Abstract
Background: Hepatic cirrhosis is the consequence of various chronic liver diseases for which there is no curative treatment. In this study, based on RNA sequencing (RNA-seq) and subsequent bioinformatic analysis, we aim to explore the biological function of non-coding RNAs (ncRNAs) in hepatic cirrhosis.
Methods: The hepatic cirrhosis models were induced by the intraperitoneal injection of carbon tetrachloride (CCl4). The transcriptome profile was acquired by RNA-seq, the results of which were verified by quantitative real-time PCR (qRT-PCR). The competing endogenous RNA (ceRNA) networks were visualized by Cytoscape software. The enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted.
Results: The differentially expressed transcript of liver cirrhosis consists of 2369 mRNAs, 374 lncRNAs, 91 circRNAs, and 242 miRNAs (|log2(fold change)|≥1 and P<0.05). The RNA-seq results were highly consistent with qRT-PCR validation of DEGs (four upregulated and four down-regulated, including ENSMUSG00000047517, ENSMUST00000217449, novel-circ-001366, miR-383-5p, ENSMUSG00000078683, ENSMUST00000148206, novel-circ-001986 and miR-216a-5p). Based on ceRNA theory, a circRNA-lncRNA co-regulated ceRNA network was established. Enrichment analysis revealed the potential key regulatory process during the liver cirrhosis progression.
Conclusion: In conclusion, the present study comprehensively analyzed differentially expressed transcripts in CCl4-induced liver cirrhosis. Our findings explored the gene signatures for liver cirrhosis’s diagnosis and precise treatment.
Keywords: RNA sequencing, liver cirrhosis, non-coding RNA, competing endogenous RNA, transcriptome, ceRNA Networks, mouse model.
[http://dx.doi.org/10.1016/S0140-6736(14)60121-5] [PMID: 24480518]
[http://dx.doi.org/10.1016/S2468-1253(19)30349-8] [PMID: 31981519]
[http://dx.doi.org/10.1016/j.metabol.2015.08.004] [PMID: 26362725]
[http://dx.doi.org/10.1016/j.jhep.2017.04.009] [PMID: 28438689]
[http://dx.doi.org/10.1016/j.ajpath.2011.06.045] [PMID: 21854740]
[http://dx.doi.org/10.1126/science.1163045] [PMID: 18974356]
[http://dx.doi.org/10.1038/nature06992] [PMID: 18509338]
[http://dx.doi.org/10.1002/mc.22362] [PMID: 26332907]
[http://dx.doi.org/10.1186/s13578-018-0259-6] [PMID: 30534359]
[http://dx.doi.org/10.18632/aging.102705] [PMID: 32003753]
[http://dx.doi.org/10.18632/aging.102405] [PMID: 31719209]
[http://dx.doi.org/10.1093/bioinformatics/btp616] [PMID: 19910308]
[http://dx.doi.org/10.1002/art.41552] [PMID: 33034147]
[PMID: 23193258]
[http://dx.doi.org/10.1038/75556] [PMID: 10802651]
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[http://dx.doi.org/10.1016/j.mam.2018.09.002] [PMID: 30213667]
[http://dx.doi.org/10.18632/oncotarget.11637] [PMID: 27580177]
[http://dx.doi.org/10.1038/nature02555] [PMID: 15190252]
[http://dx.doi.org/10.1016/j.jhep.2018.12.016] [PMID: 30582979]
[http://dx.doi.org/10.1136/gutjnl-2011-300717] [PMID: 22267590]
[http://dx.doi.org/10.1111/1751-2980.12266] [PMID: 26120970]
[http://dx.doi.org/10.5009/gnl16560] [PMID: 28750488]
[http://dx.doi.org/10.1053/j.gastro.2017.12.022] [PMID: 29305935]
[http://dx.doi.org/10.7150/ijbs.28089] [PMID: 30416378]
[http://dx.doi.org/10.4161/cc.29298] [PMID: 25483186]
[http://dx.doi.org/10.1038/s41598-017-04317-0] [PMID: 28642549]
[http://dx.doi.org/10.1016/j.jaut.2018.05.001] [PMID: 29753567]
[http://dx.doi.org/10.1016/j.taap.2019.114853] [PMID: 31816328]
[http://dx.doi.org/10.1016/j.omtn.2019.05.001] [PMID: 31150929]
[http://dx.doi.org/10.1016/j.prp.2019.152674] [PMID: 31732382]
[http://dx.doi.org/10.3892/or.2016.5139] [PMID: 27748890]
[PMID: 30592264]
[http://dx.doi.org/10.1016/j.omtn.2019.01.006] [PMID: 30753992]
[http://dx.doi.org/10.1053/j.gastro.2014.06.043] [PMID: 25066692]
[http://dx.doi.org/10.1172/jci.insight.141217] [PMID: 32910808]