Large-scale Circulating microRNA Proﬁling for the Liquid ... · Precision Medicine and Imaging Large-scale Circulating microRNA Proﬁling for the Liquid Biopsy of Prostate Cancer

Precision Medicine and Imaging

Large-scale Circulating microRNA Profiling forthe Liquid Biopsy of Prostate CancerFumihiko Urabe1,2, Juntaro Matsuzaki1, Yusuke Yamamoto1, Takahiro Kimura2,Tomohiko Hara3, Makiko Ichikawa1,4, Satoko Takizawa1,4, Yoshiaki Aoki5,Shumpei Niida6, Hiromi Sakamoto7, Ken Kato8, Shin Egawa2, Hiroyuki Fujimoto3,and Takahiro Ochiya1

Abstract

Purpose: The high false-positive rate of prostate-specificantigen (PSA) may lead to unnecessary prostate biopsies.Therefore, the United States Preventive Services Task Forcerecommends that decisions regarding PSA-based screeningof prostate cancer should be made with caution in men ages55–69 years, and that men �70 years should not undergoPSA screening. Here, we investigated the potential of serummiRNAs as an accurate diagnostic method in patients withsuspected prostate cancer.

Experimental Design: Serum samples of 809 patientswith prostate cancer, 241 negative prostate biopsies, and500 patients with other cancer types were obtained fromthe National Cancer Center, Japan. Forty-one healthy con-trol samples were obtained from two other hospitals inJapan. Comprehensive microarray analysis was perform-ed for all samples. Samples were divided into three sets.

Candidate miRNAs for prostate cancer detection wereidentified in the discovery set (n ¼ 123). A diagnosticmodel was constructed using combinations of candidatemiRNAs in the training set (n ¼ 484). The performanceof the diagnostic model was evaluated in the validation set(n ¼ 484).

Results: In the discovery set, 18 candidate miRNAs wereidentified. A robust diagnostic model was constructedusing the combination of two miRNAs (miR-17-3p andmiR-1185-2-3p) in the training set. High diagnostic per-formance with a sensitivity of 90% and a specificity of90% was achieved in the validation set regardless of theGleason score and clinical tumor–node–metastasis stage.

Conclusions: The model developed in this study may helpimprove the diagnosis of prostate cancer and reduce thenumber of unnecessary prostate biopsies.

IntroductionProstate cancer is the most frequently diagnosed cancer in

men and the third leading cause of cancer-related death in menin the United States (1), and its incidence and mortality are alsoincreasing in Japan (2). The 5-year relative survival rate inpatients with localized prostate cancer is approximately100% regardless of treatment modality; however, in patientswith metastatic disease, the 5-year relative survival decreasesmarkedly to 30% (1, 3). Therefore, early diagnosis before the

development of metastatic sites is important to reduce themortality of prostate cancer. Digital rectal examination (DRE)and serum prostate-specific antigen (PSA) monitoring are thestandard methods of prostate cancer screening (4). However,the accuracy of these methods for the detection of prostatecancer is limited. DRE is a subjective test, and the degree ofaccuracy depends on the experience of the examiner (5). In ameta-analysis, DRE had an estimated sensitivity of 51%, aspecificity of 59%, and a calculated overall positive predictivevalue of 41% for the detection of prostate cancer (6). Inaddition, PSA has low specificity and a high false-positive ratein patients with benign prostatic hyperplasia (BPH; ref. 7).Therefore, DRE and measuring PSA may lead to unnecessaryprostate biopsy and potential complications such as infection,bleeding, urinary retention, and pain. Indeed, PSA testing isestimated to lead to approximately 750,000 unnecessary biop-sies for prostate cancer in the United States every year (8).Therefore, the development of efficient and less-invasive bio-markers for the diagnosis of prostate cancer is urgent.

Recently, liquid biopsies based on circulating tumor cells,circulating tumor DNA, circulating RNA, or miRNAs havereceived increased attention as repeatable and minimally inva-sive tests for early diagnosis, cancer monitoring, and diagnosisof recurrent disease (9–11). miRNAs are small noncoding RNAsof 20–25 nucleotides in length that posttranscriptionallyregulate the expression of thousands of genes and thereby playimportant roles in oncogenesis and metastasis (12). miRNAssecreted from cells are chaperoned by various carriers, such as

1Division of Molecular and Cellular Medicine, National Cancer Center ResearchInstitute, Tokyo, Japan. 2Department of Urology, The Jikei University School ofMedicine, Tokyo, Japan. 3Department of Urology, National Cancer CenterHospital, Tokyo, Japan. 4Toray Industries, Inc., Kanagawa, Japan. 5DynacomCo., Ltd., Chiba, Japan. 6Medical Genome Center, National Center for Geriatricsand Gerontology, Aichi, Japan. 7Department of Biobank and Tissue Resources,National Cancer Center Research Institute, Tokyo, Japan. 8Department ofGastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo,Japan.

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

F. Urabe and J. Matsuzaki contributed equally to this article.

Corresponding Author: Takahiro Ochiya, National Cancer Center ResearchInstitute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. Phone: 813-3542-2511, ext. 3669; Fax: 813-3543-9305; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-18-2849

�2019 American Association for Cancer Research.

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extracellular vesicles, RNA-binding proteins, or high-densitylipoproteins, and circulating miRNAs can exist stably in bodyfluids (10). In addition, circulating miRNAs are associated withdisease conditions, and the potential of circulating miRNAs asdiagnostic biomarkers has been demonstrated (10).

Several studies demonstrated the effectiveness of circulatingmiRNAs as diagnostic biomarkers of prostate cancer (13–17).However, the results reported are inconsistent, which may beattributed to the limited number of samples and inconsisten-cies among detection protocols (18, 19). To resolve this issue,we recently launched a national project in Japan, entitled"Development and Diagnostic Technology for Detection ofmiRNA in Body Fluids." The aims of this project are to stan-dardize platforms for the evaluation of serum miRNAs and tocharacterize the serum miRNA profiles of 13 types of humancancer, including prostate cancer, using a large sample size(N > 40,000). In this study, we used these samples to investi-gate the efficacy of circulating miRNAs as biomarkers for thediagnosis of prostate cancer in men with suspected prostatecancer.

Materials and MethodsSample collectionProstate cancer and negative prostate biopsy patients. Prostatecancer serum samples were obtained from patients referred tothe National Cancer Center (NCC) Hospital (NCCH) who werehistologically diagnosed as prostate cancer. Negative prostatebiopsy (NPBx) serum samples were obtained from patientswho were not diagnosed with prostate cancer based on theresults of prostate needle biopsy at the NCCH. These sampleswere registered in the NCC Biobank between 2008 and 2016and stored at �20�C until further use. Clinical informationfor each participant was retrospectively obtained from theelectronic medical records. Exclusion criteria were as follows:(i) treatment by surgical operation, hormone therapy, chemo-therapy, or radiotherapy against prostate cancer before thecollection of serum; and (ii) simultaneous or previous diag-nosis of cancer in other organs.

Healthy controls.Healthy control serum samples were obtainedfrom the National Center for Geriatrics and Gerontology(NCGG) and the Yokohama Minoru Clinic (YMC). The inclu-sion criteria for these sample sets were no history of cancer andno hospitalization during the past 3 months, and the serumsamples were stored at �80�C until further use. Informationabout urological background, such as serum PSA levels, wasnot available for most samples. Demographic and clinicalcharacteristics of patients were obtained on the day of samplecollection.

Other cancers. To determine the specificity of the identifiedmiRNAs, samples from other cancers were included in theanalysis. Serum samples of male patients with 10 solid cancersincluding glioma, colorectal adenocarcinoma, esophagealsquamous cell carcinoma, lung carcinoma, hepatocellularcarcinoma, gastric adenocarcinoma, biliary tract cancer, boneand soft tissue sarcoma, pancreatic cancer, and bladder cancerwere collected from the NCCH between 2008 and 2016. Thehistologic diagnosis was retrospectively confirmed using theelectronic medical records.

Serum miRNA expression analysisTotal RNA was extracted from 300 mL of serum using the 3D-

Gene RNA Extraction Reagent (Toray Industries, Inc.). Compre-hensive miRNA expression analysis was performed using the 3D-GenemiRNALabelingKit and the 3D-GeneHumanmiRNAOligoChip (Toray Industries, Inc.), which was designed to detect 2,588miRNAs registered in miRBase release 21 (http://www.mirbase.org/; ref. 20). Fluorescent signals for each spot on the microarraywere obtained using the 3D-Gene Microarray Scanner (TorayIndustries, Inc.) and digitized using the accessory digitizing appli-cation "Extraction" (Toray Industries, Inc.). For quality control ofmicroarray data, the criteria for low-quality results were as fol-lows: (i) coefficient of variation for negative control probes>0.15; and (ii) number of flagged probes identified as an unevenspot image by 3D-Gene Scanner >10. Samples meeting thesecriteria were excluded from further analyses. The presence ofmiRNAs was determined on the basis of a corresponding micro-array signal greater than the [mean þ 2 � SD] of the negativecontrol signal from which the top and bottom 5%, ranked bysignal intensity, were removed. Once a miRNA was consideredpresent, the mean signal of the negative controls was subtractedfrom the miRNA signal. To normalize the signals among themicroarrays tested, three preselected internal control miRNAs(miR-149-3p, miR-2861, and miR-4463) were used as describedpreviously (21). When the signal value was negative (or unde-tected) after the normalization, the value was replaced by 0.1 on abase-2 logarithm scale. All microarray data in this study wereobtained in accordance with the Minimum Information about aMicroarray Experiment guidelines and are publicly availablethrough the Gene Expression Omnibus database (GSE112264).The reproducibility of the microarray analysis was confirmedby performing microarray analyses on the same RNA sample15 times. A strong correlation between the 15 replicates wasindicated by a Pearson correlation coefficient (R) of 0.96 (95%confidence interval, 0.94–0.98; Supplementary Fig. S1).

Identification of candidate miRNAsSamplesweredivided into threegroups:discovery, training, and

validation sets. The discovery set was used for the selection of

Translational Relevance

Prostate cancer is the most frequently diagnosed tumoramong men and the third leading cause of cancer-relateddeaths in the United States. Prostate cancer screeningmainly relies on prostate-specific antigen (PSA) testing.However, the lack of specificity of PSA tests may lead tounnecessary biopsies. Prostate biopsy sampling is an inva-sive procedure that can cause complications such as uri-nary retention and infection. Thus, identifying biomarkersfor minimally invasive detection is desirable. CirculatingmiRNAs can provide crucial information about cancerousconditions in a less-invasive manner. Large-scale miRNAmicroarray analyses were used to establish a model basedon a combination of circulating miRNAs to detect prostatecancer in men with suspected prostate cancer with highsensitivity and specificity. This model could help reduce thenumber of unnecessary biopsies and improve the diagnosisof prostate cancer.

Serum miRNA Biomarker for Prostate Cancer

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miRNA biomarker candidates. First, highly expressed miRNAswith a signal value >26 in more than 50% of prostate cancer orNPBx samples were selected in the discovery set. Subsequently, across-validation score, which indicates the robustness of dis-crimination performance between prostate cancer and NPBxsamples, was calculated on the basis of Fisher linear discrim-inant analysis for each of the selected miRNAs in the discoveryset (Supplementary Data). miRNAs with a cross-validationscore >0.70 were further selected. Finally, the expression levelsof each miRNA were compared between prostate cancer, NPBx,and healthy control samples, and miRNAs with the highest andlowest expression levels in prostate cancer samples comparedwith the other groups were identified.

Construction of diagnostic modelsThe residual prostate cancer and NPBx samples were randomly

divided into training and validation sets. In the training set, thebest combinations of the identified miRNAs were explored usingFisher linear discriminant analysis with leave-one-out cross-validation (Supplementary Data). Briefly, the best 20 discrimi-nants by one miRNA were selected, one of the residual miRNAswas added to generate two-miRNA discriminants, and the best20 discriminants by two miRNAs were selected. This method wasused to generate 1–10-miRNA discriminants. Subsequently, thebest discriminants for each number of miRNAs were listed, asshown in Table 2, and finally the model showing the best areaunder the receiver operating characteristic (ROC) curve (AUC)with the least number ofmiRNAswas selected. The solution of thediscriminant (an "index") �0 indicated the presence of prostatecancer, whereas an index <0 indicated the absence of prostatecancer. The performance of the diagnostic index was evaluated inthe validation set, and the performance of themodel was tested inother solid cancers.

Construction of cancer discrimination modelsA model was constructed to discriminate prostate cancer

from the other cancer types. Candidate miRNAs for modelconstruction were the same as those identified in the discoveryset. The residual prostate cancer, NPBx, and other cancer sam-ples were randomly divided into training and validation sets.miRNA combination models were constructed in the trainingset in the same way, and the performance was evaluated in thevalidation set.

Statistical analysisx2 test for categorical variables or one-way ANOVA for contin-

uous variables was used to compare the characteristics of patients[Gleason score (GS), serum PSA, age, and clinical tumor–node–metastasis stage (cTNM; UICC2009 7th TNM)] in the threesample sets. The unpaired t test was used to compare the char-acteristics (serum PSA and age) of patients with prostate cancerand NPBx. Linear discriminant analysis and model selectionbased on leave-one-out cross-validation were performed usingR version 3.1.2 (R Foundation for Statistical Computing, http://www.R-project.org), compute.es package version 0.2-4, hashpackage version 2.2.6, MASS package version 7.3-45, mutosspackage version 0.1-10, pROC package version 1.8, and STATAversion 14 (StataCorp). Unsupervised clustering and heatmapgeneration using Pearson correlation inWard method for linkageanalysis, and principal component analysis (PCA), were per-formed using Partek Genomics Suite 6.6. The limit of statistical

significance for all analyses was defined as a two-sided P valueof 0.05.

Ethical statementThe study was approved by the NCCH Institutional Review

Board (2015-376, 2016-249) and the Research Committeeof Medical Corporation Shintokai Yokoama Minoru Clinic(6019-18–3772). Written informed consent was obtainedfrom each participant. This study was conducted in accordancewith the ethical guideline of "Declaration of Helsinki."

ResultsParticipants

A total of 1,044 prostate cancer and 241 NPBx serum sampleswere analyzed by miRNA microarray, yielding comprehensivemiRNA expression profiles. Among the prostate cancer serumsamples, 38 were excluded for lack of patient information, 3 forsimultaneous diagnosis of other cancers, 181 for treatmentbefore the collection of serum, and 13 for low-quality micro-array results, leaving 809 samples for analysis. Prostate cancerand NPBx samples were randomly classified into discovery,training, and validation sets (Fig. 1A). There were no significantdifferences in the characteristics listed in Table 1 between thethree sample sets.

The discovery set included 41 prostate cancer and 41 NPBxsamples. The training and validation sets included 384 prostatecancer and 100NPBx samples each. In the discovery set, there wasno difference in age between patients with prostate cancer,patients with NPBx, and healthy controls (P ¼ 0.44). In thetraining and validation sets, patients with prostate cancer wereolder than patients with NPBx (P ¼ 0.001 and 0.014, respective-ly). Therefore, age-adjusted analysis was performed after themodel construction as described below. Serum PSA levels andfamily history did not differ significantly between prostate cancerand NPBx samples in each of the three sample sets (Supplemen-tary Table S1).

Forty-one healthy male control serum samples and 50 serumsamples obtained from each group of men with 10 other solidcancers, including glioma, colorectal adenocarcinoma, esophage-al squamous cell carcinoma, lung carcinoma, hepatocellularcarcinoma, gastric adenocarcinoma, biliary tract cancer, bone andsoft tissue sarcoma, pancreatic cancer, and bladder cancer, wererandomly selected from ourmiRNA database consisting of serummiRNA profiles of more than 15,000 samples.

Selection of circulating miRNA biomarker candidatesThe expression levels of the miRNAs were analyzed in the

discovery set (41 prostate cancer and 41 NPBx samples). A totalof 408 miRNAs passed the quality check criteria and wereselected (Fig. 1B). PCA mapping with these 408 miRNAssuggested that the miRNA profiles differed between the prostatecancer and NPBx samples (Fig. 1C). We identified 38 miRNAswith a cross-validation score >0.70 between prostate cancer andNPBx in the discovery set (Fig. 1B).

To select cancer-specific miRNAs, 41 healthy male controlswere included in the analysis, and the expression levels ofthe 38 miRNAs were compared between the three sample sets(41 prostate cancer, 41 NPBx, and 41 healthy male controlsamples). The analysis identified 16 miRNAs that were themost upregulated in prostate cancer and 2 miRNAs that were

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the most downregulated in prostate cancer (Fig. 1D). Signalvalues of these 18 miRNAs >26 in more than 50% of prostatecancer or NPBx were confirmed in the training and validationsets (Supplementary Fig. S2).

Identifying the best combination of miRNAs for prostatecancer diagnosis

Fisher linear discriminant analysis was used to designcomprehensive discriminants consisting of 1–10 miRNAs inthe training set (Supplementary Table S2). On the basis of thecross-validation score, the best combinations for each numberof miRNAs were selected (Table 2). On the basis of the

AUC reaching the optimal value (�0.99), a combinationof two miRNAs (miR-17-3p and miR-1185-2-3p) was consid-ered as the best model in the training set (diagnostic index ¼0.657 � miR-17-3p þ 0.385 � miR-1185-2-3p - 6.341; AUC,0.99; sensitivity, 91%; specificity, 97%). Single miRNAs werealso statistically significantly effective in distinguishing pati-ents with cancer (AUC, 0.97 for miR-17-3p; 0.92 for miR-1185-2-3p; Fig. 2). The diagnostic performance of the model wasconfirmed in the validation set, which showed that the modelwas accurate (AUC, 0.95; sensitivity, 90%; specificity, 90%;Fig. 2). Because patient age was not matched between pro-state cancer samples and NPBx samples, we performed age-

Figure 1.

Strategy for the selection ofcandidate miRNAs.A,Work flow ofpatients with prostate cancer (PCa)and NPBx and healthy controls usedfor developing a diagnostic model.Serum samples were obtained from1,044 PCa, 241 NPBx, and 41 healthycontrols. The sample set was dividedinto three groups: the discovery,training, and validation sets. B, Flowdiagram of miRNAs used for selectingcandidate miRNAs. C,A PCAmap for41 PCa samples and 41 NPBx sampleswith 408 miRNAs. D, Heatmapshowing the differences in miRNAexpression levels between PCa,NPBX, and healthy control samples.The 16 miRNAs surrounded by a redline were specifically upregulated inPCa, whereas the 2 miRNAssurrounded by a blue line werespecifically downregulated in PCa.






adjusted logistic regression analysis in the validation set.The odds ratios of the signal intensity of the two miRNAsand the diagnostic index for the presence of prostate cancerwere almost the same before and after adjusting for age(Supplementary Table S3), indicating that the diagnosticindex was independently associated with the presence of pro-state cancer.

Performance of the diagnostic index according to clinicalconditions

The performance of the diagnostic index for each prostatecancer grade was examined in the validation set. GS and cTNMstage were used to assess the performance of the diagnosticindex. The diagnostic index showed high performance for allGS values (GS6, 89%; GS3þ4, 91%; GS4þ3, 92%; and GS �8,89%), T stages (T1c, 93%; T2, 87%; and �T3, 92%), N stages(N0, 90%; and N1, 89%), and M stages (M0, 91%; and M1,85%). In addition, the score of the diagnostic index was

significantly lower in GS6 prostate cancer than in other GSgroups (P < 0.01; Fig. 3).

Comparison of prostate cancer and other solid cancers by thediagnostic index

To investigate whether the serum miRNA profile can distin-guish prostate cancer from other solid cancers, we examined theperformanceof thediagnostic index inother solid cancers. For thispurpose, we randomly selected 50male serum samples from eachgroup of 10 other solid cancers, and comprehensively analyzedthe serum miRNA profiles of these solid cancers. The diagnosticindex showed a high performance (�70%) for all 10 solid cancers(Supplementary Fig. S3).

Potential of serum miRNA profiles to discriminate prostatecancer from other solid cancers

We investigated whether the serum miRNA profile candistinguish prostate cancer from other solid cancers. For

Table 1. Patient characteristics

Characteristics Discovery set (n ¼ 123) Training set (n ¼ 484) Validation set (n ¼ 484) P

Prostate cancer 41 384 384Median age, years (range) 67 (62–69) 68 (63–73) 67 (62–72) 0.13Median PSA, ng/mL (range) 9.4 (5.8–16.4) 9.0 (5.8–17.1) 8.6 (5.8–20.4) 0.30Gleason score, n (%) 0.526 4 (9.8) 45 (11.7) 37 (9.6)3þ4 15 (36.6) 122 (31.8) 107 (27.9)4þ3 5 (12.2) 70 (18.2) 84 (21.9)8= 17 (41.5) 147 (38.3) 156 (40.6)

Clinical T stage, n (%) 0.31T1c 10 (24.4) 124 (32.3) 122 (31.8)T2a-c 25 (60.1) 158 (41.1) 171 (44.5)T3a-b 5 (12.2) 95 (24.7) 83 (21.6)T4 1 (2.4) 7 (1.8) 8 (2.1)

Clinical N stage, n (%) 0.53N1 1 (2.4) 26 (6.8) 27 (7.0)N0 40 (97.6) 358 (93.2) 357 (93.0)

Clinical M stage, n (%) 0.79M1 3 (7.3) 28 (7.3) 33 (8.6)M0 38 (93.7) 356 (92.7) 351 (91.4)

Family history, n (%) 0/14 (0) 27/185 (14.6) 19/187 (10.2) 0.16Negative prostate biopsy 41 100 100Median age, years (range) 66 (61–70) 65 (62–70) 66 (61–70) 0.93Median PSA, ng/mL (range) 7.5 (5.2–10.6) 7.1 (5.0–9.8) 7.6 (5.6–10.6) 0.25Family history, n (%) 0/17 (0) 4/40 (10.0) 4/41 (9.6) 0.47Healthy control 41 N.A. N.A.Median age, years (range) 70 (48–77) N.A. N.A.

Table 2. Discriminant analysis for prostate cancer (diagnostic model)

Model Number of miRNAs Sensitivity (%) Specificity (%) Accuracy (%) PPV (%) NPV (%) AUC

Model 1 1 88 93 89 98 67 0.97Model 2 2 91 97 92 99 73 0.99Model 3 3 91 97 92 99 73 0.99Model 4 3 95 92 94 98 81 0.98Model 5 4 93 95 94 99 79 0.99Model 6 5 91 97 92 99 73 0.99Model 7 5 94 95 94 99 81 0.99

NOTE: Model 1: (0.76687) � miR-17-3p-4.05937; model 2: (0.657037) � miR-17-3pþ(0.384996) � miR-1185-2-3p-6.34099; model 3: (0.66011) � miR-17-3pþ(0.403526) � miR-1185-2-3pþ(-0.223082) � miR-197-5p-4.61166; model 4: (0.690323) � miR-17-3pþ(0.491444) � miR-1185-1-3pþ(-0.438635) � miR-6819-5p-3.70837; model 5: (0.582969)�miR-17-3pþ(0.408897)�miR-1185-1-3pþ(0.394516)�miR-6076þ(-0.408373)�miR-197-5p-5.77338; model 6: (0.579395)�miR-17-3pþ(0.410828) � miR-1185-1-3pþ(0.382413) � miR-6076þ(-0.396207) � miR-197-5pþ(-0.156305) � miR-1228-5p-4.17594; model 7: (0.569247) � miR-17-3pþ(0.40399)�miR-1185-1-3pþ(0.34074)�miR-6076þ(-0.423294)�miR-197-5pþ(0.0754199)�miR-320b-5.49499. Bold line indicates the selected diagnostic model.Abbreviations: NPV, negative predictive value; PPV, positive predictive value.

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this purpose, prostate cancer, NPBx, and the other cancersamples were randomly divided into training and valida-tion sets (Fig. 4A). Using the 18 miRNAs identified inthe discovery set, comprehensive discriminants consisting of

1–18 miRNAs were developed in the training set (cancerdiscrimination model; Supplementary Table S4). On thebasis of the optimal level of AUC, a combination of 12miRNAs (miR-6471-5p, miR-17-3p, 1343-5p, miR-4417,

Figure 2.

ROC curve analysis of the diagnostic index. ROCcurves for detecting patients with prostate cancerusing serum PSA levels and the two miRNAsselected for the diagnostic model in the trainingand validation sets.






miR-1185-1-3p, miR-1202, miR-422a, miR-6877-5p, miR-6076,miR-3185, miR-320b, and miR-1185-2-3p) was considered asthe best discrimination model in the training set [cancerdiscrimination index ¼ 1.059 � miR-6741-5p þ 0.207 �miR-17-3p - 1.432 � miR-1343-5p þ 0.918 � miR-4417 þ0.163 � miR-1185-1-3p - 0.408 � miR-1202 - 0.161 � miR-422a - 0.350 � miR-6877-5p þ 0.279 � miR-6076 þ 0.376 �miR-3185 þ 0.131 � miR-320b þ 0.338 � miR-1185-2-3p -7.13; AUC: 0.96; sensitivity: 93%; specificity: 87%]. Thediagnostic performance of this model was confirmed in thevalidation set (AUC: 0.91; sensitivity: 91%; specificity:78%; Fig. 4B). Although this model was able to discriminateprostate cancer from NPBx, colorectal adenocarcinoma, boneand soft tissue sarcoma, esophageal squamous cell carcinoma,hepatocellular carcinoma with a specificity >80%, it could notsuccessfully distinguish prostate cancer from glioma, gastricadenocarcinoma, lung carcinoma, pancreatic cancer, biliarytract cancer, and bladder cancer (Fig. 4C). Notably, the indexof bladder cancer samples was similar to that of prostatecancer samples.

DiscussionIn this study, a comprehensive analysis of serum miRNA

expression was performed using samples from 809 prostatecancer and 241 patients with NPBx on a standardized micro-array platform (3D-Gene, Toray Industries, Inc.). The resultsshowed that patients with prostate cancer can be accuratelydistinguished from patients with NPBx according to the serumlevels of two miRNAs. To the best of our knowledge, fiveprevious reports demonstrated the potential of circulatingmiRNAs as diagnostic biomarkers for prostate cancer (13–17).The largest of these studies included 105 patients with prostatecancer (13). In addition, a comprehensive analysis of all 2,588miRNAs was not performed in these studies. In this study,we examined the expression profiles of 2,588 miRNAs, whichconstitute all the human miRNAs identified to date accordingto miRBase rel. 21; this is the largest sample size of prostatecancer reported to date.

The results of this study identified the combination ofmiR-17-3p and miR-1185-2-3p in the serum as a biomarkerfor the detection of prostate cancer. The serum expression

Figure 3.

Diagnostic performance of the model at different stages of prostate cancer. Diagnostic performance of the two selected miRNAs at different stagesin the validation set. The diagnostic index showed high performance for all GSs and T, N, and M stages. The score of the diagnostic index wassignificantly lower in low-grade (GS6) prostate cancer. The P values were calculated by one-way ANOVA. Diagnostic accuracy (%) is indicated.

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profile or function of miR-1185-2-3p has not been reportedpreviously. However, miR-17-3p was previously shown to act asan oncogenic miRNA in prostate cancer. Yang and colleaguesshowed that miR-17-5p and miR-17-3p promote prostate can-cer proliferation and invasion by targeting the same protein,namely, tissue inhibitor of metalloproteinase 3 (22). Fengand colleagues reported that the expression levels of miR-17-3pare significantly higher in prostate cancer tissues than in BPH (23).These reports suggest that elevated levels of miR-17-3p inserum are associated with prostate cancer and reflect diseaseprogression. However, in this study, we were unable to identifythe origin of the two miRNAs. We need to think the possibilitythat these miRNAs are not released from cancer cells. Huipingand colleagues reported that miR-17-3p is secreted from

immune cells, and serum levels of miR-17-3p may be helpfulto predict the therapeutic benefit of trastuzumab in patientswith HER2-positive breast cancer (24). Therefore, the upregu-lation of serum miR-17-3p and miR-1185-2-3p in patientswith prostate cancer could be caused by a type of cells otherthan prostate cancer cells in the tumor microenvironment.Further studies are needed to elucidate the detailed mechanismunderlying the upregulation of these miRNAs in patients withprostate cancer.

These results showed that the miRNA profile of prostatecancer is distinct from that of BPH regardless of the clinicalTNM stage. In addition, although the diagnostic index of ourmodel did not show complete correlation with the GS, thediagnostic index of low-grade (GS6) prostate cancer was

Figure 4.

Development of a cancer discrimination model ofprostate cancer from other cancers. A,Work flow ofthe patients included in the development of aprediction model. Serum samples were obtained from1,500 subjects, including 809 patients with prostatecancer (PCa), 241 patients with NPBx, and 500patients with other cancer. After the selection ofcandidate miRNAs in the discovery set, the sample setwas divided into two groups, a training set and avalidation set. B, ROC curves for detecting patientswith PCa using the miRNAs selected for the detectionmodel. C, Diagnostic index using the prediction modelin the validation set [PCa, 568; NPBx, 100; sarcoma(SA), 40; colorectal adenocarcinoma (CC), 40;esophageal squamous cell carcinoma (EC), 40;hepatocellular carcinoma (HC), 40; lung cancer (LK),40; pancreatic cancer (PC), 40; glioma (GL), 40; biliarytract carcinoma (BT), 40; gastric adenocarcinoma(GC), 40; bladder cancer (BL), 40]. Diagnosticaccuracy (%) is indicated.






significantly lower than that of high-grade (GS �7) prostatecancer. A high GS is associated with more aggressive disease,whereas a low GS is associated with a more indolent diseasecourse. Urologists often use the GS to design personalizedtreatment strategies for their patients (25). These results indi-cate that the diagnostic index of our model may help identifypatients who would benefit from treatments such as radiother-apy or prostatectomy, although further study is needed toconfirm these results.

In this study, we also investigated whether the two-miRNAdiagnostic index could discriminate between prostate cancerand other types of cancer. The results indicated that the diag-nostic model was not specific for patients with prostate cancer.This may be attributed to the fact that miR-17-3p was includedin the miRNA profile. miR-17-3p is a member of the miR-17/92cluster, which is overexpressed in many human cancers.Circulating miR-17-3p is upregulated in several types of cancersuch as colorectal cancer (26) and lung cancer (27). Therefore,the possibility of other concomitant cancers needs to be con-sidered in cases showing an increased diagnostic index ofprostate cancer. As most previous studies about circulatingmiRNAs in cancer did not demonstrate their specificityfor certain types of cancer (10), this is one of the strengths ofthis study.

To investigate whether the serum miRNA profile can distin-guish prostate cancer from other solid cancers, we developedanother model (cancer discrimination model). We confirmedthat it is possible to establish a model to distinguish prostatecancer from other types of cancer. However, even this modelcould not discriminate prostate cancer from bladder cancer,which belong to the same group of urogenital cancers. This isthe first report to compare the expression level of serummiRNAs between patients with prostate cancer and those withbladder cancer, and the results suggest the existence of acommon mechanism mediating the upregulation of thesemiRNAs in prostate cancer and bladder cancer. In addition,the sensitivity of the cancer discrimination model was thesame as that of the diagnostic model in the validation set.The need for an increased number of miRNAs could increasethe detection costs; therefore, the two-miRNA combinationmodel would be cost effective because its performance isadequate as a clinical application to reduce unnecessary pro-state biopsy.

The absence of prostate cancer in healthy controls wasdefined according to the self-reported medical history, and itwas not confirmed by pathologic examination. Because theincidence of latent prostate cancer increases with age (28),serum miRNAs from healthy men could not be used as acontrol group in the discovery and training sets. We thereforeanalyzed serum samples derived from pathologically con-firmed patients to construct the diagnostic model of prostatecancer, and used miRNAs from healthy controls as supportiveinformation to select miRNAs showing higher or lower expres-sion in prostate cancer than in the other sample sets. However,because needle biopsy was mainly performed in patients show-ing increased PSA levels, the diagnostic power of PSA was lowin this study. As miRNA profiling could discriminate patientswith prostate cancer and NPBx with a high PSA score, miRNAprofiling could be a powerful tool to complement PSA screen-ing, thereby decreasing the number of patients referred forneedle biopsy.

This study used retrospectively collected samples; there-fore, the storage conditions before microarray analysis werenot strictly regulated, which may have affected the results.Indeed, several studies reported that miRNAs are affected byvarious processes (29, 30). Because direct comparisonbetween NCC Biobank samples and healthy control samplescould introduce bias, we did not select biomarker miRNAcandidates by comparing miRNAs between prostate cancerand healthy samples. Rather, we used healthy samples tofurther select the miRNA candidates that were upregulated ordownregulated both in NPBx and healthy samples comparedwith malignant samples. This process allowed the exclusionof certain miRNAs showing alterations in serum levels onlyin patients with NPBx but not in patients with prostatecancer. In addition, we recently launched a clinical prospec-tive study to validate the general applicability of our datausing fresh serum samples, and we will report our resultswithin several years.

In summary, a comprehensive analysis of serum miRNA pro-files of 809 cases of prostate cancer and 241 cases of NPBxidentified a promising combination of two miRNAs, miR-17-3pand miR-1185-2-3p, for the detection of prostate cancer. Thisstudy is the largest scale study performed to date, and the resultsindicated that evaluation of circulating miRNAs is a feasiblemethod for detecting prostate cancer in men with suspectedprostate cancer. The high sensitivity and specificity of this modelcould help reduce the number of unnecessary biopsies andimprove the accuracy of diagnosis.

Disclosure of Potential Conflicts of InterestS. Takizawa is a group leader at Toray Industries, Inc. No other potential

conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: F. Urabe, J. Matsuzaki, K. Kato, T. OchiyaDevelopment of methodology: T. Hara, S. Takizawa, K. Kato, T. OchiyaAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): F. Urabe, J. Matsuzaki, T. Hara, M. Ichikawa,S. Takizawa, K. Kato, S. Egawa, H. FujimotoAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): F.Urabe, J.Matsuzaki, Y. Yamamoto, Y. Aoki, K. Kato,S. EgawaWriting, review, and/or revision of the manuscript: F. Urabe, J. Matsuzaki,Y. Yamamoto, T. Kimura, T. Hara, S. Takizawa, S. Niida, S. Egawa, H. Fujimoto,T. OchiyaAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): M. Ichikawa, S. Niida, H. Sakamoto,H. FujimotoStudy supervision: T. Hara, T. Ochiya

AcknowledgmentsThe authors thank Tomomi Fukuda, Takumi Sonoda, Hiroko Tadokoro,

Megumi Miyagi, Tatsuya Suzuki, and Kamakura Techno-Science Inc. forperforming the microarray assays; Satoshi Kondou for technical support;Noriko Abe for the management of serum samples; Michiko Ohori for themanagement of personal information; Hitoshi Fujimiya for developing in-house analytic tools; and Kazuki Sudo for independent confirmation ofparticipant eligibility. Some of the samples were obtained from the NationalCancer Center Biobank, which is supported by the National Cancer CenterResearch and Development Fund (29-A-1). Some clinical information wasobtained from the Center for Cancer Registries, National Cancer Center. Theauthors also thank the Biobank at the National Center for Geriatrics andGerontology for providing biological resources. This study was financiallysupported through a "Development of Diagnostic Technology for Detection

Urabe et al.





of miRNA in Body Fluids" grant from the Japan Agency for Medical Researchand Development (to T. Ochiya).

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby marked

advertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received September 3, 2018; revised December 21, 2018; accepted February11, 2019; published first February 26, 2019.

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