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

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Abstract

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.

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Abbreviations

HCC:

Hepatocellular carcinoma

AEI:

Allelic expression imbalance

HOM:

One site is homozygous

NO:

Not tumor-specific AEI site

gDNA:

Genomic DNA

cDNA:

Complementary DNA

References

  1. Llovet JM, Burroughs A, Bruix J. Hepatocellular carcinoma. Lancet. 2003;362:1907–17.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11(8):533–8.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Falls JG, Pulford DJ, Wylie AA, Jirtle RL. Genomic imprinting: implications for human disease. Am J Pathol. 1999;154(3):635–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

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).

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    Authors and Affiliations

    1. Key Laboratory of Systems Biomedicine (Ministry of Education) and Collaborative Innovation Center of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, 200240, China

      Qiudao Wang, Yan An & Jian Huang

    2. National Engineering Center for Biochip at Shanghai, Shanghai, 201203, China

      Qiudao Wang, Qing Yuan, Yao Qi, Ying Ou & Jian Huang

    3. Shenzhen Key Laboratory of Infection and Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, Shenzhen, 518112, China

      Jian Huang

    4. Shanghai-MOST Key Laboratory for Disease and Health Genomics, Chinese National Human Genome Center, Shanghai, 201203, China

      Jian Huang

    5. Peking University Shenzhen Hospital, Shenzhen, 518036, China

      Junhui Chen

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    1. Qiudao Wang
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    Correspondence to Jian Huang.

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    Qiudao Wang and Yan An contributed equally to this work.

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    Wang, Q., An, Y., Yuan, Q. et al. Identification of allelic expression imbalance genes in human hepatocellular carcinoma through massively parallel DNA and RNA sequencing. Med Oncol 33, 38 (2016). https://doi.org/10.1007/s12032-016-0751-y

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