In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC.

Cancer Cell Int

1Program of Innovative Cancer Therapeutics, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, College of Medicine, Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Key Laboratory of Organ Transplantation, Zhejiang University, Hangzhou, 310003 Zhejiang Province China.

Published: July 2019

Background: Mounting evidences have demonstrated that HCC patients with or without cirrhosis possess different clinical characteristics, tumor development and prognosis. However, few studies directly investigated the underlying molecular mechanisms between non-cirrhotic HCC and cirrhotic HCC.

Methods: The clinical information and RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of HCC with or without cirrhosis were obtained by R software. Functional annotation and pathway enrichment analysis were performed by Enrichr. Protein-protein interaction (PPI) network was established through STRING and mapped to Cytoscape to identify hub genes. MicroRNAs were predicted through miRDB database. Furthermore, correlation analysis between selected genes and miRNAs were conducted via starBase database. MiRNAs expression levels between HCC with or without cirrhosis and corresponding normal liver tissues were further validated through GEO datasets. Finally, expression levels of key miRNAs and target genes were validated through qRT-PCR.

Results: Between 132 non-cirrhotic HCC and 79 cirrhotic HCC in TCGA, 768 DEGs were acquired, mainly involved in neuroactive ligand-receptor interaction pathway. According to the result from gene expression analysis in TCGA, , , , and were renamed as key genes and selected for further investigation. Survival analysis indicated that upregulated correlated with worse OS in cirrhotic HCC. Furthermore, ROC analysis revealed the significant diagnostic values of and in cirrhotic HCC, and , in non-cirrhotic HCC. Next, 517 miRNAs were predicted to target the 5 key genes. Correlation analysis confirmed that 16 of 517 miRNAs were negatively regulated the key genes. By detecting the expression levels of these key miRNAs from GEO database, we found 4 miRNAs have high research values. Finally, potential miRNA-mRNA networks were constructed based on the results of qRT-PCR.

Conclusion: In silico analysis, we first constructed the miRNA-mRNA regulatory networks in non-cirrhotic HCC and cirrhotic HCC.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637578PMC
http://dx.doi.org/10.1186/s12935-019-0901-3DOI Listing

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