Correlation Between Genomic DNA Copy Number Alterations and Transcriptional Expression in Hepatitis B Virus-Associated Hepatocellular Carcinoma

View metadata, citation and similar papers at core.ac.uk brought to you by CORE

provided by Elsevier - Publisher Connector

FEBS Letters 580 (2006) 3571–3581

Correlation between genomic DNA copy number alterations and transcriptional expression in hepatitis B virus-associated hepatocellular carcinoma

Jian Huanga,c, Hai-Hui Shengb, Ting Shena, Yuan-Jie Hub, Hua-Sheng Xiaob, Qin Zhanga, Qing-Hua Zhangb, Ze-Guang Hana,* a Chinese National Human Genome Center at Shanghai, 351 Guo Shou-Jing Road, Shanghai 201203, China b National Engineering Center for Biochip at Shanghai, 151 Libin Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai 201203, China c Department of Chemistry of Fudan University, 220 Handan Road, Shanghai 200433, China

Received 13 March 2006; revised 4 May 2006; accepted 12 May 2006

Available online 22 May 2006

Edited by Takashi Gojobori

(HBV) and hepatitis C virus (HCV), chronic exposure to Afla- Abstract Human hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, in which the genetic mecha- toxin B1 (AFB1) and alcoholic cirrhosis, have been well iden- nisms of oncogenesis are still unclear. To investigate whether tified and characterized [1]. The previous studies indicated that the genomic DNA copy number alterations may contribute to the genetic abnormality of some genes, including potential primary HCC, the cDNA microarray-based comparative geno- oncogenes and tumor suppressor genes, could be involved in mic hybridization (CGH) analysis was here performed in 41 pri- hepatocarcinogenesis. However, the genetic alterations were mary HCC infected by hepatitis B virus and 12 HCC cell lines. only found in few patients with HCC, and whereas, the genetic The resulting data showed that, on average, 7.25% of genome- mechanisms of most HCC are still not completely elucidated. wide DNA copy numbers was significantly altered in those As we know, chromosomal instability, including gain or loss samples (4.61 ± 2.49% gained and 2.64 ± 1.78% lost). Gains of the region-specific genomic DNA copy number, is associ- involving 1q, 6p, 8q and 9p were frequently observed in these ated with tumor development and progression. These chromo- cases; and whilst, losses involving Ip, 16q and 19p occurred in most patients. To address the correlation between the alteration somal regions with DNA amplification often harbor of genomic DNA copy numbers and transcriptional expression, oncogenes, whereas tumor repressor genes are commonly the same cDNA microarray was further applied in 20 HCC spec- localized on certain regions with allelic deletion. To identify imens and all available cell lines to figure out the gene expression the chromosomal regions that may be crucial to hepatocarci- profiles of those samples. Interestingly, the genomic DNA copy nogenesis, a number of approaches had been employed to de- number alterations of most genes appeared not to be in generally tect the genomic alterations in HCC samples, including parallel with the corresponding transcriptional expression. How- cytogenetics, interphase fluorescence in situ hybridization ever, the transcriptional deregulation of a few genes, such as (FISH), Southern blot analysis, the conventional metaphase- osteopontin (SPP1), transgelin 2 (TAGLN2) and PEG10, could based comparative genomic hybridization (CGH) and geno- be ascribed partially to their genomic aberrations, although the typing analyses [2–4]. These data provided large numbers of many alternative mechanisms could be involved in the deregulation of these genes. In general, this work would provide new in- valuable information, in which DNA amplification on certain sights into the genetic mechanisms in hepatocarcinogenesis chromosomal regions involving 1q, 8q and 17q, as well as alle- associated with hepatitis B virus through the comprehensive sur- lic deletion on the chromosomes 1p, 4q, 5q, 6q, 8p, 9p, 13q, vey on correlation between genomic DNA copy number altera- 16p, 16q, and 17p were reported in more than 30% of HCC tions and transcriptional expression. samples [3]. Some known or candidate oncogenes and tumor 2006 Federation of European Biochemical Societies. Published suppressor genes on these regions, such as L-myc on 1p35- by Elsevier B.V. All rights reserved. 36, TERC on 3q26, APC on 5q21, SMAD5 on 5q31, MET on 7q31, c-MYC/PTK2/EIF3S3 on 8q23-q24, p16INK4A/ Keywords: Hepatocellular carcinoma; HBV; Comparative p19ARF on 9p21, PTEN on 10q23, IGF2/cyclin D1/FGF3/ genomic hybridization; Genomic DNA copy number; Gene FGF4/EMS1 on 11q13, SAS/CDK4 on 12q13-q14, Rb-1 on expression 13q14, Axin-1/SOCS-1 on 16p13.3, E-cadherin on 16q22.1 and p53 on 17p13 [2,5], could be considered to be involved in hepatocarcinogenesis. However, these approaches only pro- 1. Introduction vided the limited information on genomic DNA copy number changes in tumors due to the lower resolution, although the Hepatocellular carcinoma (HCC), a highly malignant tumor conventional metaphase-based CGH has been used for in the world, is one of the most frequent tumors in Asia, addressing the DNA copy number changes of HCC. In gen- including China and Japan, and sub-Saharan Africa, in which eral, the conventional CGH assay only detected the genomic the susceptible factors, such as the infection of hepatitis B virus regions with greater than 5–10 Mb for deletions and 2 Mb for amplification [6–8], but it was very difficult to identify *Corresponding author. Fax: +86 21 50800402. the genomic alterations within less than 1 Mb that may E-mail address: [email protected] (Z.-G. Han). contains numerous genes. To circumvent this limitation,

0014-5793/$32.00 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.febslet.2006.05.032 3572 J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 microarray-based CGH was recently devised and applied in genomic DNA copy number changes in 41 HCC specimens, detection of the DNA copy number aberrations in some dis- 6 adjacent non-cancerous livers, and 12 HCC cell lines by eases [9–12], in particular cancers [7,13]. The microarray-based the array-based CGH approach. Furthermore, to address the CGH has been proven to be a specific, sensitive, and fast tech- correlation between the genomic abnormality and transcrip- nique, with considerable advantages compared to other meth- tional expression of those genes within the aberrational regions ods used for the high-resolution detection of DNA copy in HCC, the gene expression profiles were performed on the number aberration. same 20 samples with the same cDNA microarray. Those data In addition, it is controversial for whether the genomic DNA would provide comprehensive resources for profound under- copy number aberrations may alter gene expression in some standing of the genetic mechanisms involved in hepatocarcino- tumors. The previous studies revealed that the DNA copy genesis. number alterations can lead directly to global deregulation of gene expression in breast tumors [14,15], pancreatic cancers [16,17], an immortalized prostate epithelial cell line [18], and 2. Materials and methods artificial chromosome trisomies [19], which could contribute to the development or progression of cancers. In contrast, only 2.1. Tissue specimens 4% of genes within these amplified regions were found to be Tumor samples were obtained from 41 patients with HCC (Table 1) highly expressed in metastatic colon tumors [20]. who underwent surgical resection of their diseases and were informed consent before operation on their livers. The primary tumors speci- To figure out the genome-wide pattern of DNA copy num- mens were immediately frozen at 80 C. Both paired tumor and adja- ber alterations with high-resolution in HCC, herein, the home- cent non-tumor tissues were sampled respectively, with approximate made cDNA microarray was first employed to detect the 1cm3 size of each specimen. All tumors and the adjacent noncancerous

Table 1 Clinicopathological information of 41 test samples No. Patients Gender Age HBV HCV AFP of Tumor Tumor Lymphatic Venous Cellular Pathological Clinical serum size (mm) number invasion invasion type staging staging (ng/ml) C01 03-10C Female 55 + 680 4 1 HCC II I C02 03-12C Male 25 + 1756 3.8 2 HCC II I C03 03-13C Male 58 + 2161 HCC II III C04 03-14C Female 71 + 400 11 1 HCC II II C05 03-15C Male 67 + 67 1 HCC II II C06 03-16C Male 48 + 3151 + HCC II II C07 03-17C Male 63 + 1834 3 1 HCC III I C08 03-18C Male 38 + 35 2.4 1 HCC II II C09 03-19C Male 48 + 5 2.7 1 HCC II I C10 03-21C Male 71 + 7101 HCC II I C11 03-22C Male 53 + 55 2 1 HCC II II C12 03-23C Male 39 + 1889 10 1 HCC III I C13 03-24C Male 45 + 347 2 2 HCC III I C14 03-25C Male 40 + 1885 5 1 HCC II II C15 03-29C Female 42 + 698 8.5 1 HCC III I C16 03-30C Female 39 + 2 5.8 1 HCC II II C17 ZS-21C Male 51 + 89 7 1 HCC II I C18 ZS-23c Male 52 + 421 2.8 1 HCC II I C19 ZS-24c Male 58 + 72 7.5 1 HCC II I C20 ZS-25C Male 37 + 11105 3 1 HCC II I C21 ZS-26c Male 42 + 1175 8.5 2 HCC II II C22 ZS-27c Male 40 + 10 11.5 1 HCC II I C23 ZS-30C Female 59 + 13660 3.5 3 + HCC III I C24 ZS-33c Male 72 + 320 7 1 HCC III I C25 ZS-34c Male 62 + 3535 11 1 HCC II I C26 ZS-35C Male 54 + 29875 10 1 HCC III II C27 ZS-36C Male 53 + 4 3.5 1 HCC II II C28 ZS-37C Male 71 + + 329 3.2 1 HCC II I C29 ZS-38c Male 59 + 157 1.6 1 HCC II I C30 ZS-39C Female 30 + 573 6 1 HCC II I C31 ZS-43c Male 35 + 26 6.5 1 HCC II I C32 50C Male 41 + 36.9 4.5 1 HCC II I C33 51C Female 47 + 1.61 4.5 1 HCC II I C34 52C Male 41 + 15.2 3.5 1 HCC II I C35 53C Male 54 + 1234 3 1 HCC II II C36 40C Female 67 + 35 8.5 1 HCC III I C37 41C Male 57 + 5 9.5 1 HCC III I C38 D06 Male 49 + 9 3.5 1 HCC II I C39 G09 Female 48 + 60 7 1 HCC III I C40 XUF08 Male 38 + 1100 11 1 HCC II II C41 E07C Male 45 + 300 10 1 HCC II II HCC, hepatocellular carcinoma. J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 3573 tissues were proved by pathological examination. Those HCC speci- as the reference labelled by Cy3. For 12 HCC cell lines, the equally mens in this presenting work were grouped as the differentiation grades mixed RNA obtained from these 12 HCC cell lines was used as refer- II–III according to the Edmondson grading system. ence, whereas RNA sample obtained from a given cell line was served as tested one. 2.2. Liver cancer cell lines Twelve liver tumor-derived cell lines (Bel-7402, PLC, MHCC-H, 2.7. Image acquisition and data analysis HepG2, QGY-7703, Hep3B, MHCC-L, YY-8103, Bel-7405, QGY- Hybridized array slides were scanned by confocal fluorescence 7701, SSMC-7721, and Focus) were used in this study. All of these cell microscopy as the manufacture’s instruction (Agilent Technologies). lines were grown under standard cell culture conditions in the follow- The fluorescence ratios were calculated after background subtraction ing media: minimum essential medium Eagle (Sigma, Dorset, UK) sup- (the median fluorescence signal of non-target pixels was served as plemented with 10% fetal bovine serum (Life Technologies), 1% L- background) using Agilent G2567AA Feature Extraction Software glutamine (L-glut) and 1% non-essential amino acids (NEAA) in a according to the manufacturer’s instructions. The test (Cy5)/reference 5% CO2-humidified chamber. (Cy3) ratio was determined automatically for each sample, and P values were assigned to each set of target spots. Gains and losses in DNA 2.3. Extraction of genomic DNA copy number of given genes were determined as Cy5/Cy3 ratios of the In this experiments, genomic DNA was extracted from all available corresponding spots with higher than 1.5 and lower than 0.75, respec- HCC specimens, HCC cell lines and five adjacent non-cancerous livers tively. For gene expression profiles, the upregulation and downregula- using the Dneasy Tissue kit (Qiagen, USA) according to the manufac- tion of expressed genes were considered as Cy5/Cy3 ratios of the corresponding spots with higher than 2.0 and lower than 0.5, respec- turer’s recommendation. Mixed genomic DNA of lymphocytes ob- tively. All ratios were filtered by P value or a probability for an indi- tained from healthy individuals (one male and one female) was used as diploid reference. vidual set of target spots as part of the normal distribution. Only those samples with P values of 0.01 or less were displayed in this present work. DNA copy number changes involving whole sex chromo- 2.4. Extraction of total RNA somes were not included in the analysis. All data are accessed and HCC specimens and the corresponding non-cancerous livers were available by our website http://202.127.18.238/CGH/. whetted to powder in the mortar with existing liquid nitrogen, and then poured into one centrifugal tube and homogenized in TRIzol solution 2.8. Real-time PCR (1 ml/50–100 mg). After these homogenates were incubated for 15 min at room temperature, chloroform (0.2 ml of per ml of TRIzol reagent) To evaluate the correlation between genomic aberrations and tran- was added to the homogenates and then vigorously agitated for 5 min. scriptional levels, real-time PCR were employed to detect the DNA copy numbers and transcripts of some genes as the previously described [25]. The inorganic phase was separated by centrifugation at 12000 · g for The primers and probes for osteopontin (SPP1) and transgelin 2 as well 20 min at 4 C. RNA was precipitated in the presence of 0.2 ml of chlo- b roform added as per ml of TRIZol reagent, and then centrifuged at as -actin as control were designed using Primer Express Sequence De- sign Software (Invitrogen, Shanghai, China). The primers and probes 10000 · g for 15 min at 4 C. RNA pellets were washed with 75% eth- are listed as following: SPP1 for genomic DNA: forward, 50- anol and then dissolved in diethyl pyrocarbonate (DEPC)-treated 0 0 H O. The selection of poly+(A) RNA from total RNA was performed TTGTGCCGTGATTCAGTACC-3 ; reverse, 5 -GGTTTCAGCACT- 2 CTGGTCAT-30; probe, 50-fam-AGGACCTGAACGCGCCTTCT- by using oligo (dT) according to the manufacturer’s recommendation GAT-tamra-30. SPP1 for transcript: forward, 50-AAGTTTCGC- (Qiagen, Chatsworth, CA). Total RNA concentration and quantity AGACCTGACAT-30; reverse, 50-GGTTTCAGCACTCTGGTCAT- were assessed by absorbency at 260 nm using a DNA/Protein Analyzer 0 0 0 (DU 530, Beckman, USA). 3 ; probe, 5 -fam-AGGACCTGAACGCGCCTTCTGAT-tamra-3 . Transgelin 2 for genomic DNA: forward, 50-CTGTCCAAGAAGG- GATTTA-30; reverse, 50-CCCATCTGTAACCCGATCAC-30; probe, 2.5. Microarray-based comparative genomic hybridization (CGH) 50-fam-TCCTCGGAACTTCTCGGATAACCA-tamra-30; Transgelin The homemade cDNA microarray used in this present work was 2 for transcript: forward, 50-CCAACTGGTTCCCTAAGAAATC-30; consisted of 13824 genes/ESTs, which these cDNA clones were derived reverse, 50-CCCATCTGTAACCCGATCAC-30; probe, 50-fam- from our previous HCC and liver cDNA library [21] and their PCR TCCTCGGAACTTCTCGGATAACCA-tamra-30. In this present products were printed on glass slides for microarray-based CGH as de- work, cDNA was synthesized in a 20 ll reaction system with 20 pmol oli- scribed earlier [10,22,23]. For each labelling, genomic DNA (2 lg) was go-dT as primer, in which 2 lg total RNA with DNase I treatment was first digested by DpnII and HaeIII (New England Biolabs) respectively, used as template. After incubated at 70 C for 5 min, the reaction was and then 1 lg of each DNA sample digested, respectively, by both en- mixed with 4 llof5· buffer, 2 ll of 0.1 M DTT, 2 ll dNTP (10 mM) zymes was equally mixed. The mixture (2 lg) was labelled using ran- and 1 ll (200 U) SurperScript II reverse transcriptase (Invitrogen, Carls- dom-primer (TaKaRa DNA Labelling Kit) in a 25 ll reaction, bad, CA) for further incubation at 42 C for 2 h. Real-time PCRs were including dATP, dGTP and dTTP of 120 lM each as well as dCTP, performed by TaqMan ABI 7700 (Applied Biosystems, Foster, Califor- Cy5-dCTP (for tested specimen) or Cy3-dCTP (for reference sample) nia) in 25 ll reaction system, which contains 1 ll Hotstar Taq DNA of 60 lM each, and then incubated at 37 C for 2 hours. For two-color polymerase (Qiagen Valencia, CA, USA), 200 nM forward and reverse array hybridization, the purified probes labelled by Cy5 and Cy3 primers each, and 1 ng of DNA or cDNA template, under thermal cy- respectively were equally mixed to in one new eppendorf tube, and cling conditions as follows: pre-denature at 94 C for 15 min; denature then human Cot-1 DNA (50 lg; Gibco BRL), yeast tRNA (50 lg; Gib- at 94 C, annealing at 55 C, and extend at 72 C for 40 s respectively. co BRL) and poly(dA-dT) (20 lg; Sigma) are added. After drying the mixture, the debris was diluted to solution containing 3.4· SSC and 0.3% SDS in a 40 ll final volume. Following denaturation (100 C for 1.5 min) and pre-anneal (37 C for 60 min), the probes were hybrid- 3. Results and discussion ized to the microarray under a glass coverslip at 42 C for 16–20 h, and then the array was washed in 0.1· SSC, 0.2% SDS (55 C for 10 min), followed by 1· SSC, 0.2% SDS at 55 C for 10 min, and finally washed 3.1. The chromosomal pattern of genomic DNA copy number in 0.1· SSC. The signal intensity of hybridization was measured by alterations in HBV-induced HCC confocal fluorescence microscopy (Agilent Technologies, Palo Alto, In this present work, a total of 41 HCC specimens infected CA). by HBV were analyzed through the homemade cDNA microarray-based CGH, which contains PCR products representing 2.6. Gene expression profile analysis the sequenced 13824 genes/ESTs clones derived from the The cDNA microarray analysis for gene expression was performed HCC, liver, bone marrow, cord blood and neuroendocrine as previously described [24]. Here, to figure out the differential display of gene expression between those paired of HCC and adjacent non-tu- [21,26,27]. Among 41 HCC specimens tested, the average num- mor tissues, the RNAs from HCC samples were used as test groups la- ber of target genes/ESTs with significantly genomic DNA copy belled by Cy5, whereas the corresponding non-tumor livers were served number changes in each sample was 2596 ± 1185 (±S.D.), of 3574

PEX10 PLA2G2A EIF4G3 C1QB 100% FUCA1 1p36 PNRC2 FUSIP1 SH3YL1 amplification NUDC TMEM18 WASF2 2p25 TPO 1p35 PPP1R8 DDEF2 SFRS4 PUM1 LCN7 HADHA ITPR1 1p34 CENPA CTBP1 100% PHC2 2p23 C2orf28 RAD18 4p16 ZNF258 FLJ22405 PPP2R2C 1p33 EIF2C1 KRTCAP3 3p25 deletion ADPRHL2 KIAA0007 ARPC4 TRAPPC3 CIDEC 1p32 NSEP1 LRPPRC SYN2 4p15 ANAPC4 UROD 2p21 FLJ23451 3p24 RAB5A PWDMP MKNK1 KCNK12 KIAA0342 RPL9 NRD1 2p16 MTIF2 4p14 HIP2 ZCCHC11 GOLGA4 RHOH 5p15 ZDHHC11 TGFBR3 PELI1 3p22 1p31 4p13 FLJ20273 LEPR KIAA0582 ENDOGL1 BPHL PLEK UCHL1 6p25 FLJ20331 SS18L2 PECI SFRS11 ANXA4 GABRA4 2p13 NKTR 4p12 5p14 TMEM14C LPHH1 ZNF638 CDH10 6p24 HIVEP1 CCT7 3p21 ZNF167 TBC1D7 1p22 ERBP 2p12 PLXNB1 6p23 FLJ11273 KIAA0217 AMY2B IGKC RANBP9 10p15 C10orf110 CELSR2 C2orf3 IHPK2 6p22 BZW2 AKR1C3 GMPR 7p22 FBXO25 RNH MAGI-3 SUCLG1 AMT TTRAP HDAC9 C9orf26 2p11 MAPKAPK3 TNKS 9p24 10p14 NUDT5 11p15 MUCDHL 1p21 HIPK1 LOC388966 4q12 POLR2B CMAH 8p23 XLKD1 HSD3B2 SPC12 TRA2A 10p13 MRC1 HIST1H2AC KIAA0644 9p23 PTPRD NUCB2 HIST1H2AL KIAA1967 1p13 CAST 7p15 FAPP2 8p22 2q11 ZNF2 3p14 6p21 TRIM26 9p22 MLLT3 FLJ21308 GNL1 CARD4 PTK2B RBM8A 5q11 PPAP2A ABCF1 MGC29816 3p13 FLJ10213 C7orf25 C9orf55 KIAA0493 2q12 IL1RL1 HSA9761 FLOT1 7p14 BLVRA 8p21 PPP2R2A CXCL2 9p21 ADFP CRA 5q12 HLA-C OGDH XPO7 ANP32E SCARB2 CCNB1 MICA 8p12 MERTK 4q21 CDS1 6p12 AIF1 GRHPR FLJ20519 ACTR3 RAD17 APOM AF1Q PTPN13 9p21 SHB 1q12 2q14 AK026464 SPP1 5q13 SMA5 HSPA1A 7p22 CCT6A 8p11 STAR API5 PIP5K1A FLJ10006 BHMT2 C2 CHCHD2 ITPR3 VKORC1L1 9p21 11p11 MADD S100A6 WDR33 NM_020221 DHFR KIAA1539 MYBPC3 SNAPAP 6q12 C6orf129 ASL NM_016323 ZFP318 NDUFS3 SHC1 MCM6 4q22 SDHC 2q21 SNCA 6q13 TRAM2 LOC115294 PHYHIPL 1q21 CXCR4 PARC 8q11 CHCHD7 10q11 HDGF 3q13.1 GAP43 POLR1C CALN1 IMPA3 PP UBE2L6 CD1B 4q24 MGC16169 6q14 ENPP4 7q11 PMS2L2 MS4A8B 1q22 MNDA GSTA4 RHBDL7 11q12 GPR44 TAGLN2 2q23 ARL5 COL21A1 FEN1 3q13.3 FSTL1 B3GAT3 COPA PHF3 HGF 1q23 DEDD HGD 5q21 KIAA0433 C6orf209 PEG10 DDR2 MYLK IBTK PPP1R9A VEGFB RGS5 1q23 3q21 ZNF148 ME1 ESRRA 7q21 PDK4 GNAQ H2AFY2 11q13 1q24 CREG TRH SLC25A13 9q21 TLE4 10q22 PPP2R5B MGC9084 RAB33B FOXO3A PILRB COL15A1 C10orf42 DPF2 TLK1 3q22 H41 SERPINE1 BBS1 VAMP4 AL137759 ZNF608 6q21 ZBTB24 PIK3CG 4q28 REV3L AIP PIGC 2q31 PDK1 FGB 5q23 SLC12A2 1q25 RGS16 3q23 ATP1B3 GPX3 ACTA2 GSTP1 8q21 PLEKHF2 CCRK CTTN RGL PDE1A ITGA2 KIAA0408 RBP4 HRPT2 ENPP1 CA2 HBLD2 C10orf4 NUP35 DPYS 9q21 BICD2 10q23 1q31 NEK7 2q32 SMARCA3 6q22 TBPL1 8q21 XPA ITGAV AADAC C5orf15 MATN2 TBC1D12 PTPRC OSGEPL1 3q25 DKFZp434L142 5q31 PEX3 TP53INP1 SEC61B SCD KIAA1374 4q32 FLJ20195 PGCP AL136611 B3GALT3 KIAA0992 TCERG1 POLR2K 7q31 ANKRD7 BTRC 1q32 IP09 9q31 C9orf5 2q33 ORC2L CGI-07 10q24 ADRA2A NDUFS1 3q26 8q23 CML66 FLJ10326 EEF1B2 XPNPEP1 1q41 CSF1R 7q32 TIF1 GALNT2 2q34 10q25 PNLIPRP1 5q33 TNIP1 TNFRSF11B MKI67 DEGS CRSP9 8q24 TRPS1 9q33 PBX3 1q42 TARBP1 SP100 LOC51149 ESR1 WISP1 ATRNL1 KIAA0117 3q27 EIF2B5 CACNA1B EIF3S10 2q36 INPP5D POLR2H MLLT4 NCBP1 PHPT1 10q26 GALNAC4S MTR DTYMK 3q28 FLJ35155 6q25 8q24 9q34 2q37 TBP RARRES2 CYC1 SSNA1 ZNF511 1q43 CHML 3q29 CENTB2 4q35 ARGBP2 7q36 LY6E PNAS-4 DKFZp434B227 6q27 CDK5 8q24 1 2 3 4 5 6 7 8 9 10 11

ADIPOR2 CCND2 ING4 CD9 TAPBPL 12p13 CHD4 TPI1 PAFAH1B1 C2F UBE2G1 RBP5 CXCL16 12p12 M6PR KIF1C HEBP1 NLGN2 LRMP ZBTB4 ITPR2 RPL26 12p11 FGFR1OP2 MDS006 SCML1 FSTL3 Xp22.1 FLJ11088 FLII SAT CGI-04 LOC220594 CSNK1G2 KIF21A MGC13114 SPPL2B 3571–3581 (2006) 580 Letters FEBS / al. et Huang J. WASF3 OMG APBA3 12q12 COL2A1 ANG MGC15416 FLT1 FLJ20859 MRPS34 ZNF207 CLPP ARF3 13q12 RAD51L3 NR4A1 C13orf22 ABHD4 NDUFB10 TRIP10 PEX12 VAV1 MAP3K12 14q11 PSMB5 RNPS1 PPP1R1A PABPN1 PDPK1 CNTNAP1 XAB2 MKRN3 HSPC009 KEAP1 12q13 MLC1SA 14q12 PSME1 15q11 HCFC1R1 17p13 EMILIN2 STAT6 13q13 ALG5 PSME2 IPW 16p13 CREBBP PSME3 19p13.3 CARM1 LOC56901 C14orf124 15q12 HSCARG G6PC 18p11 GNAL MGC3262 DHX8 DNAJB1 CD63 14q13 CFL2 SDOS SHMT2 RFXAP CLST11240 MGC10471 15q13 C15orf29 SNN HIS1 12q14 AVIL TNFSF11 PSMA6 16p12 ZC3HDC7 17p11 19p13.2 PRKACA C20orf96 PPM1H GOSR2 IFI30 13q14 HTR2A MEIS2 16p13 ABCC1 NPEPPS 20p13 C20orf18 12q15 RAP1B ITM2B POLR3E FLJ20850 PSMF1 NSBP1 OSBPL8 15q14 NUSAP1 MRPL10 Xq13 CGI-119 CDADC1 16p11 USP31 HOXB5 MGC13096 20p13 CDC25B RCBTB1 KIAA0252 TNRC6 19p13.1 ZNF302 20p12 MDM1 15q15 RPAP1 ABI3 NPC1 RASSF2 PHLDA1 KPNA3 DLG7 ARHGAP17 17q11 MYST2 18q11 CAPNS1 20p13 C20orf30 NEK3 14q22 CAPN3 CYP2A6 TBC1D15 TUFM SGCA 12q21 KIAA0377 HIRIP3 17q12 CYP2B7 UBE2N 14q22 SERF2 NME2 ATP5A1 20p11 KIAA0980 C14orf100 CYLD FLJ10241 NTN4 14q23 15q21 B2M HLF CEACAM6 TXNRD1 FTS SUPT4H1 ATP6V1E1 12q22 SLC27A2 MMP2 17q21 18q12 MAPK4 CEACAM1 HM13 FLJ12969 CKAP4 FLJ10980 RAD51C ZDHHC8 Xq22 SFRS5 KIAA1007 CLTC APOC1 TM9SF4 COL4A5 PRDM4 SYNJ2BP PIGB MGC4655 ZNF541 22q11 IGLJ3 UNG 13q22 C13orf24 16q12 RPS6KB1 NCOA6 12q23 ABCD4 15q22 FLJ13213 16q13 ALDOA DHRS10 EIF4ENIF1 Xq23 CDV-1 14q24 PPIB RCD-8 APPBP2 20q11 CPNE1 UBE2A OAS1 KIAA0317 17q22 BCAS3 NUCB1 EIF2B2 CALML4 16q2116q12 PSMB10 PIGN 19q13.1 RUVBL2 SDC4 Xq24 CUL4B RASAL1 16q13 SLC12A4 FLJ10215 15q23 HEXA MAP3K3 SNRP70 20q12 WFDC2 12q24 TSC SNTB2 17q23 18q21 ZNRF3 WSB2 AHSA1 NEO1 SMARCD2 FLJ20643 DDX27 C14orf142 CYP11A1 NFAT5 FLJ20793 19q13.2 LOC284361 22q12 NIPSNAP1 MGC4767 16q22 AARS SMURF2 20q12.1 ZNF313 PSMD9 MEG3 MPI 17q24 CDC42EP4 SCGF PPP2R5C 15q25 COX5A TAT PPP2R1A C20orf108 RPL3 DENR PSMD7 AD023 19q13.3 CDK2AP1 MARK3 MDS018 GRB2 ZNF83 PPP4R1L PACSIN2 14q32 FA2H TRMT1 ABCD1 RNP24 AKT1 ETFA 16q23 KARS RECQL5 MBP ZNF415 RAB22A 22q13 FLJ22471 13q33 LIG4 IGHM 15q26 KIAA1199 NOC4 17q25 H3F3B RPS9 C20orf45 ALG12 ATP6AP1 19q13.4 20q12.3 MAPK12 F8 UBC 13q34 MCF2L NEUGRIN CA5A ACOX1 IL11 DATF1 Xq28 CHD2 16q24 PCOLN3 NARF 18q23 C18orf22 ZNF134 C20orf14 PLXNB2 12 13 14 15 16 17 18 19 20 22 X Fig. 1. The chromosomal pattern of genome-wide DNA copy number alterations in HCC specimens. The pattern was here described based on 342 genes/ESTs with allelic ampliﬁcation and 360 with allelic deletion occurred in more than 30% of 41 patients with HCC. Red and green bars represent the allelic ampliﬁcation and deletion of given genes/ESTs, respectively; and whilst, the length of bars indicated the percentage of the HCC specimens with the gain or loss of a given gene/EST, where the scale was showed in upper right. J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 3575

35 30 25 * ** 20 ** 15 * 10 ** 5 0 1p 1q 2p 2q 3p 3q 4p 4q 5p 5q 6p 6q 7p 7q 8p 8q 9p 9q 10p 10q 11p 11q 12p 12q 13p 13q 14p 14q 15p 15q 16p 16q 17p 17q 18p 18q 19p 19q 20p 20q 21p 21q 22p 22q Xp Xq Yp Yq hh 0 5 10 15 DNA copy number alterations DNA * * 20 Numbers of genes/ESTs with genomic 25 * 30 35 ** Chromosomes

Fig. 2. Histogram for the number of genes/ESTs with genomic DNA copy number alterations on different chromosomal arms. The upper red bars represent the number of genes/ESTs on certain chromosomal arms, which were based on 342 genes/ESTs with allelic amplification in 41 HCC specimens; whereas the below green bars indicated the number of genes/ESTs on certain chromosomal arms, based on 360 genes/ESTs with allelic deletion in the same specimens. **P < 0.01; *P < 0.05 (v2 test). which gains were 1318 ± 796 and losses were 1278 ± 631 array-CGH. The resulting data indicated that the DNA copy according to the stringent criteria with Cy5/Cy3 ratios of high- number changes of the noncancerous livers are distinct from er than 1.5 for gains and lower than 0.75 for losses. Further- that of the corresponding HCC samples (correlation coeffi- more, 342 genes/ESTs with allelic amplification and 360 with cient R2 < 0.01) (Fig. 3), implying that the ‘‘normal’’ poly- allelic deletion occurred in more than 30% of those 41 HCC morphisms in individuals could not be associated the specimens (Fig. 1). The distribution of different chromosomal genomic DNA copy number alterations in HCC. In addition, loci with genomic DNA aberrations, represented by those it is known that about 20% of adult liver cells are polyploidy. genes/ESTs, indicated that the gains of genomic DNA copy To further evaluate the effect of ‘‘normal’’ polyploidy on the numbers were frequently observed on some certain chromo- CGH results from HCC specimens, two adult livers from pa- somal arms, such as 1q, 6p, 8q, 9p, and 10q, whereas the losses tients with haemangioma were also analyzed by the same ar- of alleles, monoallele or bi-alleles, were often found on chro- ray CGH. The resulting data reflected that the genomic mosomes 1p, 16q, 17q and 19p (Figs. 1 and 2). In general, pattern of ‘‘normal’’ polyploidy in those two individuals the resulting data was consistent with the conventional meta- could not be correlated with that of all HCC specimens (data phase-based CGH analyses on HCC specimens [28–30]. not shown). These data suggested that HBV-associated The microarray-based CGH can disclose the genomic aber- hepatocarcinogenesis could be triggered by unique genetic rations in a higher resolution than the conventional CGH, in mechanisms that need to be further clarified. which more genomic abnormalities were clustered onto certain chromosomal subdomains. For example, genomic DNA copy 3.2. Relationship between genomic DNA copy number changes numbers on chromosomes 1q22-23, 1q25, 1q43, 2p13, 2p23, and clinicopathologic features 2q11-21, 4q21, 4q28, 5q11-13, 6p21-25, 6q13, 8q11, 8q21, To investigate the correlation between the genomic DNA 8q24, 9p21-24, 10q25-26,11p15, 12p15, 13q14, 14q11, 15q22, copy number aberrations and clinicopathologic features, sta- 17q25 and 22q12-13 appeared to be frequently amplified in tistical analysis was performed on the available clinical infor- these HCC specimens infected by HBV, whereas those mono- mation, including age, gender, clinical stages, pathological allele or bi-alleles localized on chromosomes lp13-22, 1p32, and laboratory examinations in these 41 patients with HCCs. 1p35-36, 2q33, 3q22-25, 4q32-35, 5q33, 8p11-21, 9q21, However, the resulting data showed that no clinicopatho- 10q11, 11q13, 12p13, 12q22, 14q24, 14q32, 15q11, 15q26, logic features were significantly correlated with certain 16p11-12, 16q22, 16q23, 17p11, 17p13, 18q11-12, 19p13.2- chromosomal loci with genomic DNA copy number altera- 13.3, 19q13.2 and 19q13.3, could be in common deleted during tions. the hepatocarcinogenesis. Although some genomic DNA aberrations were difficult to be fine mapped onto chromosomal 3.3. Correlation between genomic DNA copy number changes subdomains in high-resolution manner due to the limited gene and transcriptional levels clones and/or possibly large-scale genomic abnormalities, these To evaluate the relationship between genomic DNA copy data would provide valuable information for identifying the number aberrations and transcriptional expression levels, we potential oncogenes and tumor suppressor genes associated further employed the same cDNA microarray to figure out with HCC. the gene expression profiles of 20 HCC specimens and avail- Recently, some evidences revealed that there are genomic able HCC cell lines, and then compared the gene expression DNA copy number polymorphisms in different individual hu- profiles with the corresponding genomic DNA copy number man genome [31]. To evaluate whether the DNA copy num- changes (Fig. 4). Herein, the cDNA microarray-based CGH ber polymorphisms of individuals could be correlated with exhibit the unique merit that can simultaneously address both the genomic alteration of those HCC specimens, six pairs genomic DNA copy number changes and transcriptional of HCC and the adjacent noncancerous livers were here em- expression, but BAC array-based CGH assay is difficult to de- ployed to test their DNA copy number changes by the same tect the gene expression although it may provides accurate 3576 J. Huang et al. / FEBS Letters 580 (2006) 3571–3581

high-resolution chromosomal mapping. Interestingly, the genomic DNA copy number aberrations appeared not to be C17 N17 C18 N18 C21 N21 C22 N22 C29 N29 C51 N51 parallel with the corresponding gene expression profiles in any given samples, whatever in HCC specimens or cell lines, implying that the genomic DNA copy number alterations, amplification and loss, could not be crucial to the transcriptional expression of most genes in HCC. In other words, the transcriptional levels of these genes were regulated by many molecular mechanisms, including genetic and epigenetic factors and networks, although the previous study showed that 62% of highly amplified genes demonstrated moderately or highly elevated expression [14]. However, a few genes with genomic DNA copy number aberrations indeed exhibit the dysregulation of gene expression, implying that the deregulated genes could be partially rel- ative to the DNA copy number changes. Among them, the transcriptional levels of few known important genes with genomic DNA copy number alterations, gains or losses, was deregulated at transcriptional levels in HCC specimens (Table 2). For example, the genomic DNA copy numbers of both secreted phosphoprotein 1 (osteopontin) (SPP1) and paternally R2= 0.001 0.003 0.007 0.002 0.006 0.0038 expressed 10 (PEG 10) were significantly amplified, partially Fig. 3. Comparison of array-based CGH data between HCC (C) and in consistency with the upregulated transcripts in HCC non-cancerous livers (N). To evaluate correlation between the DNA (Fig. 5), where these two genes were known to be highly ex- copy number polymorphisms of individuals and the genomic alteration of those corresponding HCC specimens, six pairs of HCC and the pressed in hepatocarcinogenesis [32,33]. Similarly, transgelin adjacent noncancerous livers were here employed to test their DNA 2 (TAGLN2), H2A histone family, member L (HIST1H2AC), copy number changes by the same array-CGH. The correlation importin 9 (IPO9), and attractin-like 1 (ATRNL1) also exhib- 2 coefficients (R ) (below) were calculated by the comparison of the ited the increasing genomic DNA copy numbers and upregu- genomic DNA copy number alterations in tumors (left) and the lated transcripts in HCC, implying that the genes could be corresponding non-tumor livers (right). involved in hepatocarcinogenesis. In contrast, more genes, 1 A B ocus Bel-7402 MHCC-H HepG2 QGY-7703 Hep3B MHCC-L YY-8103 Bel-7405 QGY-7701 SSMC-772 F 03-10C03-13C 03-15C 03- 23C 03-25C 03-24C03-29C 21C 26C 27C 37C 39C PLC

R2= 0.027 0.044 -0.034 0.023 -0.011 -0.004 0.009 0.002 0.038 -0.003 -0.005 -0.034 R2= 0.047 0.032 0.198 -0.017 -0.008 0.021 0.003 -0.029 -0.029 0.007 0.047 -0.040

Fig. 4. Comparison of array-based CGH data and gene expression profiles in the same HCC specimens and cell lines. Gene clustering were performed on those paired data of array-based CGH data with genomic DNA copy number, and gene expression profiles in the same 20 HCC specimens (A) and 12 HCC cell lines (B). The correlation coefficients (R2) were calculated by the comparison of the genomic DNA copy number alterations (left) and the corresponding transcriptional levels (right) of those genes/ESTs in the same samples. J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 3577

Table 2 Some genes with genomic DNA copy number alterations and deregulated expression Gene Name Description UniGene Chromosomes DNA RNA Number Status Number Status (%)a (%)b SPP1 secreted phosphoprotein 1 (osteopontin) Hs.313 4q21-q25 18 (43.9) gain 15 (75.0) UP TAGLN2 transgelin 2 Hs.75725 1q21-q25 25 (61.0) gain 12 (60.0) UP PEG10 paternally expressed 10 Hs.137476 7q21 16 (39.5) gain 9 (45.0) UP PPIB peptidylprolyl isomerase B (cyclophilin B) Hs.699 15q21-q22 28 (68.3) gain 8 (40.0) UP AKR1C3 aldo-keto reductase family 1, member C3 Hs.78183 10p15 30 (73.2) gain 7 (35.0) UP H2AFL H2A histone family, member L Hs.28777 6p21.3 22 (53.7) gain 6 (30.0) UP TBPL1 TBP-like 1 Hs.13993 6q22.1-q22.3 16 (39.0) loss 16 (80.0) DOWN NTN4 netrin 4 Hs.102541 12q22-q23 27 (65.9) loss 13 (65.0) DOWN ZNF313 zinc ﬁnger protein 313 Hs.10590 20q13.13 29 (70.7) loss 13 (65.0) DOWN LRMP lymphoid-restricted membrane protein Hs.40202 12p12.3 24 (58.5) loss 13 (65.0) DOWN MRC1 mannose receptor, C type 1 Hs.75182 10p13 30 (73.2) loss 12 (60.0) DOWN NR4A1 nuclear receptor subfamily 4, group A, member 1 Hs.1119 12q13 16 (39.0) loss 12 (60.0) DOWN AADAC arylacetamide deacetylase (esterase) Hs.587 3q21.3-q25.2 20 (48.8) loss 11 (55.0) DOWN TGFBR3 transforming growth factor, beta receptor III Hs.342874 1p33-p32 22 (53.7) loss 10 (50.0) DOWN SDC4 syndecan 4 (amphiglycan, ryudocan) Hs.252189 20q12 28 (68.3) loss 10 (50.0) DOWN SLC27A2 solute carrier family 27 (fatty acid transporter), member 2 Hs.11729 15q21.2 15 (36.6) loss 10 (50.0) DOWN PTPRC protein tyrosine phosphatase, receptor type, C Hs.170121 1q31-q32 17 (41.5) loss 9 (45.0) DOWN SORL1 sortilin-related receptor Hs.278571 11q23.2-q24.2 14 (34.1) loss 9 (45.0) DOWN ITPR2 inositol 1,4,5-triphosphate receptor, type 2 Hs.238272 12p11 14 (34.1) loss 9 (45.0) DOWN ESRRA estrogen-related receptor alpha Hs.110849 11q13 14 (34.1) loss 9 (45.0) DOWN GAP43 growth associated protein 43 Hs.79000 3q13.1-q13.2 15 (36.6) loss 8 (40.0) DOWN HSCARG HSCARG protein Hs.288969 16p13.3 16 (39.0) loss 7 (35.0) DOWN GPX3 glutathione peroxidase 3 (plasma) Hs.336920 5q23 27 (65.9) loss 7 (35.0) DOWN aIndicated the numbers of patients with the genomic DNA copy number alterations of a given gene through the array-based CGH analysis on 41 HCC specimens. bIndicated the numbers of patients with deregulated expression of a given gene through the same microarray analysis on 20 HCC specimens.

including transforming growth factor, beta receptor III 20) HCC specimens (Table 2), where both DNA amplification (TGFBR3), lymphoid-restricted membrane protein (LRMP), and upregulation of transcript were simultaneously found in 6 mannose receptor, C type 1 (MRC1), estrogen-related receptor cases (Fig. 5A), implying that the genomic DNA amplification alpha (ESRRA), MLL, carcinoembryonic antigen-related cell of SPP1 could partially contribute to the upregulated tran- adhesion molecule 6 (CEACAM6), cellular repressor of script although the multiple molecular mechanisms also could E1A-stimulated genes (CREG), growth associated protein be involved in the process. 43, vitamin D-binding protein (GC), sortilin-related receptor To independently evaluate the correlation between the (SORL1), and MEG3, showed the decreasing DNA copy num- amplification and upregulation of SSP1, real-time PCR was bers and downregulated transcriptional levels in certain HCC carried out to determine genomic DNA copy number and tran- specimens, suggesting that the genetic abnormalities of these scriptional levels in 38 pairs of HCC specimens and corre- genes could partially contribute to hepatocarcinogenesis. sponding non-HCC livers, where b-actin was used as a Interestingly, some genes with unknown functions also exhibit reference. The resulting data showed that the genomic DNA similar features, which should be further investigated on the ef- copy number of the gene was amplified in 55.3% (21/38) fect on oncogenesis of HCC. HCC specimens (Fig. 5B), which are generally parallel with the data from microarray-based CGH although real-time 3.4. DNA amplification of osteopontin (SPP1) on chromosome PCR seem to be more sensitive, implying that the micro- 4q21 could partially contribute to the upregulated transcript array-based CGH data is reliable and valuable for profound Loss of heterozygosis (LOH) involving chromosome 4q was understanding of genetic mechanisms involved in HCC. More- frequently observed in HCC and various cancers, such as over, the transcriptional upregulation of the gene was found in breast cancer [34], oral squamous cell carcinomas [35] prostate 76.3% (29/38) HCC specimens, including two samples with cancer [36] and gastric carcinomas [37]. However, the amplifi- invasion (03_16 C and ZS_30 C) (Fig. 5B), where both geno- cation of DNA copy number of some loci on chromosome mic DNA amplification and transcriptional upregulation were 4q21 and 4q28 was here found (Fig. 1), which the chromo- simultaneously observed in 39% (15/38) HCC specimens, somal gain on 4q was first reported. Interestingly, osteopontin implying that the transcriptional upregulation of the gene (SPP1), which is a member of the SIBLING family that plays could only be partially ascribed to the DNA amplification be- an important role in tumor development, invasion and metas- cause there are complex genetic and epigenetic mechanisms tasis through integrating signaling events [38,39], was located were involved in the hepatocarcinogenesis. For example, previ- on chromosome 4q21. It is known that SPP1 was significantly ous studies showed that transcript of SSP1 is regulated by upregulated in HCC with metastasis [32]. Herein, the amplifi- numerous transcription factors, including RRF, Sp1, Myc cation of genomic DNA copy number of the gene was detected and Oct-1 [40]. As the previous studies suggested, SPP1 could in 43.9% (18/41) patients with HCC through array-based be a potential molecular diagnostic marker and become opti- CGH, and whilst the transcript was upregulated in 75% (15/ mal molecular targets for novel therapies for various cancers 3578 J. Huang et al. / FEBS Letters 580 (2006) 3571–3581

[32]. Herein, HCC specimens with genomic DNA amplification DNA amplifications (Fig. 5C), although genomic DNA and overexpression of SPP1 indeed exhibited different patho- amplification of CD1b, MNDA and SDHC localized on logical stages and tumor sizes. However, whether the genomic the same chromosomal region was also observed in some DNA amplification of SPP1 is an earlier event during hepato- HCC specimens without the significantly upregulated tran- carcinogenesis or is involved in tumor progression should be scripts of these genes. It is known that TAGLN2 was in- further investigated. volved in cell proliferation and migration [41]. To further evaluate the genetic aberration and transcriptional level of 3.5. TAGLN2 involving chromosome 1q21 in HCC TAGLN2, the real-time PCR was also employed to detect As known, chromosome 1q gain is also one of the most the genomic DNA copy number and transcriptional level. common chromosomal aberrations in HCC, although the The resulting data showed that DNA amplifications of accurate genetic abnormalities during hepatocarcinogenesis TAGLN2 occurred in 36.1% (13/36) HCC samples, and are still unclear. In the present study, the high incidences whilst the overexpression of transcript was found in 63.9% of gains at multiple loci on chromosome 1q, including (23/36) HCC. However, both DNA amplification and over- chromosome 1q21-23, 1q24-25, 1q32 and 1q43, were ob- expression of the gene were only found in 28% (10/36) served in these HCC specimens (Fig. 1). Compared with HCC specimens (Fig. 5D), implying that the overexpression gene expression profiles, we paid attention on the upregu- of TAGLN2 could only be partially ascribed to the DNA lated TAGLN2 involving chromosome 1q21 with genomic amplification because there might be multiple mechanisms

A C01 C18 C05 C35 C09 C06 C21 C28 C27 C41 C11 C23 C30 C02 C03 C14 C13 C32 C26 C38 C36 C37 C17 C31 C29 C03 C07 C09 C05 C22 C14 C17 C16 C13 C15 C11 C25 C04 C24 C51 C16 C19 C08 C39 C10 C12 C07 C20 C34 C22 C33 C40 C01 C12 C28 C08 C02 C10 C23 C30 C21 SPARCL1 DMP1 SPP1 4q21 PKD2 SNCA EIF4E ADH5

0 0.5 1 2.5 5

7 B 6 5 4 3 SPP1 DNA 2 1 SPP1 RNA 0 -1 -2 -3 -4

Ratio values (log2) values Ratio -5 -6 -7 1234567891011121314151617181920212223242526272829303132333435363738 Specimens

C C13 C15 C07 C08 C03 C12 C10 C14 C17 C21 C22 C30 C35 C01 C28 C05 C02 C09 C23 C16 C11 C16 C27 C10 C20 C15 C12 C39 C13 C09 C18 C22 C40 C19 C41 C38 C26 C23 C24 C29 C21 C36 C08 C17 C25 C04 C11 C05 C32 C06 C30 C31 C33 C02 C34 C14 C28 C07 C03 C01 C37 CD1B MNDA 1q21 TAGLN2 SDHC DDR2 RGS5 CREG

0 0.5 1 2.5 5

Fig. 5. Genomic DNA ampliﬁcation and overexpression of SPP1 TAGLN2 and PEG10 in HCC specimens. (A) Both genomic DNA copy number (left) and the gene expression (right) of some genes localized on chromosome 4q21 were showed, where SPP1 was highlighted due to the genomic DNA ampliﬁcation and overexpression in many HCC specimens. Both microarray data were here clustered and showed as the scale bar. (B) Real- time PCR were performed on 38 pairs of HCC and adjacent non-cancerous livers to quantitatively evaluate the correlation between genomic DNA copy number and transcript level of SPP1. The logarithmic ratios were calculated according to the ratios of genomic DNA copy number or transcript level in HCC specimens to that in the corresponding non-cancerous livers. Similar clustering and real-time PCR were also performed on TAGLN2 localized on chromosome 1q21 (C and D). (E) Similar gene clustering for PEG10 localized on chromosome 7q21 was carried out in the same microarray data; and (F) showed the RT-PCR products of PEG10 in 30 pairs of HCC (C) and the corresponding adjacent non-cancerous livers (N), where b-actin was used as reference. J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 3579

D 4 3 2 TAGLN2 DNA 1 TAGLN2 RNA 0 -1 -2

Ratio values (log2) values Ratio -3 -4

-5 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Specimens

E C01 C09 C28 C18 C12 C03 C15 C02 C34 C19 C24 C08 C04 C11 C16 C25 C41 C06 C33 C38 C29 C23 C32 C31 C07 C39 C26 C40 C13 C20 C21 C27 C05 C22 C14 C10 C17 C30 C36 C37 C35 C01 C02 C09 C17 C03 C16 C05 C23 C12 C07 C10 C14 C30 C21 C11 C0a C22 C28 C15 C13 HGF 7q21 PEG10 PDK4 SLC25A13

0 0.5 1 2.5 5

N C N C N C N C N C N C N C N C N C N C PEG10 B-actin

PEG10 B-actin

Fig. 5 (continued)

involved in the upregulation of TAGLN2. In addition, some HCC specimens (Fig. 5F), implying that the overexpression of reports indicated that TAGLN2 was also overexpressed in the gene could be an important event in oncogenesis of HCC, gastric cancer [34] and HCC [42], where TAGLN2 was even which also need to be further investigated. considered as a diagnostic marker for HCC. However, very In conclusion, this present work employed cDNA micro- little is known about the actual effect of TAGLN2 on array-based CGH assay to figure out chromosomal patterns hepatocarcinogenesis and tumor progress. Whether chromo- with the genomic DNA copy number aberrations in a high- some 1q21-25 amplificon involving TAGLN2 as an impor- resolution through examining 41 HCC specimens. Moreover, tant event could contribute to HCC should be further the comparison of genomic DNA copy number alterations clarified. and the transcriptional expression in the same 20 HCC speci- Furthermore, PEG 10 localized on chromosome 7q21 was mens and 12 HCC cell lines provided new insights into hepato- evaluated in HCC specimens (Fig. 5E and F). The amplifica- carcinogenesis and the feasibility to identify potential novel tion of genomic DNA copy number of the gene was detected oncogenes or tumor suppressor genes associated with HCC in 39.0% (16/41) HCC specimens through array-based CGH, through integrating array-based CGH data with gene expres- and whilst the transcript was upregulated in 45% (9/20) HCC sion profiles. In addition, we also address the genomic aberra- specimens (Table 2), where both DNA amplification and tions and deregulatory transcripts of SPP1, TAGLN2 and upregulation of transcript were simultaneously found in 6 PEG10 as examples that could contribute to hepatocarcino- cases (Fig. 5E), implying that the genomic DNA amplification genesis and tumor progress. of SPP1 could partially contribute to the upregulated transcript although there might be multiple molecular mechanisms Conflict of interest statement in the upregulation. RT-PCR experiments also confirmed that the upregulation of PEG 10 was found in another 67% (20/30) None declared. 3580 J. Huang et al. / FEBS Letters 580 (2006) 3571–3581

Acknowledgements: This work was supported by National Natural Sci- [18] Phillips, J.L., Hayward, S.W., Wang, Y., Vasselli, J., Pavlovich, ence Foundation for Outstanding Youth (30425019), Chinese National C. and Padilla-Nash, H., et al. (2001) The consequences of Key Program on Basic Research (973), Chinese High-Tech Research chromosomal aneuploidy on gene expression profiles in a cell line and Development Program (863), National Foundation for Excellence model for prostate carcinogenesis. Cancer Res. 61, 8143–8149. Doctoral Project, and Shanghai Commission for Science and Technol- [19] Upender, M.B., Habermann, J.K., McShane, L.M., Korn, E.L., ogy (04XD14014 and 03DZ14024). The authors thank Dr. Yin-Kun Barrett, J.C. and Difilippantonio, M.J., et al. (2004) Chromo- Liu at Cancer Institute affiliated to Zhongshan Hospital, Fudan some transfer induced aneuploidy results in complex dysregula- University for kindly providing MHCC-H and MHCC-L cell lines. tion of the cellular transcriptome in immortalized and cancer cells. Cancer Res. 64, 6941–6949. [20] Platzer, P., Upender, M.B., Wilson, K., Willis, J., Lutterbaugh, J. References and Nosrati, A., et al. (2002) Silence of chromosomal amplifications in colon cancer. Cancer Res. 62, 1134–1138. [1] Llovet, J.M., Burroughs, A. and Bruix, J. (2003) Hepatocellular [21] Xu, X.R., Huang, J., Xu, Z.G., Qian, B.Z., Zhu, Z.D. and Yan, carcinoma. Lancet 362, 1907–1917. Q., et al. (2001) Insight into hepatocellular carcinogenesis at [2] Feitelson, M.A., Sun, B., Satiroglu Tufan, N.L., Liu, J., Pan, J. transcriptome level by comparing gene expression profiles of and Lian, Z. (2002) Genetic mechanisms of hepatocarcinogenesis. hepatocellular carcinoma with those of corresponding noncan- Oncogene 21, 2593–2604. cerous liver. Proc. Natl. Acad. Sci. USA 98, 15089–15094. [3] Thorgeirsson, S.S. and Grisham, J.W. (2002) Molecular pathogen- [22] Ermolaeva, O., Rastogi, M., Pruitt, K.D., Schuler, G.D., Bittner, esis of human hepatocellular carcinoma. Nat. Genet. 31, 339–346. M.L. and Chen, Y., et al. (1998) Data management and analysis [4] Wilkens, L., Flemming, P., Gebel, M., Bleck, J., Terkamp, C. and for gene expression arrays. Nat. Genet. 20, 19–23. Wingen, L., et al. (2004) Induction of aneuploidy by increasing [23] Duggan, D.J., Bittner, M., Chen, Y., Meltzer, P. and Trent, J.M. chromosomal instability during dedifferentiation of hepatocellular (1999) Expression profiling using cDNA microarrays. Nat. Genet. carcinoma. Proc. Natl. Acad. Sci. USA 101, 1309–1314. 21 (Suppl.), 10–14. [5] Lau, S.H. and Guan, X.Y. (2005) Cytogenetic and molecular [24] Okabe, H., Satoh, S., Kato, T., Kitahara, O., Yanagawa, R. and genetic alterations in hepatocellular carcinoma. Acta Pharmacol. Yamaoka, Y., et al. (2001) Genome-wide analysis of gene Sin. 26, 659–665. expression in human hepatocellular carcinomas using cDNA [6] Lichter, P., Joos, S., Bentz, M. and Lampel, S. (2000) Compar- microarray: identification of genes involved in viral carcinogenesis ative genomic hybridization: uses and limitations. Semin. Hema- and tumor progression. Cancer Res. 61, 2129–2137. tol. 37, 348–357. [25] Korz, C., Pscherer, A., Benner, A., Mertens, D., Schaffner, C. and [7] Inazawa, J., Inoue, J. and Imoto, I. (2004) Comparative genomic Leupolt, E., et al. (2002) Evidence for distinct pathomechanisms hybridization (CGH)-arrays pave the way for identification of in B-cell chronic lymphocytic leukemia and mantle cell lymphoma novel cancer-related genes. Cancer Sci. 95, 559–563. by quantitative expression analysis of cell cycle and apoptosis- [8] Lapierre, J.M. and Tachdjian, G. (2005) Detection of chromo- associated genes. Blood 99, 4554–4561. somal abnormalities by comparative genomic hybridization. Curr. [26] Xiao, H.S., Huang, Q.H., Zhang, F.X., Bao, L., Lu, Y.J. and Opin. Obstet. Gynecol. 17, 171–177. Guo, C., et al. (2002) Identification of gene expression profile of [9] Solinas-Toldo, S., Lampel, S., Stilgenbauer, S., Nickolenko, J., dorsal root ganglion in the rat peripheral axotomy model of Benner, A. and Dohner, H., et al. (1997) Matrix-based compar- neuropathic pain. Proc. Natl. Acad. Sci. USA 99, 8360–8365. ative genomic hybridization: biochips to screen for genomic [27] Hu, R.M., Han, Z.G., Song, H.D., Peng, Y.D., Huang, Q.H. and imbalances. Genes Chromosomes Cancer 20, 399–407. Ren, S.X., et al. (2000) Gene expression profiling in the human [10] Pollack, J.R., Perou, C.M., Alizadeh, A.A., Eisen, M.B., Perga- hypothalamus-pituitary-adrenal axis and full-length cDNA clon- menschikov, A. and Williams, C.F., et al. (1999) Genome-wide ing. Proc. Natl. Acad. Sci. USA 97, 9543–9548. analysis of DNA copy-number changes using cDNA microarrays. [28] Marchio, A., Meddeb, M., Pineau, P., Danglot, G., Tiollais, P. Nat. Genet. 23, 41–46. and Bernheim, A., et al. (1997) Recurrent chromosomal abnor- [11] Snijders, A.M., Nowak, N., Segraves, R., Blackwood, S., Brown, malities in hepatocellular carcinoma detected by comparative N. and Conroy, J., et al. (2001) Assembly of microarrays for genomic hybridization. Genes Chromosomes Cancer 18, 59–65. genome-wide measurement of DNA copy number. Nat. Genet. [29] Wong, N., Lai, P., Lee, S.W., Fan, S., Pang, E. and Liew, C.T., 29, 263–264. et al. (1999) Assessment of genetic changes in hepatocellular [12] Pinkel, D., Segraves, R., Sudar, D., Clark, S., Poole, I. and carcinoma by comparative genomic hybridization analysis: rela- Kowbel, D., et al. (1998) High resolution analysis of DNA copy tionship to disease stage, tumor size, and cirrhosis. Am. J. Pathol. number variation using comparative genomic hybridization to 154, 37–43. microarrays. Nat. Genet. 20, 207–211. [30] Qin, L.X., Tang, Z.Y., Sham, J.S., Ma, Z.C., Ye, S.L. and Zhou, [13] Albertson, D.G. and Pinkel, D. (2003) Genomic microarrays in X.D., et al. (1999) The association of chromosome 8p deletion human genetic disease and cancer. Hum. Mol. Genet. 12, R145– and tumor metastasis in human hepatocellular carcinoma. Cancer R152. Res. 59, 5662–5665. [14] Pollack, J.R., Sorlie, T., Perou, C.M., Rees, C.A., Jeffrey, S.S. and [31] Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., Lonning, P.E., et al. (2002) Microarray analysis reveals a major Lundin, P., Maner, S., Massa, H., Walker, M., Chi, M., Navin, direct role of DNA copy number alteration in the transcriptional N., Lucito, R., Healy, J., Hicks, J., Ye, K., Reiner, A., Gilliam, program of human breast tumors. Proc. Natl. Acad. Sci. USA 99, T.C., Trask, B., Patterson, N., Zetterberg, A. and Wigler, M. 12963–12968. (2004) Large-scale copy number polymorphism in the human [15] Reyal, F., Stransky, N., Bernard-Pierrot, I., Vincent-Salomon, genome. Science 305 (5683), 525–528. A., de Rycke, Y. and Elvin, P., et al. (2005) Visualizing [32] Ye, Q.H., Qin, L.X., Forgues, M., He, P., Kim, J.W. and Peng, chromosomes as transcriptome correlation maps: evidence of A.C., et al. (2003) Predicting hepatitis B virus-positive metastatic chromosomal domains containing co-expressed genes–a study of hepatocellular carcinomas using gene expression profiling and 130 invasive ductal breast carcinomas. Cancer Res. 65, 1376– supervised machine learning. Nat. Med. 9, 416–423. 1383. [33] Tsou, A.P., Chuang, Y.C., Su, J.Y., Yang, C.W., Liao, Y.L. and [16] Aguirre, A.J., Brennan, C., Bailey, G., Sinha, R., Feng, B. and Liu, W.K., et al. (2003) Overexpression of a novel imprinted Leo, C., et al. (2004) High-resolution characterization of the gene, PEG10, in human hepatocellular carcinoma and in regen- pancreatic adenocarcinoma genome. Proc. Natl. Acad. Sci. USA erating mouse livers. J. Biomed. Sci. 10, 625–635. 101, 9067–9072. [34] Jonsson, G., Naylor, T.L., Vallon-Christersson, J., Staaf, J., [17] Heidenblad, M., Lindgren, D., Veltman, J.A., Jonson, T., Huang, J. and Ward, M.R., et al. (2005) Distinct genomic profiles Mahlamaki, E.H. and Gorunova, L., et al. (2005) Microarray in hereditary breast tumors identified by array-based comparative analyses reveal strong influence of DNA copy number alterations genomic hybridization. Cancer Res. 65, 7612–7621. on the transcriptional patterns in pancreatic cancer: implications [35] Ryu, J.W., Kim, H.J., Lee, Y.S., Myong, N.H., Hwang, C.H. and for the interpretation of genomic amplifications. Oncogene 24, Lee, G.S., et al. (2003) The proteomics approach to find 1794–1801. biomarkers in gastric cancer. J. Korean Med. Sci. 18, 505–509. J. Huang et al. / FEBS Letters 580 (2006) 3571–3581 3581

[36] Kasahara, K., Taguchi, T., Yamasaki, I., Kamada, M., Yuri, K. [40] Denhardt, D.T., Mistretta, D., Chambers, A.F., Krishna, S., and Shuin, T. (2002) Detection of genetic alterations in advanced Porter, J.F., Raghuram, S. and Rittling, S.R. (2003) Transcrip- prostate cancer by comparative genomic hybridization. Cancer tional regulation of osteopontin and the metastatic phenotype: Genet. Cytogenet. 137, 59–63. evidence for a Ras-activated enhancer in the human OPN [37] Chan, W.Y., Liu, Y., Li, C.Y., Ng, E.K., Chow, J.H. and Li, promoter. Clin. Exp. Metastasis 20 (1), 77–84. K.K., et al. (2002) Recurrent genomic aberrations in gastric [41] Stanier, P., Abu-Hayyeh, S., Murdoch, J.N., Eddleston, J. and carcinomas associated with Helicobacter pylori and Epstein-Barr Copp, A.J. (1998) Paralogous sm22alpha (Tagln) genes map to virus. Diagn. Mol. Pathol. 11, 127–134. mouse chromosomes 1 and 9: further evidence for a paralogous [38] Rittling, S.R. and Chambers, A.F. (2004) Role of osteopontin in relationship. Genomics 51, 144–147. tumour progression. Br. J. Cancer 90, 1877–1881. [42] Shi, Y.Y., Wang, H.C., Yin, Y.H., Sun, W.S., Li, Y. and [39] Rangaswami, H., Bulbule, A. and Kundu, G.C. (2006) Osteo- Zhang, C.Q., et al. (2005) Identiﬁcation and analysis of tumour- pontin: role in cell signaling and cancer progression. Trends Cell associated antigens in hepatocellular carcinoma. Br. J. Cancer 92, Biol. 16, 79–87. 929–934.