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In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC
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. METHOD...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637578/ https://www.ncbi.nlm.nih.gov/pubmed/31346321 http://dx.doi.org/10.1186/s12935-019-0901-3 |
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author | Ding, Bisha Lou, Weiyang Liu, Jingxing Li, Ruohan Chen, Jing Fan, Weimin |
author_facet | Ding, Bisha Lou, Weiyang Liu, Jingxing Li, Ruohan Chen, Jing Fan, Weimin |
author_sort | Ding, Bisha |
collection | PubMed |
description | 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, CCL19, CCL25, CNR1, PF4 and PPBP were renamed as key genes and selected for further investigation. Survival analysis indicated that upregulated CNR1 correlated with worse OS in cirrhotic HCC. Furthermore, ROC analysis revealed the significant diagnostic values of PF4 and PPBP in cirrhotic HCC, and CCL19, CCL25 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12935-019-0901-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6637578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66375782019-07-25 In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC Ding, Bisha Lou, Weiyang Liu, Jingxing Li, Ruohan Chen, Jing Fan, Weimin Cancer Cell Int Primary Research 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, CCL19, CCL25, CNR1, PF4 and PPBP were renamed as key genes and selected for further investigation. Survival analysis indicated that upregulated CNR1 correlated with worse OS in cirrhotic HCC. Furthermore, ROC analysis revealed the significant diagnostic values of PF4 and PPBP in cirrhotic HCC, and CCL19, CCL25 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12935-019-0901-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-18 /pmc/articles/PMC6637578/ /pubmed/31346321 http://dx.doi.org/10.1186/s12935-019-0901-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Primary Research Ding, Bisha Lou, Weiyang Liu, Jingxing Li, Ruohan Chen, Jing Fan, Weimin In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title | In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title_full | In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title_fullStr | In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title_full_unstemmed | In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title_short | In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC |
title_sort | in silico analysis excavates potential biomarkers by constructing mirna-mrna networks between non-cirrhotic hcc and cirrhotic hcc |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637578/ https://www.ncbi.nlm.nih.gov/pubmed/31346321 http://dx.doi.org/10.1186/s12935-019-0901-3 |
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