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Competing endogenous RNA screening based on long noncoding RNA-messenger RNA co-expression profile in Hepatitis B virus-associated hepatocarcinogenesis

Author: Wen Q, Yang Y, Chen XH, Pan XD, Han Q, Wang D, Deng YC, Li XH, Yan J, Zhou JH
Page: 510

OBJECTIVE: To profile the liver cancer specific long noncoding RNAs (lncRNAs) and competing endogenous RNA (ceRNA) networks of Hepatitis B virus (HBV)-associated hepatocarcinogensis (HCG) and to examine the effect of compound K on the expression of identified ceRNA networks. METHODS: Based on lncRNA and messenger RNA (mRNA) microarray data of HBV-associated liver cancer, the current study profiles the cancer specific lncRNAs and ceRNA networks of HBV-associated HCG through comprehensive application of RegRNA 2, miRTarBase and Pearson correlation coefficient analysis. Compound K-treated liver cancer cells were harvested for analysis of transcriptional levels of both enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH)-AS1 and ENTPD5. RESULTS: The results revealed that 11 Encyclopedia of DNA Elements annotated lncRNAs were differentially expressed in the process of HBV-associated HCG. Among these lncRNAs, 95 potential ceRNA networks with highly positively correlated expression profiles between the interacting lncRNAs and mRNAs (Pearson correlation coefficient > 0.7) were constructed. Of note, two HBV-associated ceRNA networks, EHHADH-AS1-hsa-miR-4459-ectonucleoside triphosphate diphosphohydrolase 5 and LINC01018-hsa-miRNA-574-5p-glucose-6-phosphatase catalytic subunit, with Pearson correlation coefficient > 0.9, may play a critical role in hepatocellular carcinoma development, which was supported by experimental evidence. Interestingly, compound K, an intestinal bacterial metabolite of ginseng protopanaxadiol saponin, which has been proven to promote apoptosis of human hepatocellular carcinoma cells, was found to impede the down-regulation of EHHADH-AS1 in several liver cancer cell lines including HepG3B, Huh-7 and plc/prf/5 cells. CONCLUSION: Comprehensive application of co-expression network analysis and prediction of RNA interaction may be a feasible strategy to unravel the potential ceRNA networks involved in the process of human diseases.

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