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BMC Med Genomics. 2019 Jan 31;12(Suppl 1):25. doi: 10.1186/s12920-019-0472-0.

Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma.

Author information

1
Vermont Genetics Network, University of Vermont, Burlington, VT, 05405, USA. fan.zhang@uvm.edu.
2
Department of Biology, University of Vermont, Burlington, VT, 05405, USA. fan.zhang@uvm.edu.
3
School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0606, USA.
4
Department of Neurosciences, School of Medicine, University of California, San Diego, 9500 Gilman Drive #0949, La Jolla, CA, 92093, USA.
5
Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.
6
Vermont Genetics Network, University of Vermont, Burlington, VT, 05405, USA.
7
Department of Biology, University of Vermont, Burlington, VT, 05405, USA.

Abstract

BACKGROUND:

While changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence.

RESULTS:

Therefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq.

CONCLUSIONS:

Our results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.

KEYWORDS:

Biomarker discovery; Hepatocellular carcinoma; Long non-coding RNA

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