Comprehensive competitive endogenous RNA network analysis reveals EZH2-related axes and prognostic biomarkers in hepatocellular carcinoma

Document Type : Original Article


1 Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran

2 Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan, Iran


Objective(s): Hepatocellular carcinoma (HCC) is a common and lethal type of cancer worldwide. The importance of non-coding RNAs such as long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) have been recognized in the development of HCC. In this study, we constructed a four-component competing endogenous RNA (ceRNA) network in HCC and evaluated prognostic values of the ceRNAs. 
Materials and Methods: The expression profiles of lncRNAs, miRNAs, and mRNAs were retrieved from The Cancer Genome Atlas database. GSE94508 and GSE97332 studies from the Gene Expression Omnibus database were used to identify circRNAs expression profiles. A four-component ceRNA network was constructed based on differentially-expressed RNAs. Survival R package was utilized to identify potential prognostic biomarkers.
Results: A four-component ceRNA network including 295 edges and 239 nodes was constructed and enrichment analysis revealed important Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. A Protein-Protein Interaction network with 118 nodes and 301 edges was also established. The enhancer of zeste homolog 2 (EZH2) was the highest degree hub gene in the PPI network. Because of the significance of EZH2 in HCC, we presented its axes in the ceRNA network, which play important roles in HCC progression. Furthermore, ceRNAs were identified as potential prognostic biomarkers utilizing survival analysis.
Conclusion: Our study elucidates the role of ceRNAs and their regulatory interactions in the pathogenesis of HCC and identifies EZH2-related RNAs which may be utilized as novel therapeutic targets and prognostic biomarkers in the future.


1. Ferrante ND, Pillai A, Singal AG. Update on the diagnosis and treatment of hepatocellular carcinoma. Gastroenterol Hepatol 2020; 16:506-516.
2. Ko KL, Mak LY, Cheung KS, Yuen MF. Hepatocellular carcinoma: recent advances and emerging medical therapies. F1000Res 2020; 9:1-10.
3. Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol 2019; 16:589-604.
4. Alqahtani A, Khan Z, Alloghbi A, Said Ahmed TS, Ashraf M, Hammouda DM. Hepatocellular carcinoma: molecular mechanisms and targeted therapies. Medicina (Kaunas) 2019; 55:526-547.
5. Ogunwobi OO, Harricharran T, Huaman J, Galuza A, Odumuwagun O, Tan Y, et al. Mechanisms of hepatocellular carcinoma progression. World J Gastroenterol 2019; 25:2279-2293.
6. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 2011; 146:353-358.
7. Wang Y, Hou J, He D, Sun M, Zhang P, Yu Y, et al. The emerging function and mechanism of ceRNAs in cancer. Trends Genet 2016; 32:211-224.
8. Fu L, Jiang Z, Li T, Hu Y, Guo J. Circular RNAs in hepatocellular carcinoma: functions and implications. Cancer Med 2018; 7:3101-3109.
9. Mai H, Zhou B, Liu L, Yang F, Conran C, Ji Y, et al. Molecular pattern of lncRNAs in hepatocellular carcinoma. J Exp Clin Cancer Res 2019; 38:198-212.
10. Xiong Dd, Dang Yw, Lin P, Wen Dy, He Rq, Luo Dz, et al. A circRNA–miRNA–mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma. J Transl Med 2018; 16:220-240.
11. Luo Y, Li H, Huang H, Xue L, Li H, Liu L, et al. Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis. Medicine (Baltimore) 2021; 100:1-10.
12. Yan Y, Yu J, Liu H, Guo S, Zhang Y, Ye Y, et al. Construction of a long non-coding RNA-associated ceRNA network reveals potential prognostic lncRNA biomarkers in hepatocellular carcinoma. Pathol Res Pract 2018; 214:2031-2038.
13. Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002; 30:207-210.
14. Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet 2013; 45:1113-1120.
15. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an r/bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 2016; 44:71-81.
16.    Lauss M, Visne I, Kriegner A, Ringnér M, Jönsson G, Höglund M. Monitoring of technical variation in quantitative high-throughput datasets. Cancer Inform 2013; 12:193-201.
17.    Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 2010; 11:733-739.
18.    Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform 2020; 2:1-10.
19.    Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012; 28:882-883.
20.    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15:550-564.
21.    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43:47-59.
22.    Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the r/bioconductor package biomaRt. Nat Protoc 2009; 4:1184-1191.
23.    Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res 2014; 42:133-142.
24.    Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015; 4:1-38.
25.    Wong N, Wang X. MiRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 2015; 43:146-152.
26.    Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, et al. MiRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018; 46:296-302.
27.    Lin Y, Liu T, Cui T, Wang Z, Zhang Y, Tan P, et al. RNAInter in 2020: RNA interactome repository with increased coverage and annotation. Nucleic Acids Res 2019; 48:189-197.
28.    Dudekula DB, Panda AC, Grammatikakis I, De S, Abdelmohsen K, Gorospe M. CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol 2016; 13:34-42.
29.    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498-2504.
30.    Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014; 8:11-17.
31.    Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 2011; 39:316-322.
32.    Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics 2015; 31:2912-2914.
33.    Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2020; 49:605-612.
34.    Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56:337-344.
35.    Au SL, Wong CC, Lee JM, Fan DN, Tsang FH, Ng IO, et al. Enhancer of zeste homolog 2 epigenetically silences multiple tumor suppressor microRNAs to promote liver cancer metastasis. Hepatology 2012; 56:622-631.
36.    Sudo T, Utsunomiya T, Mimori K, Nagahara H, Ogawa K, Inoue H, et al. Clinicopathological significance of EZH2 mRNA expression in patients with hepatocellular carcinoma. Br J Cancer 2005; 92:1754-1758.
37.    Cao MQ, You AB, Zhu XD, Zhang W, Zhang YY, Zhang SZ, et al. MiR-182-5p promotes hepatocellular carcinoma progression by repressing FOXO3a. J Hematol Oncol 2018; 11:12-33.
38.    Shi M, Li ZY, Zhang LM, Wu XY, Xiang SH, Wang YG, et al. Hsa_circ_0007456 regulates the natural killer cell-mediated cytotoxicity toward hepatocellular carcinoma via the miR-6852-3p/ICAM-1 axis. Cell Death Dis 2021; 12:94-106.
39.    Duan R, Du W, Guo W. EZH2: a novel target for cancer treatment. J Hematol Oncol 2020; 13:104-115.
40.    Kim KH, Roberts CWM. Targeting EZH2 in cancer. Nat Med 2016; 22:128-134.
41.    Gan L, Xu M, Hua R, Tan C, Zhang J, Gong Y, et al. The polycomb group protein EZH2 induces epithelial-mesenchymal transition and pluripotent phenotype of gastric cancer cells by binding to PTEN promoter. J Hematol Oncol 2018; 11:9-20.
42.    Pellecchia S, Sepe R, Decaussin-Petrucci M, Ivan C, Shimizu M, Coppola C, et al. The long non-coding RNA prader willi/angelman region RNA5 (PAR5) is downregulated in anaplastic thyroid carcinomas where it acts as a tumor suppressor by reducing EZH2 activity. Cancers 2020; 12:235-252.
43.    Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 2002; 419:624-629.
44.    Yamagishi M, Uchimaru K. Targeting EZH2 in cancer therapy. Curr Opin Oncol 2017; 29:375-381.
45.    Xiao G, Jin LL, Liu CQ, Wang YC, Meng YM, Zhou ZG, et al. EZH2 negatively regulates PD-L1 expression in hepatocellular carcinoma. J Immunother Cancer 2019; 7:300-314.
46.    Xu L, Beckebaum S, Iacob S, Wu G, Kaiser GM, Radtke A, et al. MicroRNA-101 inhibits human hepatocellular carcinoma progression through EZH2 downregulation and increased cytostatic drug sensitivity. J Hepatol 2014; 60:590-598.
47.    Liao X, Wang X, Huang K, Han C, Deng J, Yu T, et al. Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma. J Cancer 2019; 10:3267-3283.
48.    Nomura K, Kitanaka A, Iwama H, Tani J, Nomura T, Nakahara M, et al. Association between microRNA-527 and glypican-3 in hepatocellular carcinoma. Oncol Lett 2021; 21:229-236.
49.    Zhou S, Zhu C, Pang Q, Liu HC. MicroRNA-217: A regulator of human cancer. Biomed Pharmacother 2021;133:110943.
50.    Gan L, Yang Y, Li Q, Feng Y, Liu T, Guo W. Epigenetic regulation of cancer progression by EZH2: from biological insights to therapeutic potential. Biomark Res 2018; 6:10-19.
51.    Zhai R, Tang F, Gong J, Zhang J, Lei B, Li B, et al. The relationship between the expression of USP22, BMI1, and EZH2 in hepatocellular carcinoma and their impacts on prognosis. Onco Targets Ther 2016; 9:6987-6998.
52.    Zhang K, Fang T, Shao Y, Wu Y. TGF-β-MTA1-SMAD7-SMAD3-SOX4-EZH2 signaling axis promotes viability, migration, invasion and EMT of hepatocellular carcinoma cells. Cancer Manag Res 2021; 13:7087-7099.
53.    Guo B, Tan X, Cen H. EZH2 is a negative prognostic biomarker associated with immunosuppression in hepatocellular carcinoma. PloS One 2020; 15:1-16.
54.    Xu L, Lin J, Deng W, Luo W, Huang Y, Liu C-Q, et al. EZH2 facilitates BMI1-dependent hepatocarcinogenesis through epigenetically silencing microRNA-200c. Oncogenesis 2020; 9:101-116.