Open Access

Potential role of microRNA‑223‑3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics

  • Authors:
    • Rui Zhang
    • Li‑Jie Zhang
    • Mei‑Ling Yang
    • Lan‑Shan Huang
    • Gang Chen
    • Zhen‑Bo Feng
  • View Affiliations

  • Published online on: November 27, 2017     https://doi.org/10.3892/mmr.2017.8167
  • Pages: 2211-2228
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aims of the present study were to examine the potential role of microRNA‑233‑3p (miR)‑223‑3p in the tumorigenesis of hepatocellular carcinoma (HCC), and to investigate its diagnostic accuracy and potential molecular mechanisms. The expression data of miR‑223‑3p in HCC were obtained from the Gene Expression Omnibus (GEO). Data for the precursor miR‑223 were obtained from The Cancer Genome Atlas (TCGA). The diagnostic role of miR‑223‑3p was identified by the receiver operating curve (ROC), and the diagnostic value of miR‑223‑3p in HCC was calculated from qualified reports in the literature. In addition, associated data from the GEO, TCGA and qualified experiments were pooled for comprehensive meta‑analysis. Genes, which intersected between online prediction databases, natural language processing and differentially expressed genes from TCGA were regarded as potential targets of miR‑223‑3p in HCC. The Gene Ontology enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes pathways of potential targets were performed using the Database for Annotation, Visualization and Integrated Discovery. The protein‑protein interactions were mapped using the Search Tool for the Retrieval of Interacting Genes. Among 15 qualified microarray data sets from GEO, seven showed that a significantly lower level of miR‑223‑3p was present in the HCC tissues, compared with that in non‑cancerous tissues (P<0.05). In addition, five GEO data sets revealed diagnostic values of miR‑223‑3p, with an area under the curve (AUC) of >0.80 (P<0.05). The diagnostic accuracy of the precursor miR‑223 in TCGA was also calculated (AUC=0.78, P<0.05). Similarly, the precursor miR‑223 showed a higher level of downregulation in HCC tissues, compared with that in healthy controls in TCGA (P<0.001). A summary ROC was also calculated as 0.89 (95% CI, 0.85‑0.91) in the meta‑analysis. A total of 72 potential targets were extracted, mainly involved in the terms ‘microRNAs in cancer’, ‘ATP binding’ and ‘prostate cancer’. Five potential target genes were considered the hub genes of miR‑223‑3p in HCC, including checkpoint kinase 1, DNA methyltransferase 1, baculoviral IAP repeat containing 5, kinesin family member 23, and collagen, type I, α1. Based on TCGA, the hub genes were significantly upregulated in HCC (P<0.05). Collectively, these results showed that miR‑223‑3p may be crucial in HCC carcinogenesis showing high diagnostic accuracy, and may be mediated by several hub genes.
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February-2018
Volume 17 Issue 2

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Zhang R, Zhang LJ, Yang ML, Huang LS, Chen G and Feng ZB: Potential role of microRNA‑223‑3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics. Mol Med Rep 17: 2211-2228, 2018
APA
Zhang, R., Zhang, L., Yang, M., Huang, L., Chen, G., & Feng, Z. (2018). Potential role of microRNA‑223‑3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics. Molecular Medicine Reports, 17, 2211-2228. https://doi.org/10.3892/mmr.2017.8167
MLA
Zhang, R., Zhang, L., Yang, M., Huang, L., Chen, G., Feng, Z."Potential role of microRNA‑223‑3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics". Molecular Medicine Reports 17.2 (2018): 2211-2228.
Chicago
Zhang, R., Zhang, L., Yang, M., Huang, L., Chen, G., Feng, Z."Potential role of microRNA‑223‑3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics". Molecular Medicine Reports 17, no. 2 (2018): 2211-2228. https://doi.org/10.3892/mmr.2017.8167