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BackgroundThe purpose of this study was to evaluate the potential value of circulating miRNA-122a and miRNA-221 in the diagnosis of hepatocellular carcinoma.MethodsSerum samples were obtained from 85 patients with hepatocellular carcinoma and 85 age-matched and sex-matched healthy volunteers. miRNAs were isolated from the serum samples, and alfa-fetoprotein levels were determined. Expression of miRNA-122a and miRNA-221 in cases and controls was quantified using U6 sn RNA as the internal control. The diagnostic value of miRNA-122a, miRNA-221, and alfa-fetoprotein was compared by receiver operating characteristic analysis.ResultsThe serum miRNA-122a level in patients with hepatocellular carcinoma was significantly reduced in comparison with healthy controls and correlated with known risk factors for hepatocellular carcinoma. Circulating miRNA-221 in patients with hepatocellular carcinoma was higher compared with the control group, but the difference was not statistically significant. Receiver operating characteristic analysis revealed that the diagnostic power of miRNA-122a was suboptimal compared with serum alfa-fetoprotein. Further, the serum alfa-fetoprotein and miRNA-122a combined classifier resulted in performance similar to that of alfa-fetoprotein alone.ConclusionThe serum miRNA-122a level correlates with risk factors for hepatocellular carcinoma. However, use of miRNA-122a as a diagnostic tool for hepatocellular carcinoma is not superior to alfa-fetoprotein. Further analysis is needed to evaluate the diagnostic power of plasma miRNA-122a for hepatocellular carcinoma.

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Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.