臺灣博碩士論文加值系統

肝癌這個常見的致命疾病僅有有限的治療選項以及預後，所以尋找新的生物指標有迫切的需要。次世代的全轉錄體定序(RNA-Seq)提供了尋找生物指標的新可能性。透過RNA-Seq，我們從ㄧ對癌旁與腫瘤的肝臟樣本中定序了約兩億五千萬條雙向對讀的小片段序列。利用生物資訊工具，我們確立了這個病人的肝癌轉錄體全景。在55對不同病毒感染特徵的病人樣本中驗證，我們尋找全新還沒有被發現過的生物指標。我們找到了一個全新且具有編碼區的基因，我們將之命名為DUNQU1，取自中文“遁去的ㄧ”。這個基因在C型肝炎或是沒有病毒感染的病人中具有只表現在腫瘤組織的特性。在Huh7細胞株中加強DUNQU1的表現量可以強化其形成菌落(colony)的能力。此外，我們找到了三個表現量有差異的蛋白質編碼基(ALG1L, SERPINA11,以及 TMEM82)，但過去的肝癌文獻中沒有報導過。我們還發現SERPINA11的表現量與病理期別有相關。最後，我們發現FGFR2的選擇性剪接事件跟病毒感染，腫瘤大小，肝硬化，以及腫瘤的復發都有關聯。這些新的生物指標可能有改善預後的價值以及可能可以作為開發新療法的標的。

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.

誌謝.................................1
導讀/序.................................2
中文摘要.................................3
Abstract.................................4
Flow Chart of The Study.................................5
List of Figures.................................6
List of Tables.................................7
Abbreviations.................................8
Chapter 1: Introduction.................................9
1.1 Gene signatures in previous genome-wide studies of HCC.................................9
1.2 Limitations of gene expression microarrays.................................13
1.3 Alternative splicing (AS) events in hepatocellular carcinoma (HCC).................................14
1.4 The war of Encyclopedia of DNA Elements (ENCODE).................................14
1.5 Whole transcriptome shotgun sequencing.................................15
Chapter 2: Materials and Methods.................................17
2.1 Clinical samples and RNA-Seq experiment.................................17
2.2 cDNA synthesis, primers and PCR reaction conditions.................................18
2.3 Gene expression fold change.................................18
2.4 Identification of protein-coding genes not detectable by microarrays.................................18
2.5 Percentages of genome covered by short reads.................................19
2.6 Visualization of the alignment results.................................19
2.7 Differentially included exons and AS events.................................19
2.8 Statistical analysis and FGFR2-IIIc inclusion ratio.................................20
2.9 De novo assembly of the transcriptome.................................20
2.10 Plasmid and lentivirus protocol.................................20
2.11 Soft agar colony formation assay.................................21
Chapter 3: Results.................................22
3.1 The catalog of the transcriptome landscape of HCC.................................22
3.2 Novel differentially expressed protein-coding genes whose expression profiles were missing in liver cancer.................................22
3.3 Alternative splicing events show the changes in cell behaviors and may serve as new biomarkers of HCC.................................25
3.4 The switch from FGFR2-IIIb to FGFR2-IIIc in the liver tumors was sig-nificantly associated with virus infection, and increased FGFR2-IIIc inclusion ratio was associated with cirrhosis and tumor recurrence.................................28
3.5 A novel gene, termed DUNQU1, has a tissue-specific expression pattern and may play a role in liver tumorigenesis.................................32
Chapter 4: Discussion.................................38
Chapter 5: Conclusions.................................41
Chapter 6: Related Study.................................42
Appendices.................................44
Supplementary Figure 1.................................44
Supplementary Figure 2.................................45
Supplementary Figure 3.................................46
Supplementary Figure 4.................................47
Supplementary Figure 5.................................48
Supplementary Figure 6.................................49
Supplementary Figure 7.................................50
Supplementary Figure 8.................................51
Supplementary Figure 9.................................52
Supplementary Figure 10.................................53
Supplementary Figure 11.................................54
Supplementary Figure 12.................................55
Supplementary Figure 13.................................56
Supplementary Table 1.................................57
Supplementary Table 2.................................58
Supplementary Table 3.................................59
Supplementary Table 4.................................135
Supplementary Table 5.................................136
Supplementary Table 6.................................137
Supplementary Table 7.................................139
Supplementary Table 8.................................140
Supplementary Table 9.................................142
Supplementary Table 10.................................166
References.................................167
Publication.................................173

1 Cyranoski D. Chinese bioscience: The sequence factory. Nature 2010; 464:22-24.
2 Abecasis GR, Auton A, Brooks LD et al. An integrated map of genetic variation from 1,092 human
genomes. Nature 2012; 491:56-65.
3 Hilgers L. BGI's Young Chinese Scientists Will Map Any Genome. Bloomberg Businessweek:
Bloomberg Businessweek 2013.
4 Lan MY, Chen CL, Lin KT et al. From NPC therapeutic target identification to potential treatment
strategy. Mol Cancer Ther 2010; 9:2511-2523.
5 Chen MH, Yang WL, Lin KT et al. Gene expression-based chemical genomics identifies potential
therapeutic drugs in hepatocellular carcinoma. PLoS One 2011; 6:e27186.
6 Lamb J, Crawford ED, Peck D et al. The Connectivity Map: using gene-expression signatures to connect
small molecules, genes, and disease. Science 2006; 313:1929-1935.
7 Liu CH, Chen TC, Chau GY et al. An analysis of protein-protein interactions in cross-talk pathways
reveals CRKL as a novel prognostic marker in hepatocellular carcinoma. Mol Cell Proteomics 2013.
8 Chen TC, Lin, K.T., Lee, S.A., Chen, C.H., Huang, H.C., Lee, P.Y., Liu, Y.W., Kuo, Y.L., Lai, J.M., and
Huang, C.Y. Using an in situ proximity ligation assay to systematically profile endogenous protein-
protein interactions in a pathway network. Journal of Proteomics Research 2013; (revision).
9 Fan C, Oh DS, Wessels L et al. Concordance among gene-expression-based predictors for breast cancer.
The New England journal of medicine 2006; 355:560-569.
10 Cardoso F, Van't Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ. Clinical application of the 70-
gene profile: the MINDACT trial. Journal of clinical oncology : official journal of the American Society
of Clinical Oncology 2008; 26:729-735.
11 Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular
signatures database (MSigDB) 3.0. Bioinformatics 2011; 27:1739-1740.
12 Su WH, Chao CC, Yeh SH, Chen DS, Chen PJ, Jou YS. OncoDB.HCC: an integrated oncogenomic
database of hepatocellular carcinoma revealed aberrant cancer target genes and loci. Nucleic acids
research 2007; 35:D727-731.
13 He B, Qiu X, Li P, Wang L, Lv Q, Shi T. HCCNet: an integrated network database of hepatocellular
carcinoma. Cell Res 2010; 20:732-734.
14 Jiao X, Sherman BT, Huang da W et al. DAVID-WS: a stateful web service to facilitate gene/protein list
analysis. Bioinformatics 2012; 28:1805-1806.
15 Kuhn M, Szklarczyk D, Franceschini A et al. STITCH 2: an interaction network database for small
molecules and proteins. Nucleic acids research 2010; 38:D552-556.
16 Wishart DS, Knox C, Guo AC et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets.
Nucleic acids research 2008; 36:D901-906.
16617 Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R. ConsensusPathDB: toward a
more complete picture of cell biology. Nucleic Acids Res 2011; 39:D712-717.
18 Lee SA, Chan CH, Chen TC et al. POINeT: protein interactome with sub-network analysis and hub
prioritization. BMC Bioinformatics 2009; 10:114.
19 Chen CH. Generalized association plots: Information visualization via iteratively generated correlation
matrices. Statistica Sinica 2002; 12:7-30.
20 Wu HM, Tien, Y. J., and Chen, C. GAP: A graphical environment for matrix visualization and cluster
analysis. Computational Statistics &; Data Analysis 2010; 54:767–778.
21 Hsu CN, Lai JM, Liu CH et al. Detection of the inferred interaction network in hepatocellular carcinoma
from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online). BMC Bioinformatics 2007; 8:66.
22 Kuan-Ting Lin, Chia-Hung Liu, Jen-Jie Chiou, Wen-Hsien Tseng, Kuang-Lung Lin, Hsu C-N. Gene name
service: no-nonsense alias resolution service for Homo Sapiens genes. Proceeding WI-IATW '07
Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent
Agent Technology - Workshops 2007:185-188.
23 Gautier L, Cope L, Bolstad BM, Irizarry RA. affy--analysis of Affymetrix GeneChip data at the probe
level. Bioinformatics 2004; 20:307-315.
24 Kornblihtt AR, Schor IE, Allo M, Dujardin G, Petrillo E, Munoz MJ. Alternative splicing: a pivotal step
between eukaryotic transcription and translation. Nat Rev Mol Cell Biol 2013; 14:153-165.
25 Pickrell JK, Pai AA, Gilad Y, Pritchard JK. Noisy splicing drives mRNA isoform diversity in human cells.
PLoS Genet 2010; 6:e1001236.
26 Grosso AR, Martins S, Carmo-Fonseca M. The emerging role of splicing factors in cancer. EMBO Rep
2008; 9:1087-1093.
27 Karni R, de Stanchina E, Lowe SW, Sinha R, Mu D, Krainer AR. The gene encoding the splicing factor
SF2/ASF is a proto-oncogene. Nat Struct Mol Biol 2007; 14:185-193.
28 Cooper TA, Wan L, Dreyfuss G. RNA and disease. Cell 2009; 136:777-793.
29 Huang R, Xing Z, Luan Z, Wu T, Wu X, Hu G. A specific splicing variant of SVH, a novel human
armadillo repeat protein, is up-regulated in hepatocellular carcinomas. Cancer research 2003;
63:3775-3782.
30 Saito Y, Kanai Y, Sakamoto M, Saito H, Ishii H, Hirohashi S. Overexpression of a splice variant of DNA
methyltransferase 3b, DNMT3b4, associated with DNA hypomethylation on pericentromeric satellite
regions during human hepatocarcinogenesis. Proceedings of the National Academy of Sciences of the
United States of America 2002; 99:10060-10065.
31 Liu CH, Lin, K.T., Huang, C.F., Shann, Y.J., Lin, Y.S., et al. Genome-Wide Detection of Putative
Oncofetal Genes in Human Hepatocellular Carcinoma by Splicing Pattern Comparison. iConcept
Transaction on Computational Intelligence in Bioinformatics (TCIB) 2011.
32 Finishing the euchromatic sequence of the human genome. Nature 2004; 431:931-945.
16733 Harris S. Biotechnology: the next engine of growth for Taiwan's economy? The Taiwanese government is
investing heavily in biotech infrastructure and researchers in an effort to repeat the country's success with
IT technologies. EMBO reports 2002; 3:598-600.
34 Birney E, Stamatoyannopoulos JA, Dutta A et al. Identification and analysis of functional elements in 1%
of the human genome by the ENCODE pilot project. Nature 2007; 447:799-816.
35 Ecker JR, Bickmore WA, Barroso I, Pritchard JK, Gilad Y, Segal E. Genomics: ENCODE explained.
Nature 2012; 489:52-55.
36 Kolata G. Bits of Mystery DNA, Far From ‘Junk,’ Play Crucial Role. The New York Times: The New
York Times 2012.
37 Djebali S, Davis CA, Merkel A et al. Landscape of transcription in human cells. Nature 2012;
489:101-108.
38 Clark MB, Amaral PP, Schlesinger FJ et al. The reality of pervasive transcription. PLoS Biol 2011;
9:e1000625; discussion e1001102.
39 Doolittle WF. Is junk DNA bunk? A critique of ENCODE. Proceedings of the National Academy of
Sciences of the United States of America 2013; 110:5294-5300.
40 Eddy SR. The C-value paradox, junk DNA and ENCODE. Curr Biol 2012; 22:R898-899.
41 Jeanson N. The Resurrection of 'Junk DNA'? Available from: http://www.icr.org/article/7383/.
42 Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;
10:57-63.
43 Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian
transcriptomes by RNA-Seq. Nat Methods 2008; 5:621-628.
44 Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical
reproducibility and comparison with gene expression arrays. Genome Res 2008; 18:1509-1517.
45 t Hoen PA, Friedlander MR, Almlof J et al. Reproducibility of high-throughput mRNA and small RNA
sequencing across laboratories. Nature biotechnology 2013.
46 Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 2011;
12:87-98.
47 Sultan M, Schulz MH, Richard H et al. A global view of gene activity and alternative splicing by deep
sequencing of the human transcriptome. Science 2008; 321:956-960.
48 Morin R, Bainbridge M, Fejes A et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA
and massively parallel short-read sequencing. Biotechniques 2008; 45:81-94.
49 Lander ES. Initial impact of the sequencing of the human genome. Nature 2011; 470:187-197.
50 Trapnell C, Williams BA, Pertea G et al. Transcript assembly and quantification by RNA-Seq reveals
unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;
28:511-515.
51 Flicek P, Amode MR, Barrell D et al. Ensembl 2011. Nucleic Acids Res 2011; 39:D800-806.
16852 Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics
2009; 25:1105-1111.
53 Li H, Handsaker B, Wysoker A et al. The Sequence Alignment/Map format and SAMtools.
Bioinformatics 2009; 25:2078-2079.
54 Wang K, Singh D, Zeng Z et al. MapSplice: accurate mapping of RNA-seq reads for splice junction
discovery. Nucleic Acids Res 2010; 38:e178.
55 Wu J, Akerman M, Sun S, McCombie WR, Krainer AR, Zhang MQ. SpliceTrap: a method to quantify
alternative splicing under single cellular conditions. Bioinformatics 2011; 27:3010-3016.
56 Barrett T, Troup DB, Wilhite SE et al. NCBI GEO: archive for functional genomics data sets--10 years
on. Nucleic Acids Res 2011; 39:D1005-1010.
57 Kapushesky M, Adamusiak T, Burdett T et al. Gene Expression Atlas update--a value-added database of
microarray and sequencing-based functional genomics experiments. Nucleic Acids Res 2012;
40:D1077-1081.
58 Parkinson H, Sarkans U, Kolesnikov N et al. ArrayExpress update--an archive of microarray and high-
throughput sequencing-based functional genomics experiments. Nucleic Acids Res 2011; 39:D1002-1004.
59 Rhodes DR, Kalyana-Sundaram S, Mahavisno V et al. Oncomine 3.0: genes, pathways, and networks in a
collection of 18,000 cancer gene expression profiles. Neoplasia 2007; 9:166-180.
60 Takai H, Wang RC, Takai KK, Yang H, de Lange T. Tel2 regulates the stability of PI3K-related protein
kinases. Cell 2007; 131:1248-1259.
61 David CJ, Manley JL. Alternative pre-mRNA splicing regulation in cancer: pathways and programs
unhinged. Genes Dev 2010; 24:2343-2364.
62 Warzecha CC, Sato TK, Nabet B, Hogenesch JB, Carstens RP. ESRP1 and ESRP2 are epithelial cell-type-
specific regulators of FGFR2 splicing. Mol Cell 2009; 33:591-601.
63 Amann T, Bataille F, Spruss T et al. Reduced expression of fibroblast growth factor receptor 2IIIb in
hepatocellular carcinoma induces a more aggressive growth. Am J Pathol 2010; 176:1433-1442.
64 Shapiro IM, Cheng AW, Flytzanis NC et al. An EMT-driven alternative splicing program occurs in human
breast cancer and modulates cellular phenotype. PLoS Genet 2011; 7:e1002218.
65 Kleino I, Ortiz RM, Huovila AP. ADAM15 gene structure and differential alternative exon use in human
tissues. BMC Mol Biol 2007; 8:90.
66 Ortiz RM, Karkkainen I, Huovila AP. Aberrant alternative exon use and increased copy number of human
metalloprotease-disintegrin ADAM15 gene in breast cancer cells. Genes Chromosomes Cancer 2004;
41:366-378.
67 Mochizuki S, Okada Y. ADAMs in cancer cell proliferation and progression. Cancer Sci 2007;
98:621-628.
68 Mulder N, Apweiler R. InterPro and InterProScan: tools for protein sequence classification and
comparison. Methods Mol Biol 2007; 396:59-70.
16969 Zdobnov EM, Apweiler R. InterProScan--an integration platform for the signature-recognition methods in
InterPro. Bioinformatics 2001; 17:847-848.
70 Hertervig E, Nilsson A, Nyberg L, Duan RD. Alkaline sphingomyelinase activity is decreased in human
colorectal carcinoma. Cancer 1997; 79:448-453.
71 Cheng Y, Wu J, Hertervig E et al. Identification of aberrant forms of alkaline sphingomyelinase (NPP7)
associated with human liver tumorigenesis. Br J Cancer 2007; 97:1441-1448.
72 Eichler EE, Zimmerman AW. A hot spot of genetic instability in autism. N Engl J Med 2008;
358:737-739.
73 Bochukova EG, Huang N, Keogh J et al. Large, rare chromosomal deletions associated with severe early-
onset obesity. Nature 2010; 463:666-670.
74 Myers RM, Stamatoyannopoulos J, Snyder M et al. A user's guide to the encyclopedia of DNA elements
(ENCODE). PLoS Biol 2011; 9:e1001046.
75 Petretti T, Kemmner W, Schulze B, Schlag PM. Altered mRNA expression of glycosyltransferases in
human colorectal carcinomas and liver metastases. Gut 2000; 46:359-366.
76 Parris TZ, Danielsson A, Nemes S et al. Clinical implications of gene dosage and gene expression
patterns in diploid breast carcinoma. Clin Cancer Res 2010; 16:3860-3874.
77 Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated
with breast cancer outcome. PLoS Comput Biol 2011; 7:e1002240.
78 Hua Y, Sahashi K, Rigo F et al. Peripheral SMN restoration is essential for long-term rescue of a severe
spinal muscular atrophy mouse model. Nature 2011; 478:123-126.
79 Oltean S, Sorg BS, Albrecht T et al. Alternative inclusion of fibroblast growth factor receptor 2 exon IIIc
in Dunning prostate tumors reveals unexpected epithelial mesenchymal plasticity. Proceedings of the
National Academy of Sciences of the United States of America 2006; 103:14116-14121.
80 Turner N, Grose R. Fibroblast growth factor signalling: from development to cancer. Nat Rev Cancer
2010; 10:116-129.
81 Huijts PE, van Dongen M, de Goeij MC et al. Allele-specific regulation of FGFR2 expression is cell type-
dependent and may increase breast cancer risk through a paracrine stimulus involving FGF10. Breast
cancer research : BCR 2011; 13:R72.
82 Somarelli JA, Schaeffer D, Bosma R et al. Fluorescence-based alternative splicing reporters for the study
of epithelial plasticity in vivo. Rna 2013; 19:116-127.
83 Lin R, Maeda S, Liu C, Karin M, Edgington TS. A large noncoding RNA is a marker for murine
hepatocellular carcinomas and a spectrum of human carcinomas. Oncogene 2007; 26:851-858.
84 Panzitt K, Tschernatsch MM, Guelly C et al. Characterization of HULC, a novel gene with striking up-
regulation in hepatocellular carcinoma, as noncoding RNA. Gastroenterology 2007; 132:330-342.
85 Gutschner T, Diederichs S. The hallmarks of cancer: a long non-coding RNA point of view. RNA Biol
2012; 9:703-719.
17086 Huang Q, Lin B, Liu H et al. RNA-Seq analyses generate comprehensive transcriptomic landscape and
reveal complex transcript patterns in hepatocellular carcinoma. PLoS One 2011; 6:e26168.
87 Toung JM, Morley M, Li M, Cheung VG. RNA-sequence analysis of human B-cells. Genome Res 2011;
21:991-998.