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Candidate lncRNA–miRNA–mRNA network in predicting hepatocarcinogenesis with cirrhosis: an integrated bioinformatics analysis

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to explore the potential competing endogenous RNA (ceRNA) network in forecasting HCC development in patients with cirrhosis through a comprehensive bioinformatic analysis.

Methods

Data mining from GEO and TCGA databases was employed to dig a spectrum of differentially expressed mRNA, lncRNA and miRNA profiles. Their expression was confirmed by RT-PCR in matched HCC cohorts (n = 6/group). The ceRNA network was constructed by co-expression analysis. Their reciprocal regulations and their roles in epithelial-to-mesenchymal transition (EMT) process were validated by gain- and loss-of-function experiments at the cellular level. Kaplan–Meier method was applied to reveal prognostic values.

Results

By intersecting differentially expressed genes (DEGs) in GEO and TCGA data sets and Pearson correlation analysis, 20 mRNAs, 24 miRNAs and 41 lncRNAs were identified. Of these, FOXD2-AS1, BLVRA and CYTH2 were markedly upregulated in HCC tissues and HCC cells with high metastatic potential (MHCC97H) compared with their adjacent normal/cirrhotic tissues and L02 and MHCC97L cells. However, dysregulated miR-139-5p exhibited the opposite expression pattern. Using miRanda algorithms, FOXD2-AS1, BLVRA and CYTH2 showed potential binding sites for miR-139-5p. FOXD2-AS1 knockdown induced a marked increase in miR-139-5p and EMT inhibition. The loss of miR-139-5p led to an increase in BLVRA and CYTH2 expression and EMT process. Conversely, miR-139-5p overexpression suppressed BLVRA and CYTH expression and EMT process. FOXD2-AS1, miR-139-5p, BLVRA and CYTH2 highly correlated with prognosis in patients with HCC.

Conclusion

FOXD2-AS1/miR-139-5p/BLVRA or CYTH2 axis might be the underlying molecular mechanism that dissects HCC development caused by cirrhosis.

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Abbreviations

ceRNA:

Competing endogenous RNA

EMT:

Epithelial-to-mesenchymal transition

DEG:

Differentially expressed genes

HCC:

Hepatocellular carcinoma

ncRNA:

Non-coding RNAs

GEO:

Gene Expression Omnibus

TCGA:

The Cancer Genome Atlas

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomae

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Funding

This work was supported by the Innovation Fund of Science and Technology Commission of Shanghai Municipality (No. 15411950501, 15411950507 and 17140902700).

Author information

Author notes

  1. Rui Zhang, Ying-yi Jiang and Kun Xiao contributed equally to the work.

Authors and Affiliations

  1. Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Rui Zhang, Ying-yi Jiang, Xiao-quan Huang, Jian Wang & Shi-yao Chen

  2. Center of Evidence-Based Medicine, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Rui Zhang, Ying-yi Jiang, Xiao-quan Huang & Shi-yao Chen

  3. Shanghai Institute of Liver Disease, 180 Fenglin Road, Shanghai, 200032, China

    Rui Zhang, Ying-yi Jiang, Xiao-quan Huang, Jian Wang & Shi-yao Chen

  4. Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Kun Xiao

  5. Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Shi-yao Chen

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  1. Rui Zhang
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Contributions

SYC conceived and designed the study. RZ, YYJ and KX performed the experiments. RZ, YYJ, KX and JW made statistical analysis. RZ and SYC analyzed the data and wrote the manuscript. RZ, XQH and SYC revised the manuscript.

Corresponding author

Correspondence to Shi-yao Chen.

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The authors have declared that no competing interest exists.

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Zhang, R., Jiang, Yy., Xiao, K. et al. Candidate lncRNA–miRNA–mRNA network in predicting hepatocarcinogenesis with cirrhosis: an integrated bioinformatics analysis. J Cancer Res Clin Oncol 146, 87–96 (2020). https://doi.org/10.1007/s00432-019-03090-z

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  • DOI: https://doi.org/10.1007/s00432-019-03090-z

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