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Identification of latent biomarkers in hepatocellular carcinoma by ultra-deep whole-transcriptome sequencing

Oncogene volume 33, pages 4786–4794 (2014)Cite this article

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A Corrigendum to this article was published on 01 August 2016

Abstract

There is an urgent need to identify biomarkers for hepatocellular carcinoma due to limited treatment options and the poor prognosis of this common lethal disease. Whole-transcriptome shotgun sequencing (RNA-Seq) provides new possibilities for biomarker identification. We sequenced ∼250 million pair-end reads from a pair of adjacent normal and tumor liver samples. With the aid of bioinformatics tools, we determined the transcriptome landscape and sought novel biomarkers by further empirical validations in 55 pairs of adjacent normal and tumor liver samples with various viral statuses such as HBV(+), HCV(+) and HBV(−)HCV(−). We identified a novel gene with coding regions, termed DUNQU1, which has a tissue-specific expression pattern in tumor liver samples of HCV(+) and HBV(−)HCV(−) hepatocellular carcinomas. Overexpression of DUNQU1 in Huh7 cell lines enhances the ability to form colonies in soft agar. Also, we identified three novel differentially-expressed protein-coding genes (ALG1L, SERPINA11 and TMEM82) that lack documented expression profiles in liver cancer and showed that the level of SREPINA11 is correlated with pathology stages. Moreover, we showed that the alternative splicing event of FGFR2 is associated with virus infection, tumor size, cirrhosis and tumor recurrence. The findings indicate that these new markers of hepatocellular carcinoma may be of value in improving prognosis and could have potential as new targets for developing new treatment options.

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Accession codes

Accessions

GenBank/EMBL/DDBJ

Abbreviations

HCC:

hepatocellular carcinoma

RNA-Seq:

whole-transcriptome shotgun sequencing

HCV:

hepatitis C virus

HBV:

hepatitis B virus

FPKM:

fragments per kilobase of transcript per million mapped reads

DNA:

deoxyribonucleic acid

GTF:

Gene Transfer Format

AS:

alternative splicing

CA:

cassette exon

AA:

alternative acceptor

AD:

alternative donor

IR:

intron retention

PCR:

polymerase chain reaction

DE genes:

differentially expressed protein-coding genes

TSS:

transcription start side

PTC:

premature termination codon

IHC:

immunohistochemistry

RT-qPCR:

reverse transcription quantitative real time polymerase chain reaction.

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Acknowledgements

We thank the Taiwan Liver Cancer Network for providing the liver tumor tissue samples and related clinical data (all are anonymous) for this work. This network currently includes five major medical centers in Taiwan (National Taiwan University Hospital, Chang-Gung Memorial Hospital-Linko, Veteran General Hospital-Taichung, Chang-Gung Memorial Hospital-Kaohsiung and Veteran General Hospital-Kaohsiung). Taiwan Liver Cancer Network is supported by grants from the National Science Council (NSC94–3112-B-182–002, NSC97–3112-B-182–004) and National Health Research Institutes, Taiwan. We also want to thank National Core Facility Program for Biotechnology (Bioinformatics Consortium of Taiwan, NSC102–2319-B-010–002), National Research Program for Biopharmaceuticals (NRPB, NSC10102325-B-492–001) and National Center for High-performance Computing of National Applied Research Laboratories (NCHC, NARLabs) for providing computing and storage resources. Finally, we want to thank Professor Adrian R Krainer for his valuable comments on the manuscript and hosting at Cold Spring Harbor Laboratory.

This research was supported by grants from the National Science Council (NSC101–2627-B-010–001- and NSC102-2627-B-010-001-), Taipei Veterans General Hospital (V102E2–006), the National Health Research Institutes (NHRI-EX102–10029BI), Ministry of Economic Affairs (101-EC-17-A-17-S1–152) and the Ministry of Education, Aim for the Top University Plan (National Yang-Ming University) to C-YF. Huang. This research was also supported by the research aboard grant from the National Science Council (NSC 100–2917-I-010–001) to K-T. Lin.

Author information

Author notes

  1. K-T Lin and Y-J Shann: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan

    K-T Lin & C-Y F Huang

  2. Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan

    Y-J Shann & C-Y F Huang

  3. Division of General Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan

    G-Y Chau

  4. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    C-N Hsu

  5. USC/Information Sciences Institute, Marina del Rey, CA, USA

    C-N Hsu

  6. Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    C-N Hsu

  7. Cancer Research Center and Genome Research Center, National Yang-Ming University,, Taipei, Taiwan

    C-Y F Huang

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Correspondence to C-Y F Huang.

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Author contributions

Kuan-Ting Lin and Yih-Jyh Shann designed the study, analyzed, interpreted the data and drafted the article. Kuan-Ting Lin performed RNA-Seq and statistical analysis. Yih-Jyh Shann performed the experimental validations. Gar-Yang Chau, Chun-Nan Hsu and Chi-Ying F Huang participated in the design of the study. All authors agreed to publication.

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Lin, KT., Shann, YJ., Chau, GY. et al. Identification of latent biomarkers in hepatocellular carcinoma by ultra-deep whole-transcriptome sequencing. Oncogene 33, 4786–4794 (2014). https://doi.org/10.1038/onc.2013.424

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