Reconstruction and Analysis of the Differentially Expressed IncRNA-miRNA-mRNA Network Based on Competitive Endogenous RNA in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common and aggressive malignant neoplasm with an increasing incidence rate among cancers worldwide. This study aimed to establish a competing-endogenous RNA (ceRNA) network to further understand the ceRNA regulatory mechanism and pathogenesis in HCC. mRNA and miRNA expression data and corresponding clinical characteristics of HCC patients were downloaded from The Cancer Genome Atlas Data Portal. The different expression profiles of mRNA, lncRNA, and miRNA (referred to as DEmRNAs, DElncRNAs, and DEmiR-NAs, respectively) screened 374 HCC tumor tissues and 50 adjacent nontumor control tissues. The miRcode database was used to predict interactions between DElncRNAs and DEmiRNAs. The miRTarBase, miRDB, and TargetScan databases were used to retrieve miRNA-targeted mRNAs. The lncRNA-miRNA-mRNA ceRNA network was constructed based on matched DElncRNA-DEmiRNA and DEmiRNA-DEmRNA pairs. Functional enrichment analysis revealed the biological processes and pathways of DEmRNAs involved in the development of HCC. Key differentially expressed RNAs in the ceRNA network were further analyzed for their relationships with overall survival in HCC patients. A total of 1,982 mRNAs, 1,081 lncRNAs, and 126 miRNAs were identified as differentially expressed. The ceRNA network was constructed including 74 DElncRNAs, 35 DEmRNAs, and 16 DEmiRNAs. 13 DElncRNAs and 19 DEmRNAs were identified as prognosis-related biomarkers. Twenty-five Gene Ontology terms and 8 Kyoto Encyclopedia of Genes and Genomes pathways were found to be significantly enriched by DEmRNAs in the ceRNA network. Our results demonstrated tumor-specific mRNA, lncRNA, and miRNA expression patterns and enabled us to construct a ceRNA network that provided new insights into the molecular mechanisms of HCC. Key differentially expressed RNAs associated with total survival were also identified as promising biomarkers for survival prediction.