Two human PCa cell lines (PC3 and LNCaP) and an immortalized prostate epithelial cell line (RWPE-1) were obtained from the American Type Culture Collection (Manassas, VA, USA). All cell lines were cultured in RPMI 1640 medium (Gibco Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS; Thermo Fisher Scientific, MA, USA), 100 U/ml penicillin, and 100 µg/ml streptomycin in an incubator with 5% CO₂ at 37°C.

Urine samples from 193 participants [112 PCa patients, 55 benign prostatic hyperplasia (BPH) patients, and 26 healthy donors (HDs)] were collected in the Second Affiliated Hospital of Zhejiang University School of Medicine. Approval was obtained from the Second Affiliated Hospital of Zhejiang University School of Medicine Ethical Committee before initiating the study. Detailed information on the patients is summarized in Supplementary Table S1 . All methods were performed in accordance with the relevant guidelines and regulations. All the patients met the following inclusion criteria: (1) undergoing prostate biopsy for the first time, (2) three-dimensional size of the prostate available via transabdominal ultrasonography before biopsy, (3) blood tests performed within 1 week before biopsy, (4) complete clinical and pathological data available, (5) absence of acute prostatitis or systemic inflammatory disease, (6) absence of urinary tract infection, (7) no history of prostate surgery, (8) no history of 5-alpha reductase inhibitor use, and (9) no anti-inflammatory drug use within 2 weeks before blood tests. Initial voided urine (5–10 ml) was prospectively collected from patients at the time of day most convenient to the person before prostate biopsy. Matched urine samples were collected from PCa patients prior to (n = 10) and a week after local treatment by radical prostatectomy (n = 10).

EVs were isolated from cell culture medium by ultracentrifugation as previously described (20). Briefly, when 70%–80% confluency was reached, cells were washed twice with phosphate-buffered saline (PBS; pH7.0) and then incubated for 48 h in FBS-free medium. Cell culture medium was collected and subjected to consecutive centrifugation steps (300 × g for 10 min and 2,000 × g for 20 min) to remove dead cells and cellular debris. The supernatant was vacuum filtered using a 10-kDa centrifugal filter (Merck Millipore, Darmstadt, Germany), and EV concentrates were ultracentrifuged at 100,000 × g for 70 min at 4°C (Type 70 Ti Fixed-angle Titanium Rotor, k factor = 157.4) (Optima™ XP ultracentrifuge; Beckman Coulter, Indianapolis, IN, USA). Pellets were washed with PBS followed by ultracentrifugation at the same speed and time. The supernatant was discarded, and the resulting EV pellets were suspended in PBS and stored at -80°C.

In order to obtain uEVs, urine samples from patients with PCa, BPH and HDs were centrifuged at 3,000 × g for 20 min at 4°C to remove debris and then ultracentrifuged at 200,000 × g for 2 h at 4°C. The supernatant was removed, and the uEV pellets were resuspended in PBS and stored at -80°C.

Transmission electron microscope (TEM) was used to investigate the morphology of the EVs isolated by ultracentrifugation. Briefly, EVs at an optimal concentration were first placed on 400 mesh carbon/formvar-coated grids and allowed to be absorbed on formvar for a minimum of 10 min. Next, the grids (membrane side down) were transferred to a 50-μl drop of 2.5% glutaraldehyde for 5 min, after which they were transferred to a 100-μl drop of distilled water and were left to stand for 2 min. This process was repeated nine times for a total of 10 water washes. Then, the sample was loaded on the grid and stained by 4% uranyl acetate for 10 min and 1% methylcellulose for 5 min. The remaining water was removed using filter paper. Finally, the samples were viewed using a Tecnai Bio Twin TEM (FEI, Hillsboro, OR, USA), and images were obtained using an AMT CCD camera (Advanced Microscopy Techniques, Woburn, MA, USA).

The concentration and the size distribution of EVs were analyzed by nanoparticle tracking analysis (NTA) using a ZetaView instrument (Particle Metrix, Inning am Ammersee, Germany) and the NanoSight LM10 microscope (NanoSight Ltd., Amesbury, UK) configured with a 405-nm laser. Videos were collected and analyzed using the NTA software (version 2.3) with the default setting of the minimal expected particle size, minimum track length, and blur. Each EV sample was vortexed and diluted with particle-free PBS to obtain the recommended 25–100 particles/frame of the NTA system. Five videos of typically 60-s duration were recorded to generate replicate histograms that were averaged.

Cells and EVs were lysed in radioimmunoprecipitation assay (RIPA) Lysis Buffer (Beyotime Biotechnology, Shanghai, China) for 30 min on ice, and the protein concentration was measured by the Enhanced BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China). And then, the lysates were mixed with loading buffer and heated to 100°C for 10 min. Subsequently, the samples were electrophoretically separated on an 8% sodium dodecyl sulfate–polyacrylamide gel electrophoresis(SDS-PAGE) and electro-transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, Carlsbad, CA). After blocking for 2 h at 25°C in Tris-buffered saline with 0.05% Tween-20 (TBST) and 5% non-fat dry milk, the membranes were incubated overnight at 4°C with the primary antibodies in TBST containing 5% BSA. The following antibodies were used for Western blot (WB) analysis, including anti-Alix antibody (1:1,000; ab88388; Abcam, Cambridge, MA, USA), anti-Calnexin antibody (1:200; ab238078; Abcam, Cambridge, MA, USA), anti-CD63 antibody (1:300; ab8219; Abcam, Cambridge, MA, USA), anti-EpCAM antibody (1:200; ab218448; Abcam, Cambridge, MA, USA), anti-CD9 antibody (1:300; sc-13118; Santa-Cruz Biotechnology, Santa Cruz, CA, USA), anti-beta Actin antibody (1:5,000; ab6276; Abcam, Cambridge, MA, USA), and anti-Apo antibody (1:500; ab66379; Abcam, Cambridge, MA, USA). Thereafter, the membrane was washed and immersed into horseradish peroxidase (HRP)-conjugated secondary antibodies (Jackson ImmunoResearch, Suffolk, UK) for 2 h at 25°C. Chemiluminescent detection of bands was performed using Clarity Western ECL Substrate Kit (Bio-Rad Laboratories, Inc., Hercules, CA, USA), and the signals were visualized using the Quantity One Imaging Software from Bio-Rad according to the manufacturer’s instructions. In order to quantify the levels of EpCAM-CD9-positive EVs from WB analysis and investigate the association with the chemiluminescent signals by our immunoassay, we rationally defined the EpCAM-CD9 protein density (Density_EpCAM-CD9): Density_EpCAM-CD9 = Density_EpCAM × Density_CD9, where the value of Density_EpCAM and Density_CD9 can be quantitatively obtained from WB images using Quantity One Imaging Software. This definition was based on the hypothesis that all the EVs expressing EpCAM and CD9 were sufficiently captured and detected by the antibody sets of our immunoassay, and the chemiluminescent signals of each EV captured by EpCAM antibody can be multiplied by CD9 antibody. Our results showed that Density_EpCAM-CD9 was correlated highly with chemiluminescent signals (r = 0.8395, 95% CI: 0.6317–0.9348, P < 0.0001) ( Supplementary Figure S2A ).

The expression of CD9 and EpCAM on EVs were analyzed by flow cytometry as previously described (21). Briefly, EVs attached to 4 μm aldehyde/sulfate latex beads (Invitrogen, Carlsbad, CA, USA) were incubated with anti-CD9 antibodies (SAB4700092; Sigma-Aldrich, St. Louis, MO), anti-CD63 antibodies (ab1318; Abcam, Cambridge, MA, USA), anti-CD81 antibodies (ab79559; Abcam, Cambridge, MA, USA), or anti-EpCAM antibodies (ab187372; Abcam, Cambridge, MA, USA) for 30 min with rotation at 4°C followed by Alexa-488-tagged secondary antibodies (Life Technologies, Carlsbad, CA, USA) for 30 min with rotation at 4°C. Samples were detected using CytoFLEX Flow Cytometer (Beckman Coulter, Brea, CA, USA) and data were analyzed using CytExpert (Beckman Coulter, Brea, CA, USA).

According to the manufacturer’s instructions, the concentration and the protein amount of EVs were measured by bicinchoninic acid (BCA) assay using Enhanced BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China) and a spectrophotometer (Bio-Rad Laboratories, Inc., Hercules, CA, USA) set to 562 nm.

The urinary creatinine was measured with Roche-developed assays for creatinine (CRE2U, ACN 8152) using a Roche Cobas 8000 Modular Analyzer (Roche, Woerden, Netherlands) according to the manufacturer’s instructions. The automated chemiluminescent microparticle immunoassay analyzer ARCHITECT i2000 (Abbott Laboratories, Abbott Park, IL, USA) was used following the manufacturer’s protocols to determine the concentrations of PSA protein in serum samples.

Cells were seeded and cultured in RPMI 1640 medium, which contains glucose and amino acids for 24 h. The medium was discarded, and then cells were washed once with PBS to remove trace serum. The cells were further cultured in serum-free RPMI 1640 medium under normoxia (21% O₂) to suffer serum starvation or cultured in RPMI 1640 medium supplemented with 10% FBS under hypoxic conditions (1% O₂) to suffer hypoxia for the indicated time periods.

EVs were detected by a newly developed paramagnetic particle-based sandwich CLIA ( Figure 1A ).

For EVs from the cell line supernatant, 100 μl EVs were mixed with 50 μl ACE-labeled anti-CD9 antibodies (1.320 μg/ml) and biotin-labeled anti-EpCAM antibodies (4.000 μg/ml). After incubation for 1 h at 25°C, the mixtures are incubated with 50 μl turbid liquid containing 4 mg/ml avidin-coated magnetic beads for another 30 min, followed by thorough washing of the magnetic beads under an external magnetic field. Finally, magnetic beads with ACE-labeled anti-CD9 antibodies are mixed with trigger solution for chemiluminescent signal excitation. All the measurements are performed in triplicate. EV_EpCAM-CD9 secretion index was calculated to describe the average amount of EV_EpCAM-CD9 secreted per PC3 cell. EV_EpCAM-CD9 secretion index = V_s × Con _EV/N _cell, where V_s (μl) is the volume of the PC3 cell line supernatant, Con _EV (particles/μl) is the concentration of EV_EpCAM-CD9 derived by PC3 cells in the supernatant, and N_cell corresponds to the number of the PC3 cells.

For EVs from the urine samples, each step was the same as the EVs from the cell line supernatant, except the concentration of the ACE-labeled anti-CD9 antibodies (0.132 μg/ml). To avoid urine sampling variance, uEV_EpCAM-CD9 concentrations were normalized by urinary creatinine. We herein rationally defined “n.u.”: n.u. = Con EV/Cr, where Con EV (g/L) corresponds to the concentration of EV_EpCAM-CD9 in the urine samples and Cr (g/L) corresponds to the urinary creatinine of the urine samples, to compare uEV_EpCAM-CD9 concentrations between patients with PCa and without PCa better.

Continuous variables were presented as mean ± standard deviation (SD) or median [interquartile range (IQR)] and compared with each other by Student’s t-test or Mann–Whitney U test. Categorical variables are presented as rate and compared using the chi-square test or the Fisher’s exact test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of EpCAM-CD9-positive EVs, PSA, and models. Decision curve analysis (DCA) was used to compare the diagnostic benefits of different biomarkers and models for PCa. P-values lower than 0.05 were considered statistically significant. All analyses were undertaken with GraphPad Prism version 8.0, SPSS Statistics 20, and R version 2.10.1 (R Foundation for Statistical Computing; http://www.R-project.org).

In this study, we used PC3-derived EVs to construct and optimize the CLIA. Standard characterization of EVs was performed using TEM, NTA, and WB analysis ( Figures 1B–D ). EVs showed characteristic cup-shaped morphology under TEM and showed a mean size of 175.9 ± 6.3 nm (standard error; SD: 78.6 ± 11.1 nm) by NTA. The EV fraction from PC3 was enriched in CD63, CD9, and ALIX, the common biomarkers of EVs, but did not contain calnexin and APO, the negative control of EVs, compared to the PC3 cell lysates (20). In addition, PC3-derived EVs were positive for EpCAM, an epithelial cell marker. Moreover, flow cytometry assay identified that approximately 80% of EVs released by PC3 carried EpCAM and CD9 ( Figures 1E–G ). These results indicated that EpCAM and CD9 were enriched on the membrane of EVs from the prostate cell line PC3 and EVs can be effectively captured by anti-EpCAM antibody-conjugated magnetic beads and successfully detected by ACE-labeled anti-CD9 antibodies.

We performed an ultrasensitive CLIA to quantify EV_EpCAM-CD9 ( Figure 1A ). Noteworthy, although several conventional surface markers (e.g., CD9, CD63, and CD81) are used for EV analysis, we selected CD9 as our detection antibody for EVs. The expression of CD9, CD63, CD81, and EpCAM on PC3-derived EVs were analyzed by flow cytometry in our study, showing that EVs carrying CD9, CD63, CD81, and EpCAM accounted for 81.42%, 82.08%, 67.19%, and 79.59% of total PC3-derived EVs, respectively. Practically, the CLIA employing ACE-labeled anti-CD9 antibody exhibited a superior performance over ACE-labeled CD63 antibody or ACE-labeled CD81 antibody (data not shown). This assay exhibited remarkable chemiluminescent signals for PC3-derived EVs, while the four control groups (non-EVs, non-streptavidin-labeled magnetic beads, non-biotin-labeled anti-EpCAM antibodies, and non-ACE-labeled anti-CD9 antibodies) presented negligible chemiluminescent signals ( Figure 2A ). By contrast, a significant reduction in the relative chemiluminescent unit (RCU) was observed after the addition of Triton X-100, a detergent to lyse EVs ( Figure 2B ) (22). These results strongly demonstrated the feasibility of the assay for selectively detecting EVs.

Next, we systematically optimized the reaction conditions of the EV assay, including the concentration of streptavidin-labeled magnetic beads, biotin-labeled anti-EpCAM antibodies, ACE-labeled anti-CD9 antibodies, and reaction time ( Supplementary Figures S1A–D ). To further investigate the quantitative performance of the EV assay, isolated PC3-derived EVs by ultracentrifugation were quantified using the EV assay based on the concentrations obtained by NTA. As shown in Figure 2C , the RCU value was found to greatly depend on the concentration of EVs, with a good linearity range ranging from 5.50 × 10⁴ to 8.80 × 10⁵ particles/μl (R² = 0.9823). The limit of detection (LOD) calculated as three times of SD above the background (negative control) was 2.86 × 10⁴ particles/μl. Moreover, PC3-derived EVs were quantified by a standard procedure of a recovery test to evaluate the accuracy of the EV assay. The recovery rates of low, medium, and high concentrations of EVs were 85.45%, 95.45%, and 101.65%, respectively ( Supplementary Table S2 , left panels). In addition, three different concentrations of EVs were tested to evaluate the repeatability of the EV assay. The intra-assay coefficient of variation (intra-CV) and the inter-assay coefficient of variation (inter-CV) were less than 10% and 20%, respectively ( Supplementary Table S2 , left panels). The above results suggested an excellent analytical performance of our EV assay.

Then, we asked whether the EV_EpCAM-CD9 could be used to infer the prostatic cell types, e.g., PCa cell lines (PC3 and LNCaP) and benign prostate epithelial cell line (RWPE-1). Hence, we obtained the EVs from the culture supernatant by ultracentrifugation and quantified the concentrations by our assay. As shown in Figure 2D , the concentrations of EVs derived from human PCa cell lines such as PC3 and LNCaP were significantly higher than that of the BPH cell line RWPE-1, which were consistent with the EV Density_EpCAM-CD9 from corresponding cell lines. Moreover, FBS-derived EVs exhibited negligible chemiluminescent signals in the assay. These results revealed that the concentration of EV_EpCAM-CD9 can be a potential indicator for distinguishing cancerous cells from normal ones.

In the course of tumor expansion, cancer cells within the tumor microenvironment often have restricted access to nutrients and oxygen and thus were subjected to starvation and hypoxia (23). Previous reports have demonstrated that the levels of EVs carrying tumor-related proteins can be significantly elevated under such microenvironment, contributing to the regulation of tumor microenvironment, thus promoting tumor initiation, progression, and metastasis (24). However, EV_EpCAM-CD9 derived from PCa cells under tumor microenvironment, which may be diagnostically beneficial in reflecting the pathological stage during PCa development, remained unknown.

Herein, we defined EV_EpCAM-CD9 secretion index to describe the average amount of EV_EpCAM-CD9 secreted per PC3 cell. As shown in Figure 2E , the EV_EpCAM-CD9 secretion index was gradually elevated at the early stage of cell growth due to the initial activation of the cells in the latent phase. At 24–48 h, the PC3 cells entered the logarithmic growth phase, and the equative rate of increase between the amount of EV_EpCAM-CD9 and PC3 cells resulted in a constant EV_EpCAM-CD9 secretion index. Interestingly, however, when the cell reached the stationary phase after 48 h, the EV_EpCAM-CD9 secretion index started increasing again. This may be a result of the inadequate living conditions in the microenvironment. Accordingly, we investigated the impact of some conditions (e.g., hypoxia and serum starvation) involved in such microenvironment on the EV_EpCAM-CD9 secretion index. We observed higher EV_EpCAM-CD9 secretion indexes when the PC3 cells were cultured under hypoxia and serum starvation compared with the controls ( Figures 2F, G ). Additionally, this trend can be reversed upon the treatment of the EV biogenesis inhibitor, such as GW4869 ( Figure 2H ). These results strongly support our hypothesis that EV_EpCAM-CD9 can be a potential indicator in revealing the pathological status of PCa.

Urine can harbor PCa cell-derived EVs, as mentioned above. We thus investigated whether the urinary EpCAM-CD9-positive EVs (uEV_EpCAM-CD9) can be detected using our EV assay. As shown in Supplementary Figure S2B , the protein profile from urine revealed the presence of EpCAM and CD9-positive EVs in PCa. Using a well-adopted EV protein assay, WB, uEV_EpCAM-CD9 from less than 2 ml of urine volume was almost undetectable ( Figure 3A ). However, the uEV_EpCAM-CD9 from even down to 125 μl of urine volume could be successfully detected by our CLIA, and the levels of uEV_EpCAM-CD9 in the same urine volume were statistically distinguishable between the pooled samples from PCa and healthy controls ( Figures 3B, C ). In view of the significant differences between the cell supernatant and urine in the concentration and proportion of EV_EpCAM-CD9, we optimized the methodology again. Additionally, in the clinical laboratory, it is not suitable to quantify EVs by NTA due to the requirement of the specialized equipment. And NTA may be biased toward certain particle size ranges (especially 50–150 nm), and large EVs (>400 nm) and very small EVs (<50 nm) are not well quantified by NTA. We thus used a simple and low-cost protein assay, BCA, as an alternative for EV quantification (25). As described in the Materials and Methods, we optimized the concentration of CD9 antibody and restructured the standard curve and corresponding performance evaluation. As shown in Figure 3D , the RCU value was found to greatly depend on the concentration of EVs, with a good linearity range ranging from 1.25 × 10^-3 to 20.00 × 10^-3 g/L (R² = 0.9745) and a low detection limit, 0.60 × 10^-3 g/L. The recovery test and repeatability test both performed excellently especially at the low level of uEV_EpCAM-CD9 ( Supplementary Table S2 , right panels). Furthermore, uEV_EpCAM-CD9 from nine randomly selected donors including five PCa and four HDs was assayed by the CLIA and WB, which suggested a significant elevation of uEV_EpCAM-CD9 in PCa compared with HD ( Figure 3E and Supplementary Figures S2C, D ).

In the validation experiment, urine samples from a total of 193 participants were further enrolled, including 112 PCa patients, 55 BPH patients, and 26 HDs. Complete datasets were available in 193 men who underwent the first transrectal ultrasound (TRUS)-guided prostate biopsy, and the histologic subtypes of all the 112 PCa patients were identified as prostate adenocarcinoma and without any metastatic sites confirmed by computed tomography examinations. The clinical characteristics of all the participants were listed in Supplementary Table S1 . A remarkably higher level of uEV_EpCAM-CD9 was observed from men with PCa (1.46, IQR 0.86-2.66) than men without PCa (0.55, IQR 0.22-0.84) ( Figure 3F ). ROC curve showed that the diagnostic sensitivity and specificity of uEV_EpCAM-CD9 was 66.07% and 91.36%, respectively (cutoff value: 1.130), and the area under the curve (AUC) was 0.821 (P < 0.0001), while the diagnostic sensitivity and specificity of PSA was 95.54% and 60.49%, respectively (cutoff value: 4.015), and the AUC was 0.897 (P < 0.0001) ( Figure 3G ). Moreover, there was a statistically significant correlation between uEV_EpCAM-CD9 and Gleason grades in PCa patients (r = 0.215, 95% CI: 0.025–0.389, P = 0.023). Significant decreases of uEV_EpCAM-CD9 were observed after prostatectomy in 20 PCa patients ( Figure 3H ). It also showed that uEV_EpCAM-CD9 levels were positively associated with PSA (r = 0.402, 95% CI: 0.272–0.517, P < 0.0001), which was an important indicator for the diagnosis of PCa ( Figure 3I ).

Due to the results that uEV_EpCAM-CD9 has high specificity and low sensitivity, while PSA is just the opposite ( Figure 3G ), we consider building a model combining uEV_EpCAM-CD9 and PSA to better diagnose PCa. The training dataset (n = 116) and validation dataset (n = 77) had an even distribution in patient characteristics ( Table 1 ). The predictive value of the uEV_EpCAM-CD9 was analyzed using a logistic regression model. The odds ratio (OR) for each clinical factor and/or covariate in training sets was assessed by univariate logistic regression modeling. Age, uEV_EpCAM-CD9, PSA, fPSA, f/T PSA, prostate volume (PV), and prostate-specific antigen density (PSAD) were statistically significant predictors of PCa (P < 0.001) on univariate logistic regression analysis ( Table 2 , left panels). Then, we compared varieties of multivariate diagnostic models employing different combinations of the variables assessed by their AUC in ROC curve analysis variables ( Supplementary Table S3 ). The optimal multivariate model for diagnosing PCa should be selected on the basis of the complexity (numbers of variables) and prediction efficiency (AUC); we rationally selected the multivariate model containing the variable age, smoking, drinking, family history, BMI, uEV_EpCAM-CD9, PSA, and PV as the final diagnostic model. The OR of each variable from the multivariate logistic regression analysis was presented in Table 2 (right panels).

The nomogram was constructed according to the results of multivariate logistic regression ( Figure 4A ). In the ROC curve analysis, the AUC of the combined PCa diagnostic model was increased to 0.952 in the training set ( Figure 4B ). Moreover, the multivariate diagnostic model was perfectly in the internal validations, as the calibration curve showed good agreement between prediction and observation ( Figure 4C ). On DCA, by combining uEV_EpCAM-CD9 with other clinical parameters, the combination model to predict PCa added more clinical overall benefit than that of uEV_EpCAM-CD9 only ( Figure 4D ). When applied to the validation test, the model achieved an AUC of 0.947 (P < 0.0001) ( Figure 4B ). The AUC value revealed the high performance of PCa diagnosis using the combined nomogram. Additionally, in patients with PSA gray zone (4–10 ng/ml) including 23 PCa and 31 BPH, the model based on uEV_EpCAM-CD9 showed a better diagnostic performance (AUC = 0.917, P < 0.0001) than the uEV_EpCAM-CD9 only (AUC = 0.887, P < 0.0001) and the traditional biomarkers PSA (AUC = 0.712, P = 0.0018) ( Figure 4E ).