Large-scale Circulating microRNA Profiling for the Liquid Biopsy of Prostate Cancer

This article is featured in Highlights of This Issue, p. 2941

Prostate cancer is the most frequently diagnosed cancer in men and the third leading cause of cancer-related death in men in the United States (1), and its incidence and mortality are also increasing in Japan (2). The 5-year relative survival rate in patients with localized prostate cancer is approximately 100% regardless of treatment modality; however, in patients with metastatic disease, the 5-year relative survival decreases markedly to 30% (1, 3). Therefore, early diagnosis before the development of metastatic sites is important to reduce the mortality of prostate cancer. Digital rectal examination (DRE) and serum prostate-specific antigen (PSA) monitoring are the standard methods of prostate cancer screening (4). However, the accuracy of these methods for the detection of prostate cancer is limited. DRE is a subjective test, and the degree of accuracy depends on the experience of the examiner (5). In a meta-analysis, DRE had an estimated sensitivity of 51%, a specificity of 59%, and a calculated overall positive predictive value of 41% for the detection of prostate cancer (6). In addition, PSA has low specificity and a high false-positive rate in patients with benign prostatic hyperplasia (BPH; ref. 7). Therefore, DRE and measuring PSA may lead to unnecessary prostate biopsy and potential complications such as infection, bleeding, urinary retention, and pain. Indeed, PSA testing is estimated to lead to approximately 750,000 unnecessary biopsies for prostate cancer in the United States every year (8). Therefore, the development of efficient and less-invasive biomarkers for the diagnosis of prostate cancer is urgent.

Recently, liquid biopsies based on circulating tumor cells, circulating tumor DNA, circulating RNA, or miRNAs have received increased attention as repeatable and minimally invasive tests for early diagnosis, cancer monitoring, and diagnosis of recurrent disease (9–11). miRNAs are small noncoding RNAs of 20–25 nucleotides in length that posttranscriptionally regulate the expression of thousands of genes and thereby play important roles in oncogenesis and metastasis (12). miRNAs secreted from cells are chaperoned by various carriers, such as extracellular vesicles, RNA-binding proteins, or high-density lipoproteins, and circulating miRNAs can exist stably in body fluids (10). In addition, circulating miRNAs are associated with disease conditions, and the potential of circulating miRNAs as diagnostic biomarkers has been demonstrated (10).

Several studies demonstrated the effectiveness of circulating miRNAs as diagnostic biomarkers of prostate cancer (13–17). However, the results reported are inconsistent, which may be attributed to the limited number of samples and inconsistencies among detection protocols (18, 19). To resolve this issue, we recently launched a national project in Japan, entitled “Development and Diagnostic Technology for Detection of miRNA in Body Fluids.” The aims of this project are to standardize platforms for the evaluation of serum miRNAs and to characterize the serum miRNA profiles of 13 types of human cancer, including prostate cancer, using a large sample size (N > 40,000). In this study, we used these samples to investigate the efficacy of circulating miRNAs as biomarkers for the diagnosis of prostate cancer in men with suspected prostate cancer.

Prostate cancer serum samples were obtained from patients referred to the National Cancer Center (NCC) Hospital (NCCH) who were histologically diagnosed as prostate cancer. Negative prostate biopsy (NPBx) serum samples were obtained from patients who were not diagnosed with prostate cancer based on the results of prostate needle biopsy at the NCCH. These samples were registered in the NCC Biobank between 2008 and 2016 and stored at −20°C until further use. Clinical information for each participant was retrospectively obtained from the electronic medical records. Exclusion criteria were as follows: (i) treatment by surgical operation, hormone therapy, chemotherapy, or radiotherapy against prostate cancer before the collection of serum; and (ii) simultaneous or previous diagnosis of cancer in other organs.

Healthy control serum samples were obtained from the National Center for Geriatrics and Gerontology (NCGG) and the Yokohama Minoru Clinic (YMC). The inclusion criteria for these sample sets were no history of cancer and no hospitalization during the past 3 months, and the serum samples were stored at −80°C until further use. Information about urological background, such as serum PSA levels, was not available for most samples. Demographic and clinical characteristics of patients were obtained on the day of sample collection.

To determine the specificity of the identified miRNAs, samples from other cancers were included in the analysis. Serum samples of male patients with 10 solid cancers including glioma, colorectal adenocarcinoma, esophageal squamous cell carcinoma, lung carcinoma, hepatocellular carcinoma, gastric adenocarcinoma, biliary tract cancer, bone and soft tissue sarcoma, pancreatic cancer, and bladder cancer were collected from the NCCH between 2008 and 2016. The histologic diagnosis was retrospectively confirmed using the electronic medical records.

Total RNA was extracted from 300 μL of serum using the 3D-Gene RNA Extraction Reagent (Toray Industries, Inc.). Comprehensive miRNA expression analysis was performed using the 3D-Gene miRNA Labeling Kit and the 3D-Gene Human miRNA Oligo Chip (Toray Industries, Inc.), which was designed to detect 2,588 miRNAs registered in miRBase release 21 (http://www.mirbase.org/; ref. 20). Fluorescent signals for each spot on the microarray were obtained using the 3D-Gene Microarray Scanner (Toray Industries, Inc.) and digitized using the accessory digitizing application “Extraction” (Toray Industries, Inc.). For quality control of microarray data, the criteria for low-quality results were as follows: (i) coefficient of variation for negative control probes >0.15; and (ii) number of flagged probes identified as an uneven spot image by 3D-Gene Scanner >10. Samples meeting these criteria were excluded from further analyses. The presence of miRNAs was determined on the basis of a corresponding microarray signal greater than the [mean + 2 × SD] of the negative control signal from which the top and bottom 5%, ranked by signal intensity, were removed. Once a miRNA was considered present, the mean signal of the negative controls was subtracted from the miRNA signal. To normalize the signals among the microarrays tested, three preselected internal control miRNAs (miR-149-3p, miR-2861, and miR-4463) were used as described previously (21). When the signal value was negative (or undetected) after the normalization, the value was replaced by 0.1 on a base-2 logarithm scale. All microarray data in this study were obtained in accordance with the Minimum Information about a Microarray Experiment guidelines and are publicly available through the Gene Expression Omnibus database (GSE112264). The reproducibility of the microarray analysis was confirmed by performing microarray analyses on the same RNA sample 15 times. A strong correlation between the 15 replicates was indicated by a Pearson correlation coefficient (R) of 0.96 (95% confidence interval, 0.94–0.98; Supplementary Fig. S1).

Samples were divided into three groups: discovery, training, and validation sets. The discovery set was used for the selection of miRNA biomarker candidates. First, highly expressed miRNAs with a signal value >2⁶ in more than 50% of prostate cancer or NPBx samples were selected in the discovery set. Subsequently, a cross-validation score, which indicates the robustness of discrimination performance between prostate cancer and NPBx samples, was calculated on the basis of Fisher linear discriminant analysis for each of the selected miRNAs in the discovery set (Supplementary Data). miRNAs with a cross-validation score >0.70 were further selected. Finally, the expression levels of each miRNA were compared between prostate cancer, NPBx, and healthy control samples, and miRNAs with the highest and lowest expression levels in prostate cancer samples compared with the other groups were identified.

The residual prostate cancer and NPBx samples were randomly divided into training and validation sets. In the training set, the best combinations of the identified miRNAs were explored using Fisher linear discriminant analysis with leave-one-out cross-validation (Supplementary Data). Briefly, the best 20 discriminants by one miRNA were selected, one of the residual miRNAs was added to generate two-miRNA discriminants, and the best 20 discriminants by two miRNAs were selected. This method was used to generate 1–10-miRNA discriminants. Subsequently, the best discriminants for each number of miRNAs were listed, as shown in Table 2, and finally the model showing the best area under the receiver operating characteristic (ROC) curve (AUC) with the least number of miRNAs was selected. The solution of the discriminant (an “index”) ≥0 indicated the presence of prostate cancer, whereas an index <0 indicated the absence of prostate cancer. The performance of the diagnostic index was evaluated in the validation set, and the performance of the model was tested in other solid cancers.

A model was constructed to discriminate prostate cancer from the other cancer types. Candidate miRNAs for model construction were the same as those identified in the discovery set. The residual prostate cancer, NPBx, and other cancer samples were randomly divided into training and validation sets. miRNA combination models were constructed in the training set in the same way, and the performance was evaluated in the validation set.

χ² test for categorical variables or one-way ANOVA for continuous 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 three sample sets. The unpaired t test was used to compare the characteristics (serum PSA and age) of patients with prostate cancer and NPBx. Linear discriminant analysis and model selection based on leave-one-out cross-validation were performed using R version 3.1.2 (R Foundation for Statistical Computing, http://www.R-project.org), compute.es package version 0.2-4, hash package version 2.2.6, MASS package version 7.3-45, mutoss package version 0.1-10, pROC package version 1.8, and STATA version 14 (StataCorp). Unsupervised clustering and heatmap generation using Pearson correlation in Ward method for linkage analysis, and principal component analysis (PCA), were performed using Partek Genomics Suite 6.6. The limit of statistical significance for all analyses was defined as a two-sided P value of 0.05.

The study was approved by the NCCH Institutional Review Board (2015-376, 2016-249) and the Research Committee of Medical Corporation Shintokai Yokoama Minoru Clinic (6019-18–3772). Written informed consent was obtained from each participant. This study was conducted in accordance with the ethical guideline of “Declaration of Helsinki.”

A total of 1,044 prostate cancer and 241 NPBx serum samples were analyzed by miRNA microarray, yielding comprehensive miRNA expression profiles. Among the prostate cancer serum samples, 38 were excluded for lack of patient information, 3 for simultaneous diagnosis of other cancers, 181 for treatment before the collection of serum, and 13 for low-quality microarray results, leaving 809 samples for analysis. Prostate cancer and NPBx samples were randomly classified into discovery, training, and validation sets (Fig. 1A). There were no significant differences in the characteristics listed in Table 1 between the three sample sets.

The discovery set included 41 prostate cancer and 41 NPBx samples. The training and validation sets included 384 prostate cancer and 100 NPBx samples each. In the discovery set, there was no difference in age between patients with prostate cancer, patients with NPBx, and healthy controls (P = 0.44). In the training and validation sets, patients with prostate cancer were older than patients with NPBx (P = 0.001 and 0.014, respectively). Therefore, age-adjusted analysis was performed after the model construction as described below. Serum PSA levels and family history did not differ significantly between prostate cancer and NPBx samples in each of the three sample sets (Supplementary Table S1).

Forty-one healthy male control serum samples and 50 serum samples obtained from each group of men with 10 other solid cancers, including glioma, colorectal adenocarcinoma, esophageal squamous cell carcinoma, lung carcinoma, hepatocellular carcinoma, gastric adenocarcinoma, biliary tract cancer, bone and soft tissue sarcoma, pancreatic cancer, and bladder cancer, were randomly selected from our miRNA database consisting of serum miRNA profiles of more than 15,000 samples.

The expression levels of the miRNAs were analyzed in the discovery set (41 prostate cancer and 41 NPBx samples). A total of 408 miRNAs passed the quality check criteria and were selected (Fig. 1B). PCA mapping with these 408 miRNAs suggested that the miRNA profiles differed between the prostate cancer and NPBx samples (Fig. 1C). We identified 38 miRNAs with a cross-validation score >0.70 between prostate cancer and NPBx in the discovery set (Fig. 1B).

To select cancer-specific miRNAs, 41 healthy male controls were included in the analysis, and the expression levels of the 38 miRNAs were compared between the three sample sets (41 prostate cancer, 41 NPBx, and 41 healthy male control samples). The analysis identified 16 miRNAs that were the most upregulated in prostate cancer and 2 miRNAs that were the most downregulated in prostate cancer (Fig. 1D). Signal values of these 18 miRNAs >2⁶ in more than 50% of prostate cancer or NPBx were confirmed in the training and validation sets (Supplementary Fig. S2).

Fisher linear discriminant analysis was used to design comprehensive discriminants consisting of 1–10 miRNAs in the training set (Supplementary Table S2). On the basis of the cross-validation score, the best combinations for each number of miRNAs were selected (Table 2). On the basis of the AUC reaching the optimal value (≥0.99), a combination of two miRNAs (miR-17-3p and miR-1185-2-3p) was considered as the best model in the training set (diagnostic index = 0.657 × miR-17-3p + 0.385 × miR-1185-2-3p - 6.341; AUC, 0.99; sensitivity, 91%; specificity, 97%). Single miRNAs were also statistically significantly effective in distinguishing patients with cancer (AUC, 0.97 for miR-17-3p; 0.92 for miR-1185-2-3p; Fig. 2). The diagnostic performance of the model was confirmed in the validation set, which showed that the model was accurate (AUC, 0.95; sensitivity, 90%; specificity, 90%; Fig. 2). Because patient age was not matched between prostate cancer samples and NPBx samples, we performed age-adjusted logistic regression analysis in the validation set. The odds ratios of the signal intensity of the two miRNAs and the diagnostic index for the presence of prostate cancer were almost the same before and after adjusting for age (Supplementary Table S3), indicating that the diagnostic index was independently associated with the presence of prostate cancer.

The performance of the diagnostic index for each prostate cancer grade was examined in the validation set. GS and cTNM stage were used to assess the performance of the diagnostic index. The diagnostic index showed high performance for all GS values (GS6, 89%; GS3+4, 91%; GS4+3, 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 GS groups (P < 0.01; Fig. 3).

To investigate whether the serum miRNA profile can distinguish prostate cancer from other solid cancers, we examined the performance of the diagnostic index in other solid cancers. For this purpose, we randomly selected 50 male serum samples from each group of 10 other solid cancers, and comprehensively analyzed the serum miRNA profiles of these solid cancers. The diagnostic index showed a high performance (≥70%) for all 10 solid cancers (Supplementary Fig. S3).

We investigated whether the serum miRNA profile can distinguish prostate cancer from other solid cancers. For this purpose, prostate cancer, NPBx, and the other cancer samples were randomly divided into training and validation sets (Fig. 4A). Using the 18 miRNAs identified in the discovery set, comprehensive discriminants consisting of 1–18 miRNAs were developed in the training set (cancer discrimination model; Supplementary Table S4). On the basis of the optimal level of AUC, a combination of 12 miRNAs (miR-6471-5p, miR-17-3p, 1343-5p, miR-4417, miR-1185-1-3p, miR-1202, miR-422a, miR-6877-5p, miR-6076, miR-3185, miR-320b, and miR-1185-2-3p) was considered as the best discrimination model in the training set [cancer discrimination index = 1.059 × miR-6741-5p + 0.207 × miR-17-3p - 1.432 × miR-1343-5p + 0.918 × miR-4417 + 0.163 × miR-1185-1-3p - 0.408 × miR-1202 - 0.161 × miR-422a - 0.350 × miR-6877-5p + 0.279 × miR-6076 + 0.376 × miR-3185 + 0.131 × miR-320b + 0.338 × miR-1185-2-3p - 7.13; AUC: 0.96; sensitivity: 93%; specificity: 87%]. The diagnostic performance of this model was confirmed in the validation set (AUC: 0.91; sensitivity: 91%; specificity: 78%; Fig. 4B). Although this model was able to discriminate prostate cancer from NPBx, colorectal adenocarcinoma, bone and soft tissue sarcoma, esophageal squamous cell carcinoma, hepatocellular carcinoma with a specificity >80%, it could not successfully distinguish prostate cancer from glioma, gastric adenocarcinoma, lung carcinoma, pancreatic cancer, biliary tract cancer, and bladder cancer (Fig. 4C). Notably, the index of bladder cancer samples was similar to that of prostate cancer samples.

In this study, a comprehensive analysis of serum miRNA expression was performed using samples from 809 prostate cancer and 241 patients with NPBx on a standardized microarray platform (3D-Gene, Toray Industries, Inc.). The results showed that patients with prostate cancer can be accurately distinguished from patients with NPBx according to the serum levels of two miRNAs. To the best of our knowledge, five previous reports demonstrated the potential of circulating miRNAs as diagnostic biomarkers for prostate cancer (13–17). The largest of these studies included 105 patients with prostate cancer (13). In addition, a comprehensive analysis of all 2,588 miRNAs was not performed in these studies. In this study, we examined the expression profiles of 2,588 miRNAs, which constitute all the human miRNAs identified to date according to miRBase rel. 21; this is the largest sample size of prostate cancer reported to date.

The results of this study identified the combination of miR-17-3p and miR-1185-2-3p in the serum as a biomarker for the detection of prostate cancer. The serum expression profile or function of miR-1185-2-3p has not been reported previously. However, miR-17-3p was previously shown to act as an oncogenic miRNA in prostate cancer. Yang and colleagues showed that miR-17-5p and miR-17-3p promote prostate cancer proliferation and invasion by targeting the same protein, namely, tissue inhibitor of metalloproteinase 3 (22). Feng and colleagues reported that the expression levels of miR-17-3p are significantly higher in prostate cancer tissues than in BPH (23). These reports suggest that elevated levels of miR-17-3p in serum are associated with prostate cancer and reflect disease progression. However, in this study, we were unable to identify the origin of the two miRNAs. We need to think the possibility that these miRNAs are not released from cancer cells. Huiping and colleagues reported that miR-17-3p is secreted from immune cells, and serum levels of miR-17-3p may be helpful to predict the therapeutic benefit of trastuzumab in patients with HER2-positive breast cancer (24). Therefore, the upregulation of serum miR-17-3p and miR-1185-2-3p in patients with prostate cancer could be caused by a type of cells other than prostate cancer cells in the tumor microenvironment. Further studies are needed to elucidate the detailed mechanism underlying the upregulation of these miRNAs in patients with prostate cancer.

These results showed that the miRNA profile of prostate cancer is distinct from that of BPH regardless of the clinical TNM stage. In addition, although the diagnostic index of our model did not show complete correlation with the GS, the diagnostic index of low-grade (GS6) prostate cancer was significantly lower than that of high-grade (GS ≥7) prostate cancer. A high GS is associated with more aggressive disease, whereas a low GS is associated with a more indolent disease course. Urologists often use the GS to design personalized treatment strategies for their patients (25). These results indicate that the diagnostic index of our model may help identify patients who would benefit from treatments such as radiotherapy or prostatectomy, although further study is needed to confirm these results.

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

To investigate whether the serum miRNA profile can distinguish prostate cancer from other solid cancers, we developed another model (cancer discrimination model). We confirmed that it is possible to establish a model to distinguish prostate cancer from other types of cancer. However, even this model could not discriminate prostate cancer from bladder cancer, which belong to the same group of urogenital cancers. This is the first report to compare the expression level of serum miRNAs between patients with prostate cancer and those with bladder cancer, and the results suggest the existence of a common mechanism mediating the upregulation of these miRNAs in prostate cancer and bladder cancer. In addition, the sensitivity of the cancer discrimination model was the same as that of the diagnostic model in the validation set. The need for an increased number of miRNAs could increase the detection costs; therefore, the two-miRNA combination model would be cost effective because its performance is adequate as a clinical application to reduce unnecessary prostate biopsy.

The absence of prostate cancer in healthy controls was defined according to the self-reported medical history, and it was not confirmed by pathologic examination. Because the incidence of latent prostate cancer increases with age (28), serum miRNAs from healthy men could not be used as a control group in the discovery and training sets. We therefore analyzed serum samples derived from pathologically confirmed patients to construct the diagnostic model of prostate cancer, and used miRNAs from healthy controls as supportive information to select miRNAs showing higher or lower expression in prostate cancer than in the other sample sets. However, because needle biopsy was mainly performed in patients showing increased PSA levels, the diagnostic power of PSA was low in this study. As miRNA profiling could discriminate patients with prostate cancer and NPBx with a high PSA score, miRNA profiling could be a powerful tool to complement PSA screening, thereby decreasing the number of patients referred for needle biopsy.

This study used retrospectively collected samples; therefore, the storage conditions before microarray analysis were not strictly regulated, which may have affected the results. Indeed, several studies reported that miRNAs are affected by various processes (29, 30). Because direct comparison between NCC Biobank samples and healthy control samples could introduce bias, we did not select biomarker miRNA candidates by comparing miRNAs between prostate cancer and healthy samples. Rather, we used healthy samples to further select the miRNA candidates that were upregulated or downregulated both in NPBx and healthy samples compared with malignant samples. This process allowed the exclusion of certain miRNAs showing alterations in serum levels only in patients with NPBx but not in patients with prostate cancer. In addition, we recently launched a clinical prospective study to validate the general applicability of our data using fresh serum samples, and we will report our results within several years.

In summary, a comprehensive analysis of serum miRNA profiles of 809 cases of prostate cancer and 241 cases of NPBx identified a promising combination of two miRNAs, miR-17-3p and miR-1185-2-3p, for the detection of prostate cancer. This study is the largest scale study performed to date, and the results indicated that evaluation of circulating miRNAs is a feasible method for detecting prostate cancer in men with suspected prostate cancer. The high sensitivity and specificity of this model could help reduce the number of unnecessary biopsies and improve the accuracy of diagnosis.

S. Takizawa is a group leader at Toray Industries, Inc. No other potential conflicts of interest were disclosed by the other authors.

Conception and design: F. Urabe, J. Matsuzaki, K. Kato, T. Ochiya

Development of methodology: T. Hara, S. Takizawa, K. Kato, T. Ochiya

Acquisition 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. Fujimoto

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): F. Urabe, J. Matsuzaki, Y. Yamamoto, Y. Aoki, K. Kato, S. Egawa

Writing, 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. Ochiya

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M. Ichikawa, S. Niida, H. Sakamoto, H. Fujimoto

Study supervision: T. Hara, T. Ochiya

The authors thank Tomomi Fukuda, Takumi Sonoda, Hiroko Tadokoro, Megumi Miyagi, Tatsuya Suzuki, and Kamakura Techno-Science Inc. for performing the microarray assays; Satoshi Kondou for technical support; Noriko Abe for the management of serum samples; Michiko Ohori for the management of personal information; Hitoshi Fujimiya for developing in-house analytic tools; and Kazuki Sudo for independent confirmation of participant eligibility. Some of the samples were obtained from the National Cancer Center Biobank, which is supported by the National Cancer Center Research and Development Fund (29-A-1). Some clinical information was obtained from the Center for Cancer Registries, National Cancer Center. The authors also thank the Biobank at the National Center for Geriatrics and Gerontology for providing biological resources. This study was financially supported through a “Development of Diagnostic Technology for Detection of miRNA in Body Fluids” grant from the Japan Agency for Medical Research and Development (to T. Ochiya).

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