Genome-Wide Molecular Profiles of HCV-InducedDysplasia and Hepatocellular Carcinoma

Elisa Wurmbach,1 Ying-bei Chen,1 Greg Khitrov,1 Weijia Zhang,1 Sasan Roayaie,1 Myron Schwartz,1 Isabel Fiel,1

Swan Thung,1 Vincenzo Mazzaferro,2 Jordi Bruix,3 Erwin Bottinger,1 Scott Friedman,1

Samuel Waxman,1 and Josep M. Llovet1,3

Although HCC is the third-leading cause of cancer-related deaths worldwide, there is only anelemental understanding of its molecular pathogenesis. In western countries, HCV infectionis the main etiology underlying this cancer’s accelerating incidence. To characterize themolecular events of the hepatocarcinogenic process, and to identify new biomarkers for earlyHCC, the gene expression profiles of 75 tissue samples were analyzed representing thestepwise carcinogenic process from preneoplastic lesions (cirrhosis and dysplasia) to HCC,including 4 neoplastic stages (very early HCC to metastatic tumors) from patients with HCVinfection. We identified gene signatures that accurately reflect the pathological progressionof disease at each stage. Eight genes distinguish between control and cirrhosis, 24 betweencirrhosis and dysplasia, 93 between dysplasia and early HCC, and 9 between early andadvanced HCC. Using quantitative real-time reverse-transcription PCR, we validated severalnovel molecular tissue markers for early HCC diagnosis, specifically induction of abnormalspindle-like, microcephaly-associated protein, hyaluronan-mediated motility receptor, pri-mase 1, erythropoietin, and neuregulin 1. In addition, pathway analysis revealed dysregu-lation of the Notch and Toll-like receptor pathways in cirrhosis, followed by deregulation ofseveral components of the Jak/STAT pathway in early carcinogenesis, then upregulation ofgenes involved in DNA replication and repair and cell cycle in late cancerous stages. Con-clusion: These findings provide a comprehensive molecular portrait of genomic changes inprogressive HCV-related HCC. (HEPATOLOGY 2007;45:938-947.)

Hepatocellular carcinoma (HCC) is the fifth mostcommon cancer worldwide, with 626,000 newcases reported in 2002.1 The incidence of HCC

is increasing in Europe and the United States2 and iscurrently the leading cause of death among individualswho have cirrhosis.3 Hepatitis C virus (HCV) infection isthe main risk factor in western countries and Japan,2,3

with one third developing HCC over their lifetime.No clear sequence of genetic events can yet explain the

molecular pathogenesis of HCC.4-6 A stepwise process hasbeen proposed in which external stimuli induce geneticalterations in mature hepatocytes or progenitor cells, lead-ing to cell death, cellular proliferation, dysplasia, and neo-plasia over 20 to 30 years.5 Global gene expressionprofiling is a valuable technology to unravel the pathogen-esis of HCC and explore its heterogeneous origins.7

Attempts to study risk factors (e.g., HBV or HCVinfection) and alcohol-induced HCC8-10 or differentstages of the disease,11-13 recurrence,14,15 and survival16,17

have been inconsistent. To reduce this complexity, wefocused exclusively on gene expression in eight patholog-ical stages of HCV-induced hepatocarcinogenesis and

Abbreviations: HCC, hepatocellular carcinoma; PAM, prediction analysis ofmicroarray; qPCR, quantitative real-time reverse-transcriptase PCR; SAM, signif-icance analysis of microarray.

From the 1Mount Sinai Liver Cancer Program (Division of Liver Disease, Di-vision of Hematology/Oncology, Department of Medicine; Recanati-Miller Trans-plantation Institute; Department of Pathology), Mount Sinai School of Medicine,New York, NY; the 2National Cancer Institute, Milan, Italy; and the 3BCLCGroup, IDIBAPS, Liver Unit, Hospital Clinic, Barcelona, Spain.

Received October 9, 2006; accepted November 30, 2006.Supported by the Samuel Waxman Cancer Research Foundation, the National

Institutes of Health (grant DK37340 to S. F.), the Bendheim Family Trust (toS. F.), the Instituto de Salud Carlos III (FIS 05/1285 to J. B.) and the ItalianAssociation for Cancer Research (to V. M.).

Josep M. Llovet is Professor of Research at Institut Catala de Recerca Avancada(ICREA, IDIBAPS, Hospital Clınic).

Address reprint requests to: Elisa Wurmbach, Ph.D., Division of Hematology/Oncology, Department of Medicine, Mount Sinai School of Medicine, MadisonAvenue 1468, Box, 1178, New York, NY 10029. E-mail: elisa.wurmbach@mssm.edu. Josep M. Llovet, M.D., Division of Liver Diseases, Department of Medicine,Mount Sinai School of Medicine, Madison Avenue 1425, 11F-70, Box 1104, NewYork, NY 10029. E-mail: josep.llovet@mssm.edu; fax: 212-241-2138/212-996-9688.

Copyright © 2007 by the American Association for the Study of Liver Diseases.Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/hep.21622Potential conflict of interest: Nothing to report.

938

compared their differential expression to that of normalliver tissue.

Early detection of HCC followed by appropriate treat-ment can decrease tumor-related deaths.3 A variety ofgenomic studies have identified potential biomarkers fordetection of early HCC,18 such as HSP70,19 GPC3,20

TERT, STK15, and PLA2.12 We recently identified a3-gene signature (GPC3, XLKD1, BIRC5) capable ofdifferentiating dysplastic nodules from early tumors basedon the analysis of 56 potential candidate genes.21 Thesearch for new and more accurate markers must be trans-lated into the clinical setting, where they can advanceHCC screening.

Patients and MethodsSamples were obtained from patients undergoing re-

section or liver transplantation at 3 university hospitals inthe United States (Mount Sinai Hospital, New York, NY)and Europe (Hospital Clınic, Barcelona, Spain, and Na-tional Cancer Institute, Milan, Italy). Laboratory tech-niques were centralized in the laboratories of theDivisions of Liver Disease and Hematology/Oncologyand the Center of Life Sciences at the Mount Sinai Schoolof Medicine. The research protocol was approved by therespective institutional review boards, and informed con-sent was obtained in all cases.

Sample Characteristics. The characteristics of the 75samples (48 patients) selected to assess the gene transcrip-tional profiles are described in Supplementary Table 1.Sixty-five samples were obtained from 38 patients withHCV infection representing 13 samples from cirrhotictissue, 17 dysplastic nodules, and 35 HCCs. Patients withHBV-positive markers, or a background of alcohol con-sumption, nonalcoholic steatohepatitis, hemochromato-sis, other causes of chronic liver disease or priorlocoregional treatment were excluded. Results were com-pared with samples of normal tissue obtained from thehealthy livers of 10 patients undergoing resection for he-patic hemangioma (1), focal nodular hyperplasia (3), ad-enoma/cystadenoma (2), neuroendocrine tumor (1), andliving donor liver transplantation (1).

Sample Collection and Pathological Data. Afterwritten informed consent was obtained, key clinicopath-ological variables were recorded. Fresh tissue specimenswere collected in the operating room/pathology depart-ment and processed within 1 hour to minimize degrada-tion. Samples were split: one part was collected in liquidnitrogen or RNA-later solution (Ambion, The Wood-lands, TX), and stored at �80°C until use, and the otherwas paraffin-embedded for pathological examination. Incases of liver transplantation, explanted livers were sec-tioned into 5-mm slices, and nodules that were distinct in

terms of size, color, texture, or degree of bulging wereexamined microscopically. Tissue sampling was handledusing thin sections (4 �m) of the target area, which wasmicrodissected under a scanning microscope for PCRstudies.

Pathological classification of the samples was estab-lished by 2 expert pathologists (S. T. and I. F.) who re-viewed each slide independently according to theInternational Working Party.22 Four pathological HCCstages were defined among the 35 target samples: (i) veryearly HCC (8 cases), which included well-differentiatedtumors �2 cm in diameter with no vascular invasion/satellites (size range: 8-20 mm); (ii) early HCC (10 cases),which included tumors measuring �2 cm with micro-scopic vascular invasion/satellites; well- to moderately dif-ferentiated tumors measuring 2-5 cm without vascularinvasion/satellites; or 2-3 well-differentiated nodulesmeasuring �3 cm (size range: 3-45 mm); (iii) advancedHCC (7 cases), which included poorly differentiated tu-mors measuring �2 cm with microvascular invasion/sat-ellites or tumors measuring �5 cm; and (iv) veryadvanced HCC (10 cases), which included tumors withmacrovascular invasion or diffuse liver involvement.

Microarray Hybridization. RNA extraction wasperformed as described previously.21 28S/18S ratios mea-sured with a Bioanalyzer (Agilent Technologies, PaloAlto, CA) had to exceed 0.8 for inclusion (SupplementaryTable 1). Five micrograms of purified total RNA waslabeled according to the manufacturer’s protocol (Af-fymetrix, Santa Clara, CA). Fragmentation of the com-plementary RNA to 50-200 bp was confirmed by theBioanalyzer. Fifteen micrograms of fragmented comple-mentary RNA was hybridized on the human U133 plus2.0 array (Affymetrix). To minimize variations from bio-logical samples, we included in the microarray analysisonly samples with a present call between 38% and 47%,and a 3� to 5� ratio for GAPDH of �3.18 and for ACTBof �10.41 (Supplementary Table 1).

Microarray Data Analysis. The raw data were ana-lyzed and processed in GeneTraffic (Stratagene, La Jolla,CA). Data were normalized by applying the GC robustmulti-array average algorithm, and the baseline was cal-culated using the geometric mean from the data of the 10normal liver tissue samples. Hierarchical clustering wasused to detect overall expression patterns in specimens ofdifferent stages. The microarray data were analyzed from73 samples (2 samples were excluded for quality reasons).Analysis included the significant analysis of microarray(SAM) data and the predictor of microarray analysis(PAM) data (http://www-stat.stanford.edu/�tibs/PAM).The significance level was chosen not to exceed 5% (P �0.05). Three cirrhotic samples from patients without

HEPATOLOGY, Vol. 45, No. 4, 2007 WURMBACH ET AL. 939

HCC were excluded from validation tests, because nodifference was identified to the gene expression of cir-rhotic tissue from patients with HCC.

Information about genes participating in known sig-naling pathways were derived from Entrez Gene (http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?db�gene) andKEGG pathway (http://www.genome.jp/kegg/path-way.html) databases. To identify members of particularpathways, we combined the KEGG gene number with theAffymetrix identifier.

This report has been written to conform to the Mini-mum Information About a Microarray Experimentguidelines.23 The microarray data will be available atwww.ncbi.nlm.nih.gov/projects/geo (GSE 6764).

Quantitative PCR. Selected genes were further testedvia quantitative real-time reverse-transcriptase PCR(qPCR). Five micrograms of total purified DNase-I–treated RNA was converted into complementary DNAusing oligo dT and Superscript III (Invitrogen, Carlsbad,CA), followed by RNaseH (Invitrogen, Carlsbad, CA)digestion. The complementary DNA was diluted 1/100and 5 �l were used as a template per PCR reaction. Quan-titative PCR (qPCR) assays were performed as de-scribed,24 with few modifications. The qPCR wasperformed with SYBR Green (Molecular Probes, Eugene,OR) and Platinum Taq (Invitrogen, Carlsbad, CA) onthe ABI Prism 7900 (Applied Biosystems, Foster City,CA). Primers are listed in Supplementary Table 2. Am-plicon size and reaction specificity were confirmed byagarose gel electrophoresis and melting curve analysis.All PCR reactions were run in triplicate. The raw datawere analyzed after subtracting the background andsetting the threshold to obtain the Ct value usingSDS2.2 (Applied Biosystems, Foster City, CA) in Ex-cel; the median Ct was taken from triplicate reactions

and normalized to RPL41 and SFRS4. Results wereexpressed as fold changes.

Results

Gene Expression Patterns Confirm PathologicalClassification. We compared the gene expression pro-files of 75 tissue samples using the genome-wide platformfrom Affymetrix (U133 Plus 2.0). To establish the qualityof the expression profiles and their correlation with thehistological stages, an unsupervised hierarchical clusteringwas performed using the Pearson correlation distance(Fig. 1). Two main clusters were formed, one that in-cluded 34/35 HCC samples (Fig. 1, in red) and one in-cluding all 30 preneoplastic lesions. The normal samplesremained outside of these 2 main clusters. Only 1 neo-plastic sample (ve-NY26) was misclassified (Fig. 1). Thisvery early HCC sample clustered together with dysplasticnodules obtained from the same patient. Within the can-cer cluster, the very early, early, and very advanced stagesof HCC formed subclusters; the precancerous cluster wassplit into cirrhotic and dysplastic subclusters. Differentcluster algorithms (Manhattan- and Pearson-centeredand absolute) led to similar results (data not shown) sup-porting the conclusion that the gene expression profilesrobustly reflected the histological classification.

Unique Molecular Markers of Individual Stages ofHCV-Induced HCC. To identify reliable molecularmarkers of HCV-induced HCC stages, we applied twostatistical tests: SAM, which detect genes significantlydysregulated between 2 groups, and PAM, which identifya subset of genes that best characterize each group on thebasis of “nearest shrunken centroids” (the more differentthe groups, the fewer genes needed to distinguish betweenthem). For the analyses, 5 groups were considered: con-

Fig. 1. Hierarchical clustering of 75 tissue samples included in the HCV-induced HCC study using the Pearson correlation distance. Tissue samplesare indicated on top (see Supplementary Table 1 for characteristics). The colored bars mark clusters: green, precancer; red, HCC; blue, controls.Subclusters are marked in yellow, orange, red, green, and turquoise.

940 WURMBACH ET AL. HEPATOLOGY, April 2007

trol, cirrhosis, dysplasia, early HCC, and advanced HCC.Very early and early HCC, as well as advanced and veryadvanced HCC, were combined to enhance the statisticalpower of the comparison of microarray data.

The differential expression of all genes found by SAMfor all comparisons is shown in Supplementary Fig. 1, andthe number of genes that were significantly differentiallyexpressed is summarized in Table 1. The PAM analysiswas optimized by testing different thresholds, an ap-proach that led to a reduced number of genes required fora robust prediction (Supplementary Fig. 2). The numberof genes found by PAM is displayed in Table 1. To definethe optimal set of genes able to delineate hepatocarcino-genesis, we selected genes identified by both tests. Figure2A shows the expression pattern of these genes in all tissue

samples. Roughly 50% of these genes were down-regu-lated in HCC. For the up-regulated genes in HCC, num-ber and magnitude was greater for advanced stages. Acouple of genes were upregulated in cirrhosis, while only afew genes showed upregulation in dysplasia. Based onthe expression of these identified genes (Fig. 2A), PAMwas used for each of the 72 samples to determine theprobability of being assigned to one of the pathologicalstages of hepatocarcinogenesis. As can be seen in Fig.2B, 64 of 72 samples (89%) had a probability of almost1 (100% fit), whereas the remaining had a suboptimalprobability and thus did not fit the classification. Theresults were comparable to those obtained in the orig-inal PAM analysis (compare Supplementary Fig. 2 withFig. 2B).

Table 1. Genes Deregulated in Progression of HCV-Related HCC

Group SAM PAM (Threshold) No. of Genes in the Signature

Control (n � 10) vs. cirrhosis (n � 13) 112 9 (7) 8Cirrhosis vs. dysplasia (n � 17) 29 59 (4.6) 24Dysplasia vs. early HCC (n � 18) 104 350 (3.8) 93Early HCC vs. advanced HCC (n � 17) 9 120 (3.5) 9Dysplasia vs. HCC (n � 35) 693 61 (5.9) 53

NOTE. The number of genes identified via microarray analysis defining all step-by-step carcinogenic stages from preneoplastic lesions to advanced HCC detected bystatistical tests is shown. The combination of SAM and PAM analyses identified the gene signatures. Thirty-one genes were detected in more than one comparison(187 � 31 � 156). One gene was significantly changed between cirrhosis, dysplasia, and early HCC, and 30 genes were consistently identified in the comparisonsbetween dysplasia and HCC and dysplasia and early HCC.

Fig. 2. Molecular markers for HCV induced HCC. (A) Heat map of the gene signature, identified by SAM and PAM. The 72 samples are representedin columns (c, control; ci, cirrhosis; dn, dysplasia; ve/e, early HCC; a/aa, advanced HCC) and the expression pattern (red, up-regulation; green,downregulation; black, no change) of the genes is shown in rows. (B) PAM analysis, using the genes displayed in (A) to calculate the probability ofeach sample being assigned to one of the five stages of HCV-HCC as predicted by each sample’s gene signature (c, control; ci, cirrhosis; dn, dysplasia;e, early HCC; a, advanced HCC). The x-axis represents the sample as indicated at the top of the figure. The y-axis represents the calculated probability,the sum of which must be 1 for each sample.

HEPATOLOGY, Vol. 45, No. 4, 2007 WURMBACH ET AL. 941

In summary, 8 key genes can differentiate betweencontrol and cirrhotic samples, 24 between cirrhosis anddysplasia, 53 between dysplasia and HCC, and 93 be-tween dysplasia and early tumors, whereas 9 genes werefound to define the progression of HCC (Table 1 andSupplementary Table 3). Multiclass analysis did not ledto such informative results (data not shown).

Gene markers for a specific disease stage should beunique and expressed at elevated levels. Eight genes werechosen fitting these criteria for confirmation via qPCR:one gene for cirrhosis (CLDN10), three genes for dyspla-sia (GREM2, EPO, and NRG1); and four genes as po-tential markers for early HCC (ASPM, PRIM1, HMMR,and IRAK1). Analysis with qPCR verified that these geneswere indeed differentially expressed (Fig. 3). Student ttests between high-grade dysplastic nodules and very earlyHCC, as well as between dysplasia and early HCC, re-sulted in P values below 0.05 for the differential expres-sion of 5 genes (Fig. 3). These findings open a spectrum ofmolecular markers for different stages of HCC.

Significantly Regulated Genes With Known Biolog-ical Functions. To examine the SAM deregulated genes(Table 1 and Supplementary Fig. 1) in the context of theinitiation and progression of HCC, we grouped the genesaccording to their biological function using the informa-tion found in the Entrez Gene (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db�gene) and KEGG (http://www.genome.jp/kegg/pathway.html) databases. Table 2shows the number of genes significantly deregulated withcorresponding biological functions, with the restriction of

at least 2 genes per function to emphasize the functionswith most genes differentially expressed.

In cirrhotic liver samples, genes with functions in theimmune response and cell adhesion were upregulated,and genes involved in metabolism showed varyingchanges. These results reflect the transition from normalto cirrhosis, where the liver function becomes impairedand extracellular matrix deposition increases.25 Only afew genes were significantly down-regulated between cir-rhosis and dysplasia, most likely because of the heteroge-neity of the dysplastic nodules (Supplementary Fig. 1B).They had functions in immune response, proteolysis, andRNA processing.

In contrast, the onset of HCC was associated withmajor changes. Genes whose products had functions incell cycle, protein biosynthesis and RNA processing, celldivision, DNA replication, protein modification, ubiq-uitin cycle, or chromatin modulation were up-regulated.Genes linked to the immune response, cytokine–cytokinereceptor interactions, Ca signaling, the Jak/STAT path-way, and blood coagulation were down-regulated. Fur-thermore, the progression of HCC to advanced stages wascharacterized by additional up-regulation of genes in-volved in cell cycle regulation.

Pathways Affected in HCV-Induced HCC. To gainmore insight into the types of pathways that might beaffected during HCV-induced HCC, we chose 15 majorpathways of signaling, processing of genetic information,and cell growth and death (Supplementary Table 4 and

Fig. 3. Quantitative PCR of 8 molecular markers: (A) CLDN10; (B) GREM2; (C) EPO; (D) NRG1; (E) ASPM; (F) PRIM1; (G) HMMR; and (H) IRAK1.Quantitative PCR was performed on 72 tissue samples. P values for relevant stage transitions are indicated below each panel. Medians of normalizedfold changes, with standard deviations, were plotted for each disease stage.

942 WURMBACH ET AL. HEPATOLOGY, April 2007

Supplementary Fig. 3) and present the obviously dysregu-lated genes.

Cirrhosis showed strong up-regulation of JAG1, a li-gand of the Notch receptor, and participants of the Toll-like receptor pathway such as STAT1 and CXCL9-11,IFN-�–inducible genes.26 Dysplasia was characterized byup-regulation of genes involved in cytokine–cytokine re-ceptor interactions and the Jak/STAT pathway (EPO,EPOR, CISH, STAT3, and SOCS3) as well as cell cycleregulation (GADD45B, GADD45G, and GADD45A).Early HCCs were marked by striking changes in all 15

pathways—in particular, the down-regulation of severalcomponents of the Toll-like receptor pathway (IFNAR1,TLR4, FOS, and CD14), tumor suppressors of the Jak/STAT pathway (EPO, EPOR, SPRY2, and SOCS2) aswell as participants of the transforming growth factor �pathway (BMPR2, ID2, THBS1, and DCN), and theinsulin-signaling pathway (FBP1, PCK1, PCK2, GYS2,FOXO1A, and SOCS2). Up-regulation was found forcomponents of the wnt pathway (DKK1, FZD6, FZD7,PLCB1, and LEF1) as well as for PTCH, a member of theHedgehog pathway.

Table 2. Numbers of Significantly Regulated Genes with Known Biological Functions Detected via SAM Analysis

Function/Pathway*

C-CI CI-DN

HCC Onset HCC Progression

DN–HCC DN–ve/eEarly (ve/e) vs.

Advanced (a/aa)

Up Down Up Down Up Down Up Down Up Down

Immune response 17 4 11 5

MetabolismMetabolism 4 2 12 35 12Drug metabolism 2

Cell adhesionExtracellular matrix 3Tight junction 2 1 1Focal adhesion 2 2

Cell proliferationCell proliferation 2 1Cell division 7Cell cycle 21 2 2 5Helicase activity 2

Protein synthesisProtein synthesis 15 2RNA processing 2 14 2

DNA replicationDNA replication 7Chromatin modulation 3DNA repair 3

Ubiquitin cycle–associated 7

Apoptosis 3 4 2

SignalingSignal transduction 2 1Cytokine–cytokinereceptor interaction 5

Ca2� pathway 5Jak/STAT pathway 5Toll-like receptor pathway 1 1 1 1TGF-� signaling 4

OtherTransport 2 2 2 10 7 1 4Proteolysis 3 2 1 1Protein modification 7Blood coagulation 4

Abbreviations: C, control; CI, cirrhosis; DN, dysplasia; TGF-�, transforming growth factor �.*Function/pathways were included if containing at least 2 deregulated genes.

HEPATOLOGY, Vol. 45, No. 4, 2007 WURMBACH ET AL. 943

Finally, progression of HCC was characterized bystrong up-regulation of genes involved in cell prolifera-tion, DNA repair and replication (PRIM1, PRIM2),ubiquitin cycle, RNA processing, and cell cycle regulation(ASPM, PTTG1, CCNB1, CDKN2C, and CDKN2A).

Cell Cycle Regulation. Cell cycle regulation wasthe most widely affected pathway in HCC, and corre-lated best with the progression of cancer. CDC14B,MAD1L1, GADD45G, GADD45B, GADD45A, HDAC6,CDKN1A, and CCND1 were up-regulated in dys-plastic nodules followed by down-regulation in earlyHCC. Confirmatory qPCR data were obtained forGADD45A, GADD45B, and HDAC6 on all tissuesamples (Fig. 4A). In HCC, 56 genes showed progres-sive increase during the transition from very early tovery advanced stages (Fig. 4B and Supplementary Fig.3). The up-regulation of 4 of these genes (CCNB1,CDKN2C, CDKN2A, and PTTG1) was confirmed viaqPCR (Fig. 4A).

Some of these cell cycle deregulated genes have inhib-itory functions, which is consistent with our data:GADD45 (down-regulated in HCC) inhibits CCNB(up-regulated in HCC); CDKN2 (up-regulated in HCC)inhibits CCND (not significantly regulated in HCC);CDKN1A (not significantly regulated in HCC) inhibitsCCNE and CCNA (both up-regulated in HCC).

DiscussionThrough analysis of gene expression profiles of 75

samples, representing the entire spectrum of progressionof HCV-HCC, we have identified a set of genes defininga molecular portrait of progressive stages of hepatocarci-nogenesis. This set discriminates between each of thestages from normal, cirrhotic, and dysplastic to early andadvanced HCC. Moreover, a detailed analysis yieldednew biomarkers that may be used for early detection ofHCC.

Fig. 4. Cell cycle involved in HCV-related HCC. (A) Quantitative PCR of GADD45A, GADD45B, HDAC6, CCNB1, CDKN2C, CDKN2A, and PTTG1performed on 72 tissue samples. P values for relevant stage transitions are indicated below each panel. Medians of normalized fold changes, withstandard deviations, were plotted for each disease stage. (B) Heat map of 56 genes involved in cell cycle regulation (corresponding to a region fromSupplementary Fig. 3, enlarged).

944 WURMBACH ET AL. HEPATOLOGY, April 2007

Although other studies have described the gene expres-sion pattern of a subgroup of lesions in either HBV-in-duced HCC, or from patients with mixed etiologies(HBV, HCV, hemochromatosis, alcohol, chronic cho-lestasis, and others),9-16,27-29 we have instead comprehen-sively analyzed each of the stages of HCV-induced HCC,the main etiology underlying the increasing HCC inci-dence in the United States and Europe.2,3 In contrast tosome studies that compared tumor tissues with the sur-rounding tissue,9,12 we used normal liver tissues as a base-line. This approach enabled us to detect differentialexpression between normal liver and all stages involved inthe hepatocarcinogenic process, including cirrhosis.

Screening programs in the western countries and inJapan indicate that early diagnosis of HCC is only feasiblein 30%-60% of cases,3,30 and new biomarkers are ur-gently needed.18,30 Moreover, serum biomarkers such asAFP, des-gamma-carboxyprothrombin, and AFP-L3fraction are not reliable for early diagnosis.31

Several markers of early HCC have been reported, includ-ing HSP70, GPC3, STK15, and PLA2.12,19,20 A molecularindex including a 13-gene set has also been proposed (in-cluding TERT, TOP2A, CXCL12, and IGF2).32 More re-cently, a microarray-generated signature of 120 genesreportedly discriminates between dysplastic nodules andHCC in HBV patients.13 Recently, we reported a 3-genesignature able to discriminate with high accuracy preneoplas-tic lesions from early tumors via qPCR and immunohisto-chemistry.21 These 3 genes, GPC3, XLKD1, and BIRC5,were confirmed by microarray analysis to be significantlydysregulated between dysplastic tumors and HCC, and 2 ofthem (GPC3 and XLKD1) were included in our gene signa-tures (Supplementary Table 3). Despite differences in studydesign between our report and those examining different ormixed etiologies, or comparing tumor tissues with surround-ing tissues, many of the dysregulated genes were found incommon with prior studies (GPC3, XLKD1, BIRC5,TOP2A, PTTG1, BUB1, DKK1, CCNB1, HMMR,CDKN2C, MCM7, GALNT109,11,13,15,21,27,29). However,we have extended this analysis further by validating thosegenes that were significantly up-regulated in specific diseasestages with qPCR. This approach identified 3 genes thatwere elevated in dysplasia (GREM2, EPO and NRG1), 7that were elevated in HCC (PRIM1, ASPM, HMMR,PTTG1, CCNB1, CDKN2C, and CDKN2A), and 1 thatwas elevated in the surrounding cirrhotic tissue (CLDN10).Notably, the significance of these differential expressions, inparticular during the transition from preneoplastic to earlyHCC was very high (Figs. 3, 4). Four of these genes(HMMR, PTTG1, CCNB1, and CDKN2C) have beenlinked previously to HCC,9,11,15,27,29 and the receptor ofEPO was downregulated in HCC.28 PRIM1, ASPM, and

HMMR were associated with other neoplasms, but none ofthese genes has been tested yet as a serum or tissue marker.PRIM1 plays an essential role in cell proliferation, and am-plifications have been described in several cancers, includingbladder cancer, breast cancer, and osteosarcoma.33 PRIM1aberrations affect the cell cycle transition from G1 to S phase.ASPM may regulate neural stem cell proliferation and/ordifferentiation during brain development, possibly by medi-ating spindle assembly during cell division. ASPM is report-edly up-regulated in ovarian cancer.34 Finally, HMMR, anoncogene, encodes a cell surface receptor that mediates mo-tility of many cell types and acts downstream of Ras.35

Current studies have been unable to define either thecritical hits and sequence of events leading to HCC,4,5 orits cellular origins.6 Once established, the canonical wntpathway is activated in at least one-third of HCCs,4,5,36-38

disturbances in cell cycle regulation occur in 30%-40% ofcases,4,5 activation of the Ras/MAPK pathway has beenconsistently reported,39 and many other mechanisms con-tribute to this neoplasm, making it difficult to establishwhich of these are critical.4,5,36

The present study describes dysregulated genesthroughout all stages of HCV-induced HCC. This ap-proach, unique in its design, detected novel genes dys-regulated in the early stages of hepatocarcinogenesis,which may play an important role in the progression ofthe disease. In cirrhotic tissue, genes with functions in theimmune response (including IFN-�–inducible genessuch as CXCL9-11), cell adhesion, and metabolism weredifferentially expressed, in agreement with published da-ta.25,26 Conversely, the strong up-regulation of JAG1 incirrhosis is a novel finding, but it has also been reported inprostate cancer and cervical tumors.40 In addition, severalcomponents of the Jak/STAT and the Toll-like receptorpathway were deregulated in precancerous tissues andearly HCC. STAT1 was up-regulated in cirrhosis, whosegene product can be bound by the HCV core protein andplays an important role in antiviral immunity.41 Severalcomponents of the Jak/STAT pathway—particularlyEPO, EPOR, CISH, STAT3, and SOCS3—were alsoup-regulated in dysplasia and may yield new insights intothe transition to HCC. Down-regulation of these genes aswell as of SOCS2 and of SPRY2 in HCC is consistentwith other studies.39,42 Through the suppression of inhib-itors such as CISH and SOCS1-3, the Jak/STAT pathwaybecomes activated in early HCC.39

Another novel finding is the increase in GREM2 mes-senger RNA in dysplasia. GREM2 is a BMP antagonistwithin the transforming growth factor � pathway (http://www.genome.jp/kegg/pathway.html), several compo-nents of which were also down-regulated in HCC, inaccordance with published data.43 Components of the

HEPATOLOGY, Vol. 45, No. 4, 2007 WURMBACH ET AL. 945

wnt/beta-catenin pathway, such as FZD7 and FZD6,were up-regulated at early stages of HCC. FZD7, a wntreceptor, was overexpressed in 90% of HBV-inducedHCC, which correlated with enhanced cellular motility.38

On the other hand, DKK1, a wnt inhibitor, was alsoup-regulated in HCC, similar to another HCC microar-ray study27 and in hepatoblastomas and Wilms’ tumors.44

PTCH, a critical receptor of the Hedgehog pathway, wasup-regulated, suggesting a potential role in early carcino-genesis.45

Many microarray studies confirm the cell cycle dereg-ulation as a consistent event in HCC.9,12,13,27 We found itto be the most affected pathway, involving 56 genes, withnovel alterations present in dysplasia (up-regulation ofGADD45, MAD1L1, HDAC6, and CCND1). The geneproduct of MAD1L1 has an antiproliferative function inthat it suppresses TERT,46 which is up-regulated inHCC.21,47 MAD1L1 is decreased in cancer.48 This geneproduct also affects the coordination of ribosome biogen-esis and cell growth, which is consistent with our data.

In HCC, GADD45 is decreased,49 allowing the in-crease of CCNB1, which promotes the G2/M transitionand is elevated in cancers,50 including HCC. In addition,CDKN2C and CDKN2A messenger RNAs were signifi-cantly up-regulated in HCC. Increased CDKN2C mes-senger RNA has been detected repeatedly in HCC viamicroarray analysis,9,12 whereas CDKN2A is up-regu-lated in pancreatic tumors. The products are likely tumorsuppressors, because both inhibit the CDK4 kinase(http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db�gene), which associates with CCND1.

In conclusion, we identified gene signatures that accu-rately distinguish the pathological stages of HCC andhave uncovered potential molecular markers for early di-agnosis. Furthermore, several components of signalingpathways are dysregulated in preneoplastic stages and arefurther altered during the transition to HCC—in partic-ular, components of the Jak/STAT signaling pathway.The progression of HCC was best characterized by pro-gressive up-regulation of 56 cell cycle genes. Combined,these data provide a basis for optimizing the selection ofmolecular targets for HCC therapies.

Acknowledgment: Many thanks to Andreas Jenny,Justine Rudner, and Sarah Rosenbaum for careful readingof the manuscript and to Liliana Ossowski for helpfuldiscussions.

References1. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA

Cancer J Clin 2005;55:74-108.2. El-Serag HB, Mason AC. Rising incidence of hepatocellular carcinoma in

the United States. N Engl J Med 1999;340:745-750.

3. Llovet JM, Burroughs A, Bruix J. Hepatocellular carcinoma. Lancet 2003;362:1907-1917.

4. Bruix J, Boix L, Sala M, Llovet JM. Focus on hepatocellular carcinoma.Cancer Cell 2004;5:215-219.

5. Thorgeirsson SS, Grisham JW. Molecular pathogenesis of human hepato-cellular carcinoma. Nat Genet 2002;31:339-346.

6. Farazi PA, DePinho RA. Hepatocellular carcinoma pathogenesis: fromgenes to environment. Nat Rev Cancer 2006;6:674-687.

7. Thorgeirsson SS, Lee JS, Grisham JW. Molecular prognostication of livercancer: end of the beginning. J Hepatol 2006;44:798-805.

8. Delpuech O, Trabut JB, Carnot F, Feuillard J, Brechot C, Kremsdorf D.Identification, using cDNA macroarray analysis, of distinct gene expres-sion profiles associated with pathological and virological features of hepa-tocellular carcinoma. Oncogene 2002;21:2926-2937.

9. Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R, Yamaoka Y, et al.Genome-wide analysis of gene expression in human hepatocellular carci-nomas using cDNA microarray: identification of genes involved in viralcarcinogenesis and tumor progression. Cancer Res 2001;61:2129-2137.

10. Iizuka N, Oka M, Yamada-Okabe H, Mori N, Tamesa T, Okada T, et al.Comparison of gene expression profiles between hepatitis B virus- andhepatitis C virus-infected hepatocellular carcinoma by oligonucleotide mi-croarray data on the basis of a supervised learning method. Cancer Res2002;62:3939-3944.

11. Ye QH, Qin LX, Forgues M, He P, Kim JW, Peng AC, et al. Predictinghepatitis B virus-positive metastatic hepatocellular carcinomas using geneexpression profiling and supervised machine learning. Nat Med 2003;9:416-423.

12. Smith MW, Yue ZN, Geiss GK, Sadovnikova NY, Carter VS, Boix L, et al.Identification of novel tumor markers in hepatitis C virus-associated hep-atocellular carcinoma. Cancer Res 2003;63:859-864.

13. Nam SW, Park JY, Ramasamy A, Shevade S, Islam A, Long PM, et al.Molecular changes from dysplastic nodule to hepatocellular carcinomathrough gene expression profiling. HEPATOLOGY 2005;42:809-818.

14. Kurokawa Y, Matoba R, Takemasa I, Nagano H, Dono K, Nakamori S, etal. Molecular-based prediction of early recurrence in hepatocellular carci-noma. J Hepatol 2004;41:284-291.

15. Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, et al.Oligonucleotide microarray for prediction of early intrahepatic recurrenceof hepatocellular carcinoma after curative resection. Lancet 2003;361:923-929.

16. Lee JS, Chu IS, Heo J, Calvisi DF, Sun Z, Roskams T, et al. Classificationand prediction of survival in hepatocellular carcinoma by gene expressionprofiling. HEPATOLOGY 2004;40:667-676.

17. Lee JS, Heo J, Libbrecht L, Chu IS, Kaposi-Novak P, Calvisi DF, et al. Anovel prognostic subtype of human hepatocellular carcinoma derived fromhepatic progenitor cells. Nat Med 2006;12:410-416.

18. Marrero JA, Lok AS. Newer markers for hepatocellular carcinoma. Gas-troenterology 2004;127:S113-S119.

19. Chuma M, Sakamoto M, Yamazaki K, Ohta T, Ohki M, Asaka M, et al.Expression profiling in multistage hepatocarcinogenesis: identification ofHSP70 as a molecular marker of early hepatocellular carcinoma. HEPATOL-OGY 2003;37:198-207.

20. Capurro M, Wanless IR, Sherman M, Deboer G, Shi W, Miyoshi E, et al.Glypican-3: a novel serum and histochemical marker for hepatocellularcarcinoma. Gastroenterology 2003;125:89-97.

21. Llovet JM, Chen Y, Wurmbach E, Roayaie S, Fiel MI, Schwartz M, et al.A molecular signature to discriminate dysplastic nodules from early hepa-tocellular carcinoma in HCV cirrhosis. Gastroenterology 2006;131:1758-1767.

22. International Working Party. Terminology of nodular hepatocellular le-sions. International Working Party. HEPATOLOGY 1995;22:983-993.

23. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeck-ert C, et al. Minimum information about a microarray experiment (MI-AME)-toward standards for microarray data. Nat Genet 2001;29:365-371.

24. Wurmbach E, Yuen T, Sealfon SC. Focused microarray analysis. Methods2003;31:306-316.

946 WURMBACH ET AL. HEPATOLOGY, April 2007

25. Albanis E, Friedman SL. Antifibrotic agents for liver disease. Am J Trans-plant 2006;6:12-19.

26. Bieche I, Asselah T, Laurendeau I, Vidaud D, Degot C, Paradis V, et al.Molecular profiling of early stage liver fibrosis in patients with chronichepatitis C virus infection. Virology 2005;332:130-144.

27. Patil MA, Chua MS, Pan KH, Lin R, Lih CJ, Cheung ST, et al. Anintegrated data analysis approach to characterize genes highly expressed inhepatocellular carcinoma. Oncogene 2005;24:3737-3747.

28. Ge X, Yamamoto S, Tsutsumi S, Midorikawa Y, Ihara S, Wang SM, et al.Interpreting expression profiles of cancers by genome-wide survey ofbreadth of expression in normal tissues. Genomics 2005;86:127-141.

29. Yang CW, Su JY, Tsou AP, Chau GY, Liu HL, Chen CH, et al. Integrativegenomics based identification of potential human hepatocarcinogenesis-associated cell cycle regulators: RHAMM as an example. Biochem BiophysRes Commun 2005;330:489-497.

30. Bruix J, Sherman M. Management of hepatocellular carcinoma. HEPATOL-OGY 2005;42:1208-1236.

31. Bruix J, Sherman M, Llovet JM, Beaugrand M, Lencioni R, BurroughsAK, et al. Clinical management of hepatocellular carcinoma. Conclusionsof the Barcelona-2000 EASL conference. European Association for theStudy of the Liver. J Hepatol 2001;35:421-430.

32. Paradis V, Bieche I, Dargere D, Laurendeau I, Laurent C, Bioulac Sage P,et al. Molecular profiling of hepatocellular carcinomas (HCC) using alarge-scale real-time RT-PCR approach: determination of a molecular di-agnostic index. Am J Pathol 2003;163:733-741.

33. Yotov WV, Hamel H, Rivard GE, Champagne MA, Russo PA, Leclerc JM,et al. Amplifications of DNA primase 1 (PRIM1) in human osteosarcoma.Genes Chromosomes Cancer 1999;26:62-69.

34. Kouprina N, Pavlicek A, Collins NK, Nakano M, Noskov VN, Ohzeki J, et al.The microcephaly ASPM gene is expressed in proliferating tissues and encodesfor a mitotic spindle protein. Hum Mol Genet 2005;14:2155-2165.

35. Hall CL, Yang B, Yang X, Zhang S, Turley M, Samuel S, et al. Overex-pression of the hyaluronan receptor RHAMM is transforming and is alsorequired for H-ras transformation. Cell 1995;82:19-26.

36. Roberts LR, Gores GJ. Hepatocellular carcinoma: molecular pathways andnew therapeutic targets. Semin Liver Dis 2005;25:212-225.

37. Laurent-Puig P, Legoix P, Bluteau O, Belghiti J, Franco D, Binot F, et al.Genetic alterations associated with hepatocellular carcinomas define dis-tinct pathways of hepatocarcinogenesis. Gastroenterology 2001;120:1763-1773.

38. Merle P, Kim M, Herrmann M, Gupte A, Lefrancois L, Califano S, et al.Oncogenic role of the frizzled-7/beta-catenin pathway in hepatocellularcarcinoma. J Hepatol 2005;43:854-862.

39. Calvisi DF, Ladu S, Gorden A, Farina M, Conner EA, Lee JS, et al.Ubiquitous activation of Ras and Jak/Stat pathways in human HCC. Gas-troenterology 2006;130:1117-1128.

40. Santagata S, Demichelis F, Riva A, Varambally S, Hofer MD, Kutok JL, etal. JAGGED1 expression is associated with prostate cancer metastasis andrecurrence. Cancer Res 2004;64:6854-6857.

41. Lin W, Choe WH, Hiasa Y, Kamegaya Y, Blackard JT, Schmidt EV, et al.Hepatitis C virus expression suppresses interferon signaling by degradingSTAT1. Gastroenterology 2005;128:1034-1041.

42. Fong CW, Chua MS, McKie AB, Ling SH, Mason V, Li R, et al.Sprouty 2, an inhibitor of mitogen-activated protein kinase signaling,is down-regulated in hepatocellular carcinoma. Cancer Res 2006;66:2048-2058.

43. Seidler DG, Goldoni S, Agnew C, Cardi C, Thakur ML, Owens RT, etal. Decorin protein core inhibits in vivo cancer growth and metabolismby hindering epidermal growth factor receptor function and triggeringapoptosis via caspase-3 activation. J Biol Chem 2006;281:26408-26418.

44. Wirths O, Waha A, Weggen S, Schirmacher P, Kuhne T, Goodyer CG, etal. Overexpression of human Dickkopf-1, an antagonist of wingless/WNTsignaling, in human hepatoblastomas and Wilms’ tumors. Lab Invest2003;83:429-434.

45. Sicklick JK, Li YX, Jayaraman A, Kannangai R, Qi Y, Vivekanandan P, etal. Dysregulation of the Hedgehog pathway in human hepatocarcinogen-esis. Carcinogenesis 2006;27:748-757.

46. Lin SY, Elledge SJ. Multiple tumor suppressor pathways negatively regu-late telomerase. Cell 2003;113:881-889.

47. Farazi PA, Glickman J, Jiang S, Yu A, Rudolph KL, DePinho RA. Differ-ential impact of telomere dysfunction on initiation and progression ofhepatocellular carcinoma. Cancer Res 2003;63:5021-5027.

48. Kienitz A, Vogel C, Morales I, Muller R, Bastians H. Partial downregu-lation of MAD1 causes spindle checkpoint inactivation and aneu-ploidy, but does not confer resistance towards taxol. Oncogene 2005;24:4301-4310.

49. Gramantieri L, Chieco P, Giovannini C, Lacchini M, Trere D, Grazi GL,et al. GADD45-alpha expression in cirrhosis and hepatocellular carci-noma: relationship with DNA repair and proliferation. Hum Pathol 2005;36:1154-1162.

50. Zhao M, Kim YT, Yoon BS, Kim SW, Kang MH, Kim SH, et al. Expres-sion profiling of cyclin B1 and D1 in cervical carcinoma. Exp Oncol2006;28:44-48.

HEPATOLOGY, Vol. 45, No. 4, 2007 WURMBACH ET AL. 947