Medical Oncology

, 33:38 | Cite as

Identification of allelic expression imbalance genes in human hepatocellular carcinoma through massively parallel DNA and RNA sequencing

  • Qiudao Wang
  • Yan An
  • Qing Yuan
  • Yao Qi
  • Ying Ou
  • Junhui Chen
  • Jian HuangEmail author
Original Paper


Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. The prognosis and treatment of this disease have changed little in recent decades because the mechanisms underlying most events of this disease remain obscure. Allelic variation of gene expression is associated with many important biological processes, which provide a new perspective to understand HCC pathogenesis at the molecular level. To identify allelic expression imbalance (AEI) genes in HCCs, we developed a computational method that considered accurate mapping and vigorous AEI detection using paired DNA-seq and RNA-seq data. We analyzed the DNA-seq and RNA-seq data derived from two HCC samples and two cell lines. By applying a strict criterion, a total of 203 tumor-specific AEI genes were identified with high confidence, and several genes have been reported to be associated with the migration or proliferation of cancer cells, such as the genes RELN and DHRS3. In addition, we also found some novel AEI genes in HCCs, such as HNRNPR and PTAFR. Our study provides new insight into AEI events that may contribute to understanding gene expression regulation, cell proliferation and migration, and tumorigenesis.


Allelic expression imbalance Hepatocellular carcinoma DNA-seq RNA-seq Cancer Tumor-specific AEI gene 



Hepatocellular carcinoma


Allelic expression imbalance


One site is homozygous


Not tumor-specific AEI site


Genomic DNA


Complementary DNA



This work was supported by Grants from the National High Technology Research and Development Program of China (2012AA02A205), the National Natural Science Foundation of China (81472639 and 81272306), the Shanghai Commission for Science and Technology (15431902900), and the Program of Shenzhen Science Technology and Innovation Committee (JCYJ20130329171031740, CXZZ20130515163643, and JCYJ20120831144704366).

Complaince with ethical standards

Conflicts of interest

The authors disclose no potential conflicts of interest.

Supplementary material

12032_2016_751_MOESM1_ESM.xlsx (32 kb)
Supplementary material 1 (XLSX 31 kb)


  1. 1.
    Llovet JM, Burroughs A, Bruix J. Hepatocellular carcinoma. Lancet. 2003;362:1907–17.CrossRefPubMedGoogle Scholar
  2. 2.
    Hu X, Wan S, Ou Y, Zhou B, Zhu J, Yi X, et al. RNA over-editing of BLCAP contributes to hepatocarcinogenesis identified by whole-genome and transcriptome sequencing. Cancer Lett. 2015;357(2):510–9.CrossRefPubMedGoogle Scholar
  3. 3.
    Fujimoto A, Totoki Y, Abe T, Boroevich KA, Hosoda F, Nguyen HH, et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet. 2012;44(7):760–4.CrossRefPubMedGoogle Scholar
  4. 4.
    Guichard C, Amaddeo G, Imbeaud S, Ladeiro Y, Pelletier L, Maad IB, et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat Genet. 2012;44(6):694–8.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Huang J, Deng Q, Wang Q, Li K-Y, Dai J-H, Li N, et al. Exome sequencing of hepatitis B virus-associated hepatocellular carcinoma. Nat Genet. 2012;44(10):1117–21.CrossRefPubMedGoogle Scholar
  6. 6.
    Sung W-K, Zheng H, Li S, Chen R, Liu X, Li Y, et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet. 2012;44(7):765–9.CrossRefPubMedGoogle Scholar
  7. 7.
    Totoki Y, Tatsuno K, Yamamoto S, Arai Y, Hosoda F, Ishikawa S, et al. High-resolution characterization of a hepatocellular carcinoma genome. Nat Genet. 2011;43(5):464–9.CrossRefPubMedGoogle Scholar
  8. 8.
    Montgomery SB, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464(7289):773–7.CrossRefPubMedGoogle Scholar
  9. 9.
    Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464(7289):768–72.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Messina DN, Glasscock J, Gish W, Lovett M. An ORFeome-based analysis of human transcription factor genes and the construction of a microarray to interrogate their expression. Genome Res. 2004;14(10b):2041–7.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11(8):533–8.CrossRefPubMedGoogle Scholar
  12. 12.
    Blanchette M, Bataille AR, Chen X, Poitras C, Laganière J, Lefèbvre C, et al. Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res. 2006;16(5):656–68.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Chan TL, Yuen ST, Kong CK, Chan YW, Chan AS, Ng WF, et al. Heritable germline epimutation of MSH2 in a family with hereditary nonpolyposis colorectal cancer. Nat Genet. 2006;38(10):1178–83.CrossRefPubMedGoogle Scholar
  14. 14.
    Ligtenberg MJ, Kuiper RP, Chan TL, Goossens M, Hebeda KM, Voorendt M, et al. Heritable somatic methylation and inactivation of MSH2 in families with Lynch syndrome due to deletion of the 3′ exons of TACSTD1. Nat Genet. 2009;41(1):112–7.CrossRefPubMedGoogle Scholar
  15. 15.
    Yan H, Dobbie Z, Gruber SB, Markowitz S, Romans K, Giardiello FM, et al. Small changes in expression affect predisposition to tumorigenesis. Nat Genet. 2002;30(1):25–6.CrossRefPubMedGoogle Scholar
  16. 16.
    Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S, Jiang Z, et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 2014;15(8):405.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM. A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res. 2011;21(10):1728–37.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009;25(24):3207–12.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ramaswami G, Zhang R, Piskol R, Keegan LP, Deng P, O’Connell MA, et al. Identifying RNA editing sites using RNA sequencing data alone. Nat Methods. 2013;10(2):128–32.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 2010;38(14):4570–8.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38(18):e178.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Lee MP. Allele-specific gene expression and epigenetic modifications and their application to understanding inheritance and cancer. Biochim Biophys Acta (BBA) Gene Regul Mech. 2012;1819(7):739–42.CrossRefGoogle Scholar
  24. 24.
    Tan AC, Fan J-B, Karikari C, Bibikova M, Wickham Garcia E, Zhou L, et al. Allele-specific expression in the germline of patients with familial pancreatic cancer: an unbiased approach to cancer gene discovery. Cancer Biol Ther. 2008;7(1):135–44.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43(5):491–8.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Nookaew I, Papini M, Pornputtpong N, Scalcinati G, Fagerberg L, Uhlén M, et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012;40(20):10084–97.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Piskol R, Ramaswami G, Li JB. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 2013;93(4):641–51.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Romanel A, Lago S, Prandi D, Sboner A, Demichelis F. ASEQ: fast allele-specific studies from next-generation sequencing data. BMC Med Genomics. 2015;8(1):9.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Chen LY, Wei K-C, Huang AC-Y, Wang K, Huang C-Y, Yi D, et al. RNASEQR—a streamlined and accurate RNA-seq sequence analysis program. Nucleic Acids Res. 2012;40(6):1–12.CrossRefGoogle Scholar
  32. 32.
    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Falls JG, Pulford DJ, Wylie AA, Jirtle RL. Genomic imprinting: implications for human disease. Am J Pathol. 1999;154(3):635–47.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Dohi O, Takada H, Wakabayashi N, Yasui K, Sakakura C, Mitsufuji S, et al. Epigenetic silencing of RELN in gastric cancer. Int J Oncol. 2010;36(1):85–92.PubMedGoogle Scholar
  35. 35.
    Jahromi MS, Putnam AR, Druzgal C, Wright J, Spraker-Perlman H, Kinsey M, et al. Molecular inversion probe analysis detects novel copy number alterations in Ewing sarcoma. Cancer Genet. 2012;205(7):391–404.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Okamura Y, Nomoto S, Kanda M, Hayashi M, Nishikawa Y, Fujii T, et al. Reduced expression of reelin (RELN) gene is associated with high recurrence rate of hepatocellular carcinoma. Ann Surg Oncol. 2011;18(2):572–9.CrossRefPubMedGoogle Scholar
  37. 37.
    Sato N, Fukushima N, Chang R, Matsubayashi H, Goggins M. Differential and epigenetic gene expression profiling identifies frequent disruption of the RELN pathway in pancreatic cancers. Gastroenterology. 2006;130(2):548–65.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Qiudao Wang
    • 1
    • 2
  • Yan An
    • 1
  • Qing Yuan
    • 2
  • Yao Qi
    • 2
  • Ying Ou
    • 2
  • Junhui Chen
    • 5
  • Jian Huang
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.Key Laboratory of Systems Biomedicine (Ministry of Education) and Collaborative Innovation Center of Systems Biomedicine, Shanghai Center for Systems BiomedicineShanghai Jiao Tong UniversityShanghaiChina
  2. 2.National Engineering Center for Biochip at ShanghaiShanghaiChina
  3. 3.Shenzhen Key Laboratory of Infection and Immunity, Shenzhen Third People’s HospitalGuangdong Medical CollegeShenzhenChina
  4. 4.Shanghai-MOST Key Laboratory for Disease and Health GenomicsChinese National Human Genome CenterShanghaiChina
  5. 5.Peking University Shenzhen HospitalShenzhenChina

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