Evidence is accumulating that long non-coding RNAs (lncRNAs) are involved in human tumorigenesis and dysregulated in many cancers, including hepatocellular carcinoma (HCC). Because lncRNAs can regulate essential pathways that contribute to tumor initiation and progression with their tissue specificity, lncRNAs are valuable biomarkers and therapeutic targets. Maternally expressed gene 3 (MEG3) is a lncRNA overexpressed in HCC cells that inhibits HCC progression, however, the mechanism remains largely unknown. Recently, a novel regulatory mechanism has been proposed in which RNAs can cross-talk with each other via competing for shared microRNAs (miRNAs). The proposed competitive endogenous RNAs could mediate the bioavailability of miRNAs on their targets, thus imposing another level of post-transcriptional regulation. In the current study, we demonstrated that MEG3 is down-regulated in HCC tissues. MEG3 over-expression imposes another level of post-transcriptional regulation, whereas MEG3 overexpression increase the expression of the miR-664 target gene, ADH4, through competitive "sponging" miR-664. In addition, NF-κB may affect transcription of MEG3 by directly binding to the promoter region. Our data revealed that NF-κB may affect the transcript of MEG3. MEG3 overexpression inhibited the proliferation of HCC cells, at least in part by affecting miR-664mediated regulation of ADH4. Together, these results suggest that MEG3 is a suppressor of tumor which acts in part through "sponging" miR-664. J. Cell. Biochem. 118: 3713-3721, 2017. © 2017 Wiley Periodicals, Inc.
Computational analysis and bioinformatics have significantly advanced the ability of researchers to process and analyze biological data. Molecular data from human and model organisms may facilitate drug target validation and identification of biomarkers with increased predictive accuracy. The aim of the present study was to investigate the function of long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) using online databases, and to predict their regulatory mechanism. HCC-associated lncRNAs, their downstream transcription factors and microRNAs (miRNAs/miRs), as well as the HCC-associated target genes, were identified using online databases. HCC-associated lncRNAs, including HOX antisense intergenic RNA (HOTAIR) and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) were selected based on established databases of lncRNAs. The interaction between the HCC-associated lncRNAs and miRNAs (hsa-miR-1, hsa-miR-20a-5p) was predicted using starBase2.0. Signal transducer and activator of transcription 1, hepatocyte nuclear factor 4α (HNF4A), octamer-binding transcription factor 4, Nanog homeobox (NANOG), caudal type homeobox 2 (CDX2), DEAD-box helicase 5, brahma-related gene 1, MYC-associated factor X and MYC proto-oncogene, bHLH transcription factor have been identified as the transcription factors for HOTAIR and MALAT1 using ChIPBase. Additionally, CDX2, HNF4A, NANOG, ETS transcription factor, Jun proto-oncogene and forkhead box protein A1 were identified as the transcription factors for hsa-miR-1 and hsa-miR-20a-5p. CDX2, HNF4A and NANOG were the transcriptional factors in common between the lncRNAs and miRNAs. Cyclin D1, E2F transcription factor 1, epithelial growth factor receptor, MYC, MET proto-oncogene, receptor tyrosine kinase and vascular endothelial growth factor A were identified as target genes for the HCC progression, two of which were also the target genes of hsa-miR-1 and hsa-miR-20a-5p using the miRwalk and OncoDN. HCC databases. Additionally, these target genes may be involved in biological functions, including the regulation of cell growth, cell cycle progression and mitosis, and in disease progression, as demonstrated using DAVID clustering analysis. The present study aimed to predict a regulatory network of lncRNAs in HCC progression using bioinformatics analysis.
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