleic Acids Res 2013; 41(Web Server issue): W77-83.
[http://dx.doi.org/10.1093/nar/gkt439] [PMID: 23703215]
[30]
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005; 33(Database issue): D514-7.
[http://dx.doi.org/10.1093/nar/gki033] [PMID: 15608251]
[31]
Sondka Z, Bamford S, Cole CG, Ward SA, Dunham I, Forbes SA. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer 2018; 18(11): 696-705.
[http://dx.doi.org/10.1038/s41568-018-0060-1] [PMID: 30293088]
[32]
Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive characterization of cancer driver genes and mutations. Cell 2018; 173(2): 371-385.e18.
[http://dx.doi.org/10.1016/j.cell.2018.02.060] [PMID: 29625053]
[33]
Jiang Q, Wang Y, Hao Y, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009; 37(Database issue): D98-D104.
[http://dx.doi.org/10.1093/nar/gkn714] [PMID: 18927107]
[34]
Ruepp A, Kowarsch A, Schmidl D, et al. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol 2010; 11(1): R6.
[http://dx.doi.org/10.1186/gb-2010-11-1-r6] [PMID: 20089154]
[35]
Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012; 9(5): 471-2.
[http://dx.doi.org/10.1038/nmeth.1938] [PMID: 22426491]
[36]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498-504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[37]
Song H, Yu Z, Sun X, et al. Androgen receptor drives hepatocellular carcinogenesis by activating enhancer of zeste homolog 2-mediated Wnt/β-catenin signaling. EBioMedicine 2018; 35: 155-66.
[http://dx.doi.org/10.1016/j.ebiom.2018.08.043] [PMID: 30150059]
[38]
Shi L, Yan P, Liang Y, et al. Circular RNA expression is suppressed by androgen receptor (AR)-regulated adenosine deaminase that acts on RNA (ADAR1) in human hepatocellular carcinoma. Cell Death Dis 2017; 8(11): e3171.
[http://dx.doi.org/10.1038/cddis.2017.556] [PMID: 29144509]
[39]
Lai HC, Yeh CC, Jeng LB, et al. Androgen receptor mitigates postoperative disease progression of hepatocellular carcinoma by suppressing CD90+ populations and cell migration and by promoting anoikis in circulating tumor cells. Oncotarget 2016; 7(29): 46448-65.
[http://dx.doi.org/10.18632/oncotarget.10186] [PMID: 27340775]
[40]
Dittmer J. The role of the transcription factor Ets1 in carcinoma. Semin Cancer Biol 2015; 35: 20-38.
[http://dx.doi.org/10.1016/j.semcancer.2015.09.010] [PMID: 26392377]
[41]
Cao L, Xie B, Yang X, et al. MiR-324-5p suppresses hepatocellular carcinoma cell invasion by counteracting ECM degradation through post-transcriptionally downregulating ETS1 and SP1. PLoS One 2015; 10(7): e0133074.
[http://dx.doi.org/10.1371/journal.pone.0133074] [PMID: 26177288]
[42]
Hua S, Lei L, Deng L, et al. miR-139-5p inhibits aerobic glycolysis, cell proliferation, migration, and invasion in hepatocellular carcinoma via a reciprocal regulatory interaction with ETS1. Oncogene 2018; 37(12): 1624-36.
[http://dx.doi.org/10.1038/s41388-017-0057-3] [PMID: 29335523]
[43]
Ma N, Chen F, Shen SL, et al. MicroRNA-129-5p inhibits hepatocellular carcinoma cell metastasis and invasion via targeting ETS1. Biochem Biophys Res Commun 2015; 461(4): 618-23.
[http://dx.doi.org/10.1016/j.bbrc.2015.04.075] [PMID: 25912876]
[44]
Kang R, Saito H, Ihara Y, et al. Transcriptional regulation of the N-acetylglucosaminyltransferase V gene in human bile duct carcinoma cells (HuCC-T1) is mediated by Ets-1. J Biol Chem 1996; 271(43): 26706-12.
[http://dx.doi.org/10.1074/jbc.271.43.26706] [PMID: 8900148]
[45]
O’Hara SP, Splinter PL, Trussoni CE, et al. ETS Proto-oncogene 1 transcriptionally up-regulates the cholangiocyte senescence-associated protein cyclin-dependent kinase inhibitor 2A. J Biol Chem 2017; 292(12): 4833-46.
[http://dx.doi.org/10.1074/jbc.M117.777409] [PMID: 28184004]
[46]
Ma G, Liu H, Hua Q, et al. KCNMA1 cooperating with PTK2 is a novel tumor suppressor in gastric cancer and is associated with disease outcome. Mol Cancer 2017; 16(1): 46.
[http://dx.doi.org/10.1186/s12943-017-0613-z] [PMID: 28231797]
[47]
Chen YL, Wang TH, Hsu HC, Yuan RH, Jeng YM. Overexpression of CTHRC1 in hepatocellular carcinoma promotes tumor invasion and predicts poor prognosis. PLoS One 2013; 8(7): e70324.
[http://dx.doi.org/10.1371/journal.pone.0070324] [PMID: 23922981]
[48]
Sulpice L, Rayar M, Desille M, et al. Molecular profiling of stroma identifies osteopontin as an independent predictor of poor prognosis in intrahepatic cholangiocarcinoma. Hepatology 2013; 58(6): 1992-2000.
[http://dx.doi.org/10.1002/hep.26577] [PMID: 23775819]
[49]
Zhang J, Hao N, Liu W, et al. In-depth proteomic analysis of tissue interstitial fluid for hepatocellular carcinoma serum biomarker discovery. Br J Cancer 2017; 117(11): 1676-84.
[http://dx.doi.org/10.1038/bjc.2017.344] [PMID: 29024941]
[50]
Deng B, Qu L, Li J, et al. MiRNA-211 suppresses cell proliferation, migration and invasion by targeting SPARC in human hepatocellular carcinoma. Sci Rep 2016; 6: 26679.
[http://dx.doi.org/10.1038/srep26679] [PMID: 27230656]
[51]
Xia H, Chen J, Shi M, et al. EDIL3 is a novel regulator of epithelial-mesenchymal transition controlling early recurrence of hepatocellular carcinoma. J Hepatol 2015; 63(4): 863-73.
[http://dx.doi.org/10.1016/j.jhep.2015.05.005] [PMID: 25980764]
[52]
Feng MX, Ma MZ, Fu Y, et al. Elevated autocrine EDIL3 protects hepatocellular carcinoma from anoikis through RGD-mediated integrin activation. Mol Cancer 2014; 13: 226.
[http://dx.doi.org/10.1186/1476-4598-13-226] [PMID: 25273699]
[53]
Huang X, Ji G, Wu Y, Wan B, Yu L. LAMA4, highly expressed in human hepatocellular carcinoma from Chinese patients, is a novel marker of tumor invasion and metastasis. J Cancer Res Clin Oncol 2008; 134(6): 705-14.
[http://dx.doi.org/10.1007/s00432-007-0342-6] [PMID: 18084776]
[54]
Frantz C, Stewart KM, Weaver VM. The extracellular matrix at a glance. J Cell Sci 2010; 123(Pt 24): 4195-200.
[http://dx.doi.org/10.1242/jcs.023820] [PMID: 21123617]
[55]
Lee C, Kim M, Lee JH, et al. COL6A3-derived endotrophin links reciprocal interactions among hepatic cells in the pathology of chronic liver disease. J Pathol 2019; 247(1): 99-109.
[PMID: 30246318]
[56]
Huang QX, Cui JY, Ma H, Jia XM, Huang FL, Jiang LX. Screening of potential biomarkers for cholangiocarcinoma by integrated analysis of microarray data sets. Cancer Gene Ther 2016; 23(2-3): 48-53.
[http://dx.doi.org/10.1038/cgt.2015.66] [PMID: 26679756]
[57]
Yeh CN, Weng WH, Lenka G, et al. cDNA microarray profiling of rat cholangiocarcinoma induced by thioacetamide. Mol Med Rep 2013; 8(2): 350-60.
[http://dx.doi.org/10.3892/mmr.2013.1516] [PMID: 23754683]

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