clinical signiﬁcance and prognostic value of microrna...

Imaging, Diagnosis, Prognosis

Clinical Significance and Prognostic Value of microRNAExpression Signatures in Hepatocellular Carcinoma

Rongrong Wei1,6, Guo-Liang Huang4, Mei-Yin Zhang1, Bin-Kui Li2, Hui-Zhong Zhang3, Ming Shi2, Xiao-QianChen1, LongHuang1,Qing-MingZhou1,Wei-Hua Jia1, X.F. StevenZheng5, Yun-Fei Yuan2, andHui-YunWang1

AbstractPurpose:MicroRNAs (miRNAs) play important roles in the development and progression of cancer. The

aim of this study is to identify miRNA expression signatures in hepatocellular carcinoma and delineate their

clinical significance for hepatocellular carcinoma.

ExperimentalDesign:Patients with hepatocellular carcinoma, undergoing hepatectomywere randomly

divided into training set (60 patients) and test set (50 patients). Other 56 patients were used as an

independent cohort. ThemiRNA expression levels were detected bymicroarray and verified by quantitative

real-time reverse transcription-PCR (qRT-PCR).

Results: A 30-miRNA signature consisting of 10 downregulated and 20 upregulated miRNAs was

established for distinguishing hepatocellular carcinoma from noncancerous liver tissues in the training

set with 99.2% accuracy. The classification accuracies of this signature were 97% and 90% in the test set and

independent cohort, respectively. The expression level of four miRNAs in the 30-miRNA signature was

verified by qRT-PCR in the training set. TwentymiRNAswere then selected to construct prognostic signature

in the training set. Of the 20miRNAs, sixwere risk factors and 14were protective factors. A formula based on

the 20 miRNAs was built to compute prognostic index. Kaplan–Meier analysis showed that patients with a

higher prognostic index had a significantly lower survival than those with a low index. This was verified in

the test and independent sets. Multivariate analysis indicated that the 20-miRNA signature was an

independent prognostic predictor.

Conclusions: The 30- and 20-miRNA signatures identified in this study should provide new molecular

approaches for diagnosis and prognosis of patients with hepatocellular carcinoma and clues for elucidating

molecular mechanism of hepatocarcinogenesis. Clin Cancer Res; 19(17); 4780–91. �2013 AACR.

IntroductionMicroRNA (miRNA) is a small noncoding RNA of about

22-nt that plays important roles in posttranscriptional generegulation (1). By base-pairing with the 30 untranslatedregions of target mRNAs, miRNA is capable of degradingtarget mRNA or downregulating mRNA translation (2).

Since the discovery of the first miRNA lin-4 in 1993 (3,4), thousands of miRNAs have been identified in human,animals, plants, and viruses bymolecular cloning, sequenc-ing, and computational approaches (5, 6).miRNAs are nowwidely recognized to play important roles in the control ofdevelopmental and physiologic processes, especially indevelopmental timing, cell death, cell proliferation, hema-topoiesis, and patterning of the nervous system (7). Duringcancer development and progression, miRNA can functionas tumor suppressor or oncogene (8–11). As such, they areoften referred to as Oncomirs (11).

Many studies indicate that miRNA expression signaturesor profiles can serve as diagnosis and prognosis predictorsfor various tumors (12–15). miRNA profiles have beenshown to be more accurate for classification of cancerscompared with mRNA profiles (14). Several studies onmiRNA expression profiles in hepatocellular carcinomasrevealed the extensive correlations between miRNA expres-sion and hepatocellular carcinoma development, progres-sion, and therapy (16, 17).However, only a limited numberof miRNA species were analyzed in these studies due to thesmall number of known miRNA available at that time.

In this study, we analyzed the miRNA expression profilesin 166 patients with hepatocellular carcinoma from South

Authors' Affiliations: 1State Key Laboratory of Oncology in South China;Departments of 2Hepatobiliary Oncology, and 3Pathology, Sun Yat-SenUniversity Cancer Center, Guangzhou; 4Sino-American Cancer ResearchInstitute, Guangdong Medical College, Dongguan, China; 5Department ofPharmacology/The Cancer Institute of New Jersey, UMDNJ-Robert WoodJohnson Medical School, Piscataway, New Jersey; and 6Present address,Department of Medicinal Chemistry andMolecular Pharmacology, PurdueUniversity, West Lafayette, Indiana

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

R. Wei and G.-L. Huang contributed equally to this work.

Corresponding Authors: Hui-Yun Wang, Sun Yat-Sen University CancerCenter, 651 Dongfeng East Road, West Building, Rm 704, Guangzhou510060, China. Phone: 86-20-8734-3308; Fax: 86-20-8734-3170; E-mail:[email protected]; and Yun-Fei Yuan, E-mail:[email protected]

doi: 10.1158/1078-0432.CCR-12-2728

�2013 American Association for Cancer Research.

ClinicalCancer

Research

Clin Cancer Res; 19(17) September 1, 20134780

on July 2, 2018. © 2013 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst June 28, 2013; DOI: 10.1158/1078-0432.CCR-12-2728

http://clincancerres.aacrjournals.org/

China using a custom microarray consisting of probes for683miRNAs, which is much larger than any of the previousstudies. As a result, we identified a 30-miRNA signature forclassifying hepatocellular carcinoma and noncancerousliver and a 20-miRNA signature for predicting hepatocel-lular carcinoma survival in a training set, and validatedthem in a test set and an independent cohort.

Materials and MethodsPatients and samplesPaired hepatocellular carcinomas and matched noncan-

cerous liver were collected from 110 patients undergoingresection of hepatocellular carcinoma between 2004 and2007 in SunYat-SenUniversity CancerCenter (Guangzhou,PR China). The patients were randomly assigned into atraining set (n ¼ 60) and test set (n ¼ 50). Another 56patients subjected to hepatectomy during 2001 to 2003 inthe same center were used as the independent set. Thehepatocellular carcinoma tissues were pathologically con-firmed by 2 pathologists. All of patients received a radicalhepatectomy and no distance metastasis was found beforesurgery. The clinical characteristics of all patients in the 3sets were summarized in Table 1. The follow-up timesranged from 1 to 58 months with a median time of 39months for 110 patients and from 1 to 81 months with amedian time of 28months for 56 patients, respectively. Theoverall survival (OS) time was calculated from the date ofsurgery to the date of death and disease-free survival (DFS)time from the date of surgery to the first relapse or distantmetastasis or death. The data of patient survival was pre-sented in Supplementary Table S1.

Both hepatocellular carcinoma and corresponding adja-cent noncancerous liver (more than 2 cm away from thehepatocellular carcinoma)were sampled. Fresh tissues wereimmediately immersed in RNAlater (Ambion, Inc.) aftersurgical resection, stored at 4�C overnight, and then frozenat �80�C until use. Total RNA was extracted using theTRIzol reagent (Invitrogen) according to themanufacturer’sinstructions.

This study was reviewed and approved by the Committeefor the Conduct of Human Research of the Sun Yat-SenUniversity Cancer Center. Written informed consent wasobtained from each patient with hepatocellular carcinoma.None of the patients had received any other therapies suchas chemoembolization or chemotherapy before surgery.

miRNA microarray fabrication, RNA labeling, andhybridization

Microarray fabrication was carried out as described byWang and colleagues (18) and probe design and RNAlabeling were conducted according to published protocolswith minor modifications (19). Briefly, all of the humanmiRNAs (release 12) in the miRBase (20) were used todesign the probes. With the principles described by Wangand colleagues (19), 683 probes for miRNAs were success-fully designed for the microarray. For fabrication of micro-array, the probes (40 mmol/L final concentration) mixedwith printing buffer were printed onto slides in duplicateusing SmartArrayer 136 printer (CapitalBio Inc). The qual-ity of microarray was tested using 2 control RNA samples todo hybridization for every batch of microarrays and thecorrelation among the different microarray batches wasanalyzed. The correlation analysis showed that the corre-lation coefficient was more than 95%, indicating that thedifferences between the batches of microarrays were in theacceptable range. During the labeling reaction, 2.5 mg oftotal RNAwas usedwith 100nmol/L of pCp-DY647or pCp-DY547 (Dharmacon) and 15 units of T4 RNA ligase (USB)in a total reaction volume of 20 mL at 16�C overnight.Labeled RNAs from paired hepatocellular carcinoma andnoncancerous liver were mixed and hybridized to the arraywith a 2� hybridization solution (final concentration: 5�Denhart’s solution, 0.5% SDS, and 5 � SSC) in a Hybrid-ization Chamber (Corning Inc.) at 46�C for 12 to 16 hours.After washing, microarray was scanned with a LuxScan 10 KMicroarray Scanner (CapitalBio Inc.) at PMT 800 to 900.Scanned images were gridded using the GenPix Pro 6.0software (Axon Instruments).

Real-time quantitative reverse transcription-PCRThe reverse transcription (RT)was conductedwith 2 mg of

total RNA isolated from hepatocellular carcinoma or corre-sponding noncancerous liver tissues, 5 nmol/L of Bulge-LoopmiRNA RT specific primers (RiboBio Co.), 0.2 mmol/L dNTP, 40 U RNase inhibitor, 200 U M-MLV reversetranscriptase (Promega) in a 50 mL volume at 42�C for 60minutes. The quantitative PCR reaction was carried out in a20 mL volume with 2 mL of RT products, 2 mL of PlatinumSYBR Green qPCR SuperMix-UDG reagents (Invitrogen,

Translational RelevanceAlterations inmicroRNA (miRNA) expression are very

common in various human cancers and have shownsignificant potential role in cancer management. In thisstudy,miRNAexpression signatures have been identifiedfor hepatocellular carcinoma classification and progno-sis. A 30-miRNA signature was developed to discrimi-nate hepatocellular carcinomas from correspondingnoncancerous liver tissues in the training set. A 20-miRNA signature, in which each miRNA was foundsignificantly associated with the survival of patient withhepatocellular carcinoma, was established for predictingsurvival in the training set. ThesemiRNA signatures werevalidated in the test set and independent set. We foundthat the 20-miRNA signature is a significant independentsurvival predictor. This provides a potentially valuableapproach for evaluating the prognosis of patient withhepatocellular carcinoma and helping clinicians todesign appropriate treatment plans. The 20- and 30-miRNA signatures, in which most of miRNA have notbeen reported in hepatocellular carcinoma, may alsolead to novel miRNA-targeted hepatocellular carcinomatherapies.

Clinical Significance of miRNA Signature for Hepatocellular Carcinoma

www.aacrjournals.org Clin Cancer Res; 19(17) September 1, 2013 4781




USA), and 500 nmol/L each of Bulge-LoopmiRNA forwardspecific primer and universal reverse primer in an AppliedBiosystems PRISM 7900HT instrument. The PCR reactioncycle was as follows: 95�C for 2 minutes, followed by 40cycles of 95�C for 15 seconds and60�C for 30 seconds, and adissociation stage. U6 RNA was detected by qRT-PCR as aninternal control.

Data process and statistical analysesMicroarray raw data preprocessing included local back-

ground subtraction, averaging of intensities of duplicatedprobes, quantile normalization across multiple arrays, log2 transformation of expression levels. Only the miRNAswith expression in more than 15% of samples were sub-jected to further statistical analysis. This normalized micro-array data have been deposited into the Gene ExpressionOmnibus Public Database at the National Center forBiotechnology Information, and the accession number isGSE31384.

Student t test and significance analysis of microarray(SAM) were used to analyze the differential expression ofmiRNA detected by microarray. Hierarchical clusteringanalysis (HCL) was conducted using MultiExperimentViewer version 4.2 (21). Prediction analysis of microarrays(PAM; ref. 22) was used to evaluate the prediction capacityof miRNA signature to classify hepatocellular carcinomaand noncancerous liver samples.

2�DDCt was used to represent expression changes ofmiRNA between hepatocellular carcinoma and matchednoncancerous liver detected by qRT-PCR. Paired Studentt test was conducted to analyze the differential miRNAexpression levels between tumors and noncancerous livertissues. The survival risk prediction of patients was donewith BRB ArrayTools v.3.7.1 (http://linus.nci.nih.gov/BRB-ArrayTools.html). With GraphPad Prism 5 software(www.graphpad.com), the log-rank test and Kaplan–Meier survival analysis were used to compare the survivalsof patients in different risk groups. Cox proportionalhazards regression was used to evaluate the HR of miRNAsignature and clinical variables for patient survival. Allother statistical analyses were conducted using the SPSS16.0 (SPSS Inc.).

Table 1. Clinical characteristics of patients withhepatocellular carcinoma in the training, test,and independent sets

CharacteristicTrainingset, n (%)

Test set,n (%)

Independentset, n (%) Pa

Age, y 0.232�50 22 (37) 20 (40) 29 (52)<50 38 (63) 30 (60) 27 (48)

Gender 0.381Male 48 (80) 43 (86) 50 (89)Female 12 (20) 7 (14) 6 (11)

Pathologygrade

0.007

I 6 (10) 4 (8) 13 (23)II 32 (53) 39 (78) 32 (57)III 22 (37) 7 (14) 11 (20)

HBV-DNA 0.8Positive 25 (42) 23 (46) 22 (39)Negative 35 (58) 27 (54) 34 (61)

HBsAg 0.073Positive 48 (80) 46 (92) 52 (93)Negative 12 (20) 4 (8) 4 (7)

HBeAg 0.101Positive 7 (12) 8 (16) 15 (27)Negative 53 (88) 42 (84) 41 (73)

AFP (mg/L) 0.217�400 30 (50) 25 (50) 20 (36)<400 30 (50) 25 (50) 36 (64)

Cirrhosis 0.003Yes 47 (78) 38 (76) 54 (96)No 13 (22) 12 (24) 2 (4)

Cancerembolus

0.512

Yes 12 (20) 6 (12) 8 (14)No 48 (80) 44 (88) 48 (86)

Tumornumber

0.714

�2 15 (25) 16 (32) 17 (30)<1 45 (75) 34 (68) 39 (70)

Main size�5 cm 46 (77) 39 (78) 43 (77) 0.984<5 cm 14 (23) 11 (22) 13 (23)

Tumorencapsulation

0.235

None 21 (35) 11 (22) 11 (20)Incomplete 18 (30) 21 (42) 18 (32)Complete 21 (35) 18 (36) 27 (48)

TNM <0.001I 17 (28) 13 (26) 30 (54)II 23 (38) 20 (40) 5 (9)III 20 (33) 17 (34) 21 (37)

BCLC Stage 0.6480–A 36 (60) 31 (62) 33 (59)B 12 (20) 13 (26) 16 (29)C 12 (20) 6 (12) 7 (12)

Postsurgicalmetastasis

0.787

Yes 8 (13) 6 (12) 5 (9)

(Continued on the following column)

Table 1. Clinical characteristics of patients withhepatocellular carcinoma in the training, test,and independent sets (Cont'd )

CharacteristicTrainingset, n (%)

Test set,n (%)

Independentset, n (%) Pa

No 52 (87) 44 (88) 51 (91)Relapse 0.117Yes 20 (33) 15 (30) 27 (48)No 40 (67) 35 (70) 29 (52)

Abbreviations: AFP, alpha-fetoprotein; HBsAg, hepatitis Bsurface antigen.aPearson c2 test.

Wei et al.

Clin Cancer Res; 19(17) September 1, 2013 Clinical Cancer Research4782




ResultsIdentification of a 30-miRNA signature to discriminatehepatocellular carcinoma from correspondingnoncancerous liver tissuesmiRNA expression profiles of 110 pairs of hepatocellular

carcinomas and noncancerous liver tissues in the trainingand test sets were investigated using a custom miRNAmicroarray. In the training set, SAManalysis [false discoveryrate (FDR) was set to 0] revealed that there were 334miRNAs with differential expressions between hepatocel-lular carcinoma and noncancerous liver tissue. Of these

miRNAs, 174 were upregulated and 160 were downregu-lated in hepatocellular carcinoma. To identify a signature todistinguish between hepatocellular carcinoma and noncan-cerous liver,miRNAswithhighest fold changes and smallestP values were selected from the 334 miRNAs in the trainingset using SAM program (FDR ¼ 0) and paired Student ttest. A 30-miRNA signature was developed by class predic-tion and clustering, which reached the maximum correctclassification rate (99.2%) for hepatocellular carcinomaand noncancerous liver (Fig. 1A). Out of the 30 miRNAs(Table 2), 10 were downregulated and 20 upregulated in

Figure 1. Hierarchical clustering ofhepatocellular carcinoma samplesin the training and test sets. A,hierarchical clustering of 60hepatocellular carcinoma samples(blue bars at the top left) and 60matched nontumor livers (yellowbars at the top right) in the trainingset by the 30-miRNA signature;one hepatocellular carcinoma wasmisclassified into nontumor groupand 2 nontumor liver intohepatocellular carcinoma group;each row represents theexpression level of an individualmiRNA and each columnrepresents an individual tissuesample. Pseudo-colors indicateexpression levels from low to high(green to red). The color scale atbottom denotes the geneexpression levels from �3 to 3 inlog base 2 units. B, hierarchicalclustering of 50 hepatocellularcarcinoma samples (blue bars) and50 matched nontumor livers(yellow bars) in the test set by thesame signature.






hepatocellular carcinoma. The effectiveness of the 30-miRNA signature was verified in 50 paired hepatocellularcarcinoma and noncancerous liver samples of the test set(Fig. 1B) and 56 paired samples of the independent cohort(Supplementary Fig. S1A). To further test the effectivenessof the 30-miRNA signature, PAM analysis was conducted inthe test set and the independent cohort (Supplementary Fig.S1B andS1C). ThePredictedClasses andPredictedPosteriorProbabilities were listed in Supplementary Table S2. In thetest set, the accuracy, sensitivity, and specificity for distin-guishing between hepatocellular carcinoma and noncan-cerous liver were 97%, 100%, and 94%, respectively; in theindependent cohort, those were 90%, 94%, and 88%,respectively. This result suggested that the 30-miRNA sig-nature for distinguishing between hepatocellular carcino-ma and noncancerous liver was robust.

In addition, we analyzed the relationship of miRNAexpressionwith cirrhosis, pathologic grade, and clinical stagein whole cohort and found some differential expression of

miRNAs between these characteristics using the criteria offold change > 1.5 and P < 0.05 (Supplementary Table S3).

qRT-PCR validation of miRNA expression levelsdetected by microarray

To validate the miRNA expression level detected bymicroarray, we carried out qRT-PCR for 2 upregulatedmiRNAs (mir-17 and mir-21) and 2 downregulated ones(mir-269-5p and mir-625�) in 60 paired hepatocellularcarcinoma and noncancerous liver samples from the train-ing set. In the qRT-PCR assay, U6 gene was used as anendogenous control. The results showed that the expressionlevels of the four miRNAs detected by qRT-PCR were sig-nificantly different between hepatocellular carcinomas andnoncancerous liver tissues as indicated by paired student ttest (all P < 0.05, Fig. 2A), and strongly correlated with themicroarray data (Fig. 2B). These results show that miRNAlevels detected bymicroarray are reliable and canbeused forthe further analysis.

Table 2. Summary of 30 miRNAs for distinguishing between hepatocellular carcinoma and noncancerousliver in the training set

No. miRNA

Mean intensity inhepatocellularcarcinoma

Mean intensityin noncancerousliver

Hepatocellularcarcinoma/noncancerousliver (ratio)

Expression level inhepatocellularcarcinoma

1 hsa-miR-625� 1,464 4,513 0.32 Down2 hsa-miR-296-5p 2,274 6,223 0.37 Down3 hsa-miR-634 825 2,048 0.4 Down4 hsa-miR-451 1,400 3,411 0.41 Down5 hsa-miR-29b-1� 479 1,124 0.43 Down6 hsa-miR-766 783 1,647 0.48 Down7 hsa-miR-486-5p 860 1,775 0.48 Down8 hsa-miR-940 1,077 2,118 0.51 Down9 hsa-miR-223 849 1,497 0.57 Down10 hsa-miR-135b 578 993 0.58 Down11 hsa-miR-21 10,254 3,842 2.67 Up12 hsa-miR-20a 2,410 1,117 2.16 Up13 hsa-miR-612 1,819 924 1.97 Up14 hsa-miR-17 2,600 1,328 1.96 Up15 hsa-miR-933 3,207 1,649 1.94 Up16 hsa-miR-298 1,411 735 1.92 Up17 hsa-miR-221 1,374 777 1.77 Up18 hsa-miR-106b 1,550 878 1.77 Up19 hsa-miR-18a 1,177 670 1.76 Up20 hsa-miR-516a-5p 879 501 1.76 Up21 hsa-miR-210 1,110 635 1.75 Up22 hsa-miR-93 2,204 1,264 1.74 Up23 hsa-miR-130b 875 514 1.7 Up24 hsa-miR-20b 1,719 1,042 1.65 Up25 hsa-miR-675 2,218 1,362 1.63 Up26 hsa-miR-320a 2,333 1,445 1.61 Up27 hsa-miR-23a 2,196 1,386 1.58 Up28 hsa-miR-19a 1,442 912 1.58 Up29 hsa-miR-103 2,655 1,720 1.54 Up30 hsa-let-7i 1,335 887 1.51 Up

Wei et al.





Identification of 20 miRNAs associated with survival inthe training setThe microarray raw data in the training set were normal-

ized and transformed into a log 2 ratio of hepatocellularcarcinoma to noncancerous liver by BRB-ArrayTools andthen miRNAs with expression in less than 15% of samplesand change in expression less than 1.5-fold were filtered(23, 24). To identify a miRNA signature correlated withpatients’ survival, the relationship between miRNA expres-sion levels and patients’ survival was assessed in the trainingset by fitting Cox proportional hazards models using BRB-ArrayTools software andmiRNAswere chosenby the criteriaof P < 0.1. Then different combinations of miRNAs weretested for predicting hepatocellular carcinoma survival. Inthis way, a best combination (20 miRNA) was selected toachieve the best prediction of survival of patients withhepatocellular carcinoma. A simple formula for computingprognostic index was established from the 20 miRNAs:P

i wixi�0:375, where wi and xi are the weight and logged

gene expression level for the i-th gene, and the weight ofeach miRNA is shown in Table 3. Of the 20 miRNAs, 6miRNAs were risk factors that were defined as those withhazard ratio (HR) for deathmore thanone, and the other 14miRNAs were protective factors that were defined as thosewith HR for death less than one (Table 3). With thisformula, each patient had a prognostic index and themedian of prognostic index for all patients in training setwas �0.090. Patients were assigned to a high-risk group iftheir prognostic index was more than �0.090 or a low-riskgroup if their prognostic index was equal to or less than�0.090.

Correlation between the 20-miRNA signature andpatient survival in the training set

Using the 50th percentile of the 20-miRNA prognosticindex as a cut-off point (�0.090), 60 patients in the train-ing set were separated into a high-risk (high index) sub-group or a low-risk (low index) subgroup according to their

Figure 2. The expression levels of 4miRNAs detected with microarraywere verified by qRT-PCR. A, thesignificant differentially expressedgenes, 2 (miR-17, miR-21)upregulated and another 2(miR-296-5p and miR-625�)downregulated in hepatocellularcarcinoma detected bymicroarray,were validated by qRT-PCR in 60hepatocellular carcinomas (T) and60 matched nontumor livers (N) ofthe training set. The relativeexpression level was normalized toU6 and data are presented as themean� SEM. B, comparison of theexpression levels of 4 miRNAsmeasured by microarray and qRT-PCR. The ratios (hepatocellularcarcinoma to nontumor) of miRNAexpression levels of miR-17, miR-21, miR-296-5p, and miR-625� bymicroarray were stronglyconcordant with those by qRT-PCR in the training set.






prognostic index. There were 30 patients in high-risk groupwith a 3-year survival rate of 35.3% and 30 in the low-riskgroup with a 3-year survival rate of 92.6%. Kaplan–Meiersurvival analysis of the patients in the two subgroupsrevealed that both OS rate and DFS rate in the high-riskgroup were significantly lower than those in the low-riskgroup (P < 0.001 and P < 0.001, respectively, see Fig. 3A).

Validation of the 20-miRNA signatures for survivalprediction in the test set

To validate the 20-miRNA signature for predicting sur-vival of patients with hepatocellular carcinoma, 50 patientsin the test set were classified into a high-risk subgroup and alow-risk subgroup on the basis of the same Cox propor-tional hazards model and cut-off point, which wereobtained from the training set. Kaplan–Meier survival anal-ysis on thepatientswithhigh-risk (n¼23) and low-risk (n¼27) also was conducted as described for the training set. Asexpected, the OS and DFS rate of the patients with high-riskwere markedly poorer than those of patients with low-risk(P ¼ 0.033, P ¼ 0.039, respectively, Fig. 3B).

The 20-miRNA signature for survival prediction wasfurther verified in an independent cohort

Furthermore, to validate the 20-miRNA signatures asso-ciation with hepatocellular carcinoma survival, another 56patients with hepatocellular carcinoma received the hepa-tectomy in the samehospital during 2001 to 2003wereusedas an independent cohort and detected by the miRNA

microarray. Data processing was conducted as describedabove. The 56 patients were split into a high-risk subgroup(n¼ 29) and a low-risk subgroup (n¼ 27) according to thesame Cox model and cut-off point. Kaplan–Meier survivalanalysis showed similar results to those in the training setand test set. The OS and DFS of high-risk group weresignificantly worse than those of the low-risk group (P ¼0.033 and P ¼ 0.040, respectively, Fig. 3C).

To directly show the relationship between the 20-miRNAprognostic index and survival, we plotted the prognosticindex and survival status of all patients in training and testsets (Supplementary Fig. S2). In this plot, patients with low-risk scores had much less deaths than those with high-riskscore. Likewise, similar results were found in the trainingset, test set, and independent cohort (Supplementary Fig.S3A–S3C, respectively). All of the analytic data pertaining tosurvival indicate that the 20-miRNA signature is robustlycorrelated with hepatocellular carcinoma patient survival.

Univariate and multivariate Cox regression analysis ofthe 20-miRNA signature and clinical variables

To further verify whether the signature was an indepen-dent prognostic factor, the signature and clinical variables inall of 166 patients with hepatocellular carcinoma wereanalyzed by Cox regressionmodel. First, the univariate Coxregression revealed that the 20-miRNA signature and 7clinical variables were significant predictors. Then, themultivariate Cox regression showed that the 20-miRNAsignature (P < 0.001) and hepatitis B e Antigen (HBeAg;

Table 3. miRNAs associated with overall survival of patients with hepatocellular carcinoma in the trainingset

No. miRNA Weight HR (95% CI) Pa Putative function

1 hsa-miR-708 0.13 7.56 (1.52–37.57) 0.013 High-risk2 hsa-miR-34c-3p 0.08 5.21 (1.67–16.34) 0.005 High-risk3 hsa-miR-584 0.03 4.31 (1.29–14.40) 0.018 High-risk4 hsa-miR-431� 0.15 3.84 (1.97–7.46) <0.001 High-risk5 hsa-miR-744 0.12 1.74 (1.01–2.98) 0.045 High-risk6 hsa-miR-141 0.05 1.42 (1.10–1.83) 0.007 High-risk7 hsa-let-7d �0.24 0.28 (0.10–0.83) 0.022 Protective8 hsa-miR-15a �0.24 0.25 (0.08–0.80) 0.02 Protective9 hsa-miR-142-3p �0.17 0.23 (0.05–1.00) 0.051 Protective10 hsa-miR-10b �0.13 0.21 (0.04–1.03) 0.054 Protective11 hsa-let-7e �0.25 0.19 (0.05–0.65) 0.008 Protective12 hsa-miR-28-3p �0.14 0.16 (0.03–0.80) 0.026 Protective13 hsa-miR-193b �0.15 0.16 (0.03–0.82) 0.028 Protective14 hsa-miR-101 �0.22 0.16 (0.03–0.92) 0.04 Protective15 hsa-miR-451 �0.21 0.15 (0.02–1.16) 0.068 Protective16 hsa-miR-142-5p �0.16 0.13 (0.02–0.96) 0.045 Protective17 hsa-miR-26b �0.24 0.11 (0.02–0.62) 0.013 Protective18 hsa-miR-497 �0.21 0.1 (0.02–0.56) 0.009 Protective19 hsa-miR-29c �0.36 0.09 (0.02–0.51) 0.007 Protective20 hsa-miR-140-3p �0.17 0.07 (0.01–0.37) 0.002 Protective

aCox proportional hazard regression analysis.

Wei et al.





P ¼ 0.008) were independent prognostic factors (Table 4).Cox regression analysis on the score of the 20-miRNAsignature indicated that the patients with high-risk had asignificant higher HR for tumor-related death (HR, 2.75)

compared with those with low-risk. In addition, we con-ducted univariate and multivariate Cox regression analysison thepatients in the3 sets separately (Supplementary TableS4) and combination of 2 sets (Supplementary Table S5).

Figure 3. Kaplan–Meier curve analysis of (OS) and relapse-free survival (OFS) in patients with hepatocellular carcinoma with high- or low-risk according to the20-miRNA signature score. A, OS andOFS of patients with high- or low-risk score in the training set. B, OS andOFS of patients with high- or low-risk score inthe testing data set. C, OS, and OFS of patients with high- or low-risk score in the independent cohort.






The resultswere generally consistentwith those in thewholecohort.

DiscussionIn this study, we fabricated a custommicroarray contain-

ing 683miRNA probes to detect miRNA profiles in patientswith hepatocellular carcinoma from Southern China.Although there had been some reported miRNA-profilingstudies on hepatocellular carcinoma, the total number ofhuman miRNA species in those studies was very limited(about 300 at most; refs. 25, 26). This is the first study inwhich a microarray consisting of 683 miRNAs has beenused in profiling 166 hepatocellular carcinomas. With thismicroarray, a 30-miRNA signature for discriminating hepa-tocellular carcinomas fromnoncancerous liver samples wasestablished in the training set and validated in the test andindependent set. This result shows that the signature iden-tified from the training set is highly reproducible in the testand independent sets despite some heterogeneity in thelatter set.

Reviewing the articles on miRNA profiling in hepatocel-lular carcinoma, in 2006, Murakami and colleagues con-ducted the first miRNA expression profiling study in 25Japanese patients with hepatocellular carcinoma with a

microarray containing probes corresponding to 180maturemiRNAs and 206 precursormiRNAs (27). They identified 8-miRNA signature (7 mature miRNAs and 1 precursormiRNA) that could distinguish hepatocellular carcinomafromnontumor liverwith a97.8%(45/46)of accuracy (27).In this 8-miRNA signature, only one miRNA (miR-18) ispresent in our 30-miRNA signature. Another Japanese studyreported that 23 miRNAs were differentially expressedbetween 26 pairs of hepatocellular carcinoma and livertissues with chronic hepatitis B or C, which represents70.8% of accuracy for distinguishing hepatocellular carci-noma from the corresponding chronic hepatitis B (n¼ 12)and 82.1% for hepatocellular carcinoma from chronichepatitis C (n ¼ 14; ref. 26). Of the 23 miRNAs, only 3miRNAs (miR-223, miR-21, and miR-221) are present inour 30-miRNA signature (26). Possible reasons for themiRNA discrepancy between our signature and Japanesegroup’s signatures include different number of miRNAs(683 versus 180 and 188) in the different microarrays, thedifferent pathogens [hepatitis B virus (HBV) vs. HBV andhepatitis C virus (HCV)] and/or genetic variations betweenJapanese and Chinese patients.

In 2008, Jiang and colleagues revealed 16 miRNAs dif-ferentially expressed between 28 pairs of hepatocellular

Table 4. Univariate and multivariate analysis of features associated with overall survivala

Clinical features HR (95% CIb) P

Univariate Cox regression analysis20-miRNA signature 3.56 (2.15–5.87) <0.001Age 0.85 (0.53–1.35) 0.494Gender (M vs. F) 1.2 (0.60–2.41) 0.611Grade (I, II, III) 1.5 (1.02–2.21) 0.038HBV-DNA (positive vs negative) 1.46 (0.92–2.32) 0.11HBsAg (positive vs. negative) 1.33 (0.64–2.77) 0.453HBeAg (positive vs. negative) 2.03 (1.19–3.48) 0.01AFP (�400 mg/L vs. < 400 mg/L) 1.38 (0.87–2.18) 0.175Cirrhosis (yes vs. no) 2.07 (0.95–4.51) 0.068Cancer Embolus (yes vs. no) 2.43 (1.44–4.11) 0.001Tumor Number (1 vs. � 2) 2.26 (1.41–3.64) 0.001Main size (�5 cm vs. < 5 cm) 2.92 (1.45–5.87) 0.003Tumor encapsulation (complete, incomplete, none) 0.64 (0.48–0.85) 0.002TNM stage (I, II, III) 1.87 (1.41–2.50) <0.001Multivariate Cox regression analysis20-miRNA signature (high score vs. low score) 2.75 (1.58–4.79) <0.001Grade (I, II, III) 1.03 (0.66–1.61) 0.887HBeAg (positive vs. negative) 2.3 (1.29–4.10) 0.005Cancer embolus (yes vs. no) 1.74 (0.96–3.13) 0.067Tumor Number (1 vs. � 2) 1.39 (0.76–2.53) 0.281Main size (�5 cm vs. < 5 cm) 1.93 (0.93–4.02) 0.079Tumor encapsulation (complete, incomplete, none) 0.94 (0.67–1.31) 0.71TNM stage (I, II, III) 1.43 (0.96–2.14) 0.08

Abbreviations: AFP, alpha-fetoprotein; TNM, tumor-node-metastasis.aAnalysis was conducted on the entire cohort (n ¼ 166).b95% CI.

Wei et al.





carcinoma and adjacent benign tissues from the UnitedStates using qRT-PCR for 196 mature miRNAs (28). Withthe 16-miRNA signature for distinguishing hepatocellularcarcinoma from noncancerous liver, the correct classifica-tion rate was 96.4% (54/56). Out of the 16 miRNAs, 5miRNAswere present in our 30miRNAs. In the same year, Liand colleagues found that 69 miRNAs (5 of them werepredicted but later proven to be false miRNAs) showedsignificantly different expression in 78 pairs of hepatocel-lular carcinoma and corresponding noncancerous livertissues from Eastern China and 89.7% of the samples couldbe correctly classified by the 69-miRNA signature (29).Interestingly, 11 miRNAs (hsa-let-7i, hsa-miR-103, hsa-miR-106b, hsa-miR-130b, hsa-miR-18a, hsa-miR-20a,hsa-miR-20b, hsa-miR-210, hsa-miR-221, hsa-miR-451,and hsa-miR-93) are shared between Li’s 69-miRNA signa-ture and our 30-miRNA signature. Of the 11 miRNAs, 10were upregulated and only one (miR-451) was downregu-lated in hepatocellular carcinoma in the 2 studies. Thisresult provides an independent validation of our result.With regard to the correct classification rate, our 30-miRNAsignature was 99.2% accuracy for distinguishing hepatocel-lular carcinoma from noncancerous liver in training set,which was verified in the test and independent sets. Theaccuracy of our signature was higher than all previouslyreported miRNA signatures.In addition, there are 11 unique miRNAs (miR-296-1,

miR-486-5p, miR-516a-5p, miR-612, miR-625�, miR-634,miR-675, miR-766, miR-933, and miR-940) in our 30-miRNA signature, which have not been reported in hepa-tocellular carcinoma by others because many of them wereonly recently identified and made available in the publicdatabase and the number of miRNAs included in our studyare much more than those in previous studies. The studieson miRNA differential expression between hepatocellularcarcinoma and noncancerous liver suggested that miRNAexpression profile might be a potential diagnostic tool inhepatocellular carcinoma clinical practice and provides aclue for exploring molecular mechanism of hepatocarcino-genesis and discovering new molecular targets for hepato-cellular carcinoma therapy.With the custommicroarray,we also identify a20-miRNA

signature that is significantly associated with OS andDFS in60 patients of the training set. Each of the 20 miRNAs wasalso significantly associated with hepatocellular carcinomasurvival by Cox model. This 20-miRNA signature was val-idated in 50 patients of the test set and 56 patients of theindependent set. These results indicate that the signature isrobust and reliable. The survival-related 20-miRNA signa-ture should provide a valuable approach to help cliniciansmake better informed decision on patient with hepatocel-lular carcinoma management.Compared with a survival-related 19-miRNA signature

detected in 25 U.S. patients with hepatocellular carcinomaby qRT-PCR with 196 miRNAs reported by Jiang andcolleagues in 2008 (28), our 20-miRNA signature sharedno common miRNA with the 19-signature. A potentialreason is that the hepatocarcinogenesis process of U.S.

patient is very different from that of Chinese patient, whichmay reflect the fact that majority of US patients withhepatocellular carcinoma carry HCV, whereas Chinesepatients are mainly infected with HBV. Another possibleexplanation for the distinct discrepancy is that the numberofmiRNAs in our studywas nearly 4 timesmore than that inthe study conducted by Jiang and colleagues. In yet anotherhepatocellular carcinoma miRNA profiling study using amicroarray containing about 225 human miRNA probes,Budhu and colleagues identified a metastasis-related 20-miRNA signature (30). In comparison with the miRNAs inour survival 20-miRNA signature, only one miRNA (has-miR-15a) is common even though the metastasis-relatedsignature was also reported to be associated with outcomeof hepatocellular carcinoma. Six miRNAs of our 20-miRNAsignature were identified recently and deposited into themiRBase database and not included in microarray con-ducted by Budhu and colleagues. Thus, our 20-miRNAsignature seems to be more representative and comprehen-sive than other signatures.

In univariate Cox analysis, our 20-miRNA signature was asignificant predictor in the training, test, and independentsets (Supplementary Table S4), respectively. In multivariateCox analysis, the 20-miRNA signature was a significantindependent predictor in training set and a marginallysignificant independent predictor in test set, but not anindependent predictor in independent cohort (Supplemen-tary Table S4). However, when combined with the test setand independent cohort, the signature was a significantindependent predictor withmultivariate Cox analysis (Sup-plementary Table S5). These results suggest that this signa-ture should be also an independent predictor in the test setor independent cohort if the sample size is increased.

In the present study, HBeAg was shown to be an inde-pendent predictor for survival of patients with hepatocel-lular carcinoma, which is not often reported in the pub-lished literature. HBV infection is a major cause of hepato-cellular carcinoma in some areas, especially in China, andHBeAg is a marker of active HBV replication and infection,which aggravates the impaired liver function of patientswith hepatocellular carcinoma. Liver function status is animportant predictor of hepatocellular carcinoma survivaland one of themain factors of the hepatocellular carcinomaclinical staging system. Hence, it is possible that HBeAg isassociated with hepatocellular carcinoma survival. Somestudies on patients with hepatocellular carcinoma withHBV infection indicate that HBeAg is an independent prog-nostic factor or correlated with hepatocellular carcinomasurvival (31, 32). Furthermore, a lot of studies indicate thatHBV infection is a predictive factor for hepatocellular car-cinoma survival (33, 34). Therefore, our result providesfurther support that HBeAg was a predictor for hepatocel-lular carcinoma survival.

In conclusion, we used a large comprehensive set ofmiRNAs to identify a new 30-miRNA signature that candiscriminate hepatocellular carcinoma tissues from non-cancerous liver tissues and a novel 20-miRNA signature thatcan predict the survival of patients with hepatocellular






carcinoma in the training set. Both signatures are validatedin the test and independent sets.MostmiRNA species of oursignatures have not been reported previously in hepatocel-lular carcinoma. The findings in the present study have thepotential to provide novel molecular approaches for diag-nosis and prognosis of patients with hepatocellular carci-noma. The miRNAs of the 2 signatures may also be impor-tant for studying pathogenesis of hepatocellular carcinomaand identifying potential targets for hepatocellular carcino-ma therapy. Furthermore, the study on these miRNAs willprovide new insights into the development and progressionof hepatocellular carcinoma.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: R. Wei, M. Shi, Y.-F. Yuan, H.-Y. WangDevelopment of methodology: R. Wei, G.-L. Huang, M. Shi, H.-Y. WangAcquisitionofdata (provided animals, acquired andmanagedpatients,provided facilities, etc.): R. Wei, G.-L. Huang, M.-Y. Zhang, B.-K. Li,H.-Z. Zhang, M. Shi, L. Huang, W.-H. Jia, Y.-F. Yuan, H.-Y. Wang

Analysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis):R.Wei,G.-L.Huang, B.-K. Li,H.-Z. Zhang,M. Shi, X.-Q. Chen, Q.-M. Zhou, Y.-F. Yuan, H.-Y. WangWriting, review, and/or revision of themanuscript: R.Wei, G.-L. Huang,M. Shi, X.F.S. Zheng, H.-Y. WangAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): R. Wei, M.-Y. Zhang, B.-K. Li,H.-Y. Wang

AcknowledgmentsThe authors thank Miss Qi Wang, a student of Doctor of Pharmacy

Program in Rutgers University (New Brunswick, NJ) for her assistance inproofreading this manuscript.

Grant SupportThis work was fully supported by the National Natural Science Founda-

tion of China (No: 30973397 to H.-Y. Wang) and the Research Fund of StateKey Laboratory of Oncology in South China (to H.-Y. Wang).

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received August 19, 2012; revised April 16, 2013; accepted May 30, 2013;published OnlineFirst June 28, 2013.

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2013;19:4780-4791. Published OnlineFirst June 28, 2013.Clin Cancer Res Rongrong Wei, Guo-Liang Huang, Mei-Yin Zhang, et al. Expression Signatures in Hepatocellular CarcinomaClinical Significance and Prognostic Value of microRNA

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