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. 2015 Mar 1;34(3):189–200. doi: 10.1089/dna.2014.2663

Combinations of Serum Prostate-Specific Antigen and Plasma Expression Levels of let-7c, miR-30c, miR-141, and miR-375 as Potential Better Diagnostic Biomarkers for Prostate Cancer

Darina Kachakova 1,, Atanaska Mitkova 1, Elenko Popov 2, Ivan Popov 1, Alexandrina Vlahova 3, Tihomir Dikov 3, Svetlana Christova 3, Vanio Mitev 1, Chavdar Slavov 2, Radka Kaneva 1
PMCID: PMC4337462  PMID: 25521481

Abstract

In the current study, expression levels of let-7c, miR-30c, miR-141, and miR-375 in plasma from 59 prostate cancer (PC) patients with different clinicopathological characteristics and two groups of controls: 16 benign prostatic hyperplasia (BPH) samples and 11 young asymptomatic men (YAM) were analyzed to evaluate their diagnostic and prognostic value in comparison to prostate-specific antigen (PSA). miR-375 was significantly downregulated in 83.5% of patients compared to BPH controls and showed stronger diagnostic accuracy (area under the curve [AUC]=0.809, 95% CI: 0.697–0.922, p=0.00016) compared with PSA (AUC=0.710, 95% CI: 0.559–0.861, p=0.013). Expression levels of let-7c showed potential to distinguish PC patients from BPH controls with AUC=0.757, but the result did not reach significance. Better discriminating performance was observed when combinations of studied biomarkers were used. Sensitivity of 86.8% and specificity of 81.8% were reached when all biomarkers were combined (AUC=0.877) and YAM were used as calibrators. None of the studied microRNAs (miRNAs) showed correlation with clinicopathological characteristics. PSA levels were significantly correlated with the Gleason score, tumor stage, and lymph node metastasis with Spearman correlation coefficients: 0.612, 0.576, and 0.458. In conclusion, the combination of the studied circulating plasma miRNAs and serum PSA has the potential to be used as a noninvasive diagnostic biomarker for PC screening outperforming the PSA testing alone.

Introduction

Prostate cancer (PC) is the most commonly diagnosed male malignancy and the second leading cause of male cancer-related death (Jemal et al., 2010). It is a huge burden for the health system and despite advances in science, the etiology of this disease is not yet fully understood. The only estimated risk factors are age, ethnicity, family history, and diet (Plata Bello and Concepcion Masip, 2014). Hereditary factors account for 42% of PC risk (Lichtenstein et al., 2000). At present, diagnosis of PC is derived from serum prostate-specific antigen (PSA) measurement, digital rectal examination (DRE), and histopathological evaluation of prostate needle biopsies (Gandellini et al., 2010). PSA testing has a low specificity, and the optimal threshold for biopsy is unclear (Thompson et al., 2003). In addition, PSA screening leads to over diagnosis and over treatment of indolent PCs (Dall'Era et al., 2008). On the other hand, many patients with cancer did not have elevated levels of PSA (Loeb and Catalona, 2008). Due to low specificity of PSA and low sensitivity of DRE, these tests have restricted diagnostic value (Backer, 1999). Current clinicopathological models also do not allow clinicians to accurately discern between lethal and indolent PC at an early stage, leading to anxiety for both clinicians and patients about choosing the best treatment course (Albertsen, 1998). Thus, there has been rapidly growing interest in alternative biomarkers such as microRNAs (miRNAs).

miRNAs are a class of 19–23 nucleotide long, endogenous noncoding RNA molecules that are frequently dysregulated in cancer. These miRNAs modulate the activity of transcriptome by binding to the 3′-untranslated regions of target mRNA sequences and leading to mRNA cleavage, decay, or inhibition of translation in various types of cancer (Bartel, 2004, 2009; Garzon et al., 2006). Although miRNAs comprise ∼3% of human encoded genes, more than 30% of mRNAs are regulated by miRNAs (Mahn et al., 2011). They have been shown to play an important role in a wide range of biological and pathological processes, including cellular proliferation, differentiation, metastasis, immune response, metabolism, or apoptosis (Bartel, 2004, 2009; Brase et al., 2010; Mahn et al., 2011; Xu et al., 2011). miRNA expression profiles are often tissue, developmental, and disease specific. Early work demonstrated that miRNA expression signatures are more useful than equivalent mRNA signatures and could accurately distinguish between different tumor types and are able to identify cancers of histologically uncertain origin (Lu et al., 2005). Other useful characteristics of miRNAs for biomarker applications include their exceptional stability in various types of clinical samples, including formalin-fixed paraffin-embedded tissues (Mitchell, 2008), ease of quantitation using polymerase chain reaction (PCR)-based assays, and conservation between species (Selth et al., 2012). miRNAs also show an ability to be sampled noninvasively. Circulating miRNAs are remarkably stable, resisting degradation by ribonucleases (Chen et al., 2008; Mitchell, 2008).

Initially, miRNAs have been studied in normal and tumor tissue, in metastasis, and in PC cell lines (Porkka et al., 2007; Ambs et al., 2008; Bonci et al., 2008; Lin et al., 2008; Ozen et al., 2008; Prueitt et al., 2008; Yamakuchi et al., 2008; DeVere White et al., 2009; Ribas et al., 2009; Coppola et al., 2010; Ribas and Lupold, 2010; Schaefer et al., 2010b; Spahn et al., 2010; Catto et al., 2011; Peng et al., 2011; Takayama et al., 2011; Watahiki et al., 2011; Li et al., 2012a; Martens-Uzunova et al., 2012; Nadiminty et al., 2012; Wach et al., 2012). PC diagnostic and prognostic panels have been identified; however, they are partially overlapping or completely different in various studies. In PC, the efforts for tracing a reliable miRNA profile have proven inconclusive. Conflicting results between different datasets are possibly due to various study designs, underestimated treatments of the patients, methods of sample collection, presence of contaminating cells, and sensitivity and specificity of platforms used (Coppola et al., 2010). In addition, the expression of miRNAs is dynamic and it is changed depending on the prostate carcinogenesis stage, type of therapy, and molecular pathway of every tumor.

To overcome the disadvantages of miRNA studies in tumors, researchers started to analyze miRNA expression in blood samples (plasma, serum) and even urine (Mitchell, 2008; Lodes et al., 2009; Brase et al., 2011; Mahn et al., 2011; Moltzahn et al., 2011; Yaman Agaoglu et al., 2011; Zhang et al., 2011; Bryant et al., 2012; Chen et al., 2012; Shen et al., 2012; Srivastava et al., 2013; Haj-Ahmad et al., 2014).

In the current study, we analyzed the expression of miR-141, miR-375, let-7c, and miR-30c in the plasma of PC samples and controls consisting of benign prostatic hyperplasia (BPH) subjects and young asymptomatic men (YAM) to evaluate their diagnostic and prognostic value in Bulgarian patients. These miRNAs are extensively studied and their expression is correlated with PC development, metastasis, and other clinicopathological characteristics (Mitchell, 2008; Yaman Agaoglu et al., 2011; Nadiminty et al., 2012; Nguyen et al., 2013), but still there is a need for more validation studies in plasma and serum.

The initial study of Mitchell (2008) has shown that miR-141, a miRNA involved in epithelial–mesenchymal transition (EMT), was elevated in the serum of metastatic PC patients compared to healthy controls. The observation that elevated levels of this miRNA in serum and plasma were correlated with clinical progression, high Gleason score, and the development of metastasis in castration-resistant PC (mCRPC) was made in several different studies (Brase et al., 2011; Gonzales et al., 2011; Selth et al., 2012; Bryant et al., 2012; Nguyen et al., 2013; Westermann et al., 2014). However, a following study could not reproduce the significant difference in miR-141 levels and it was suggested that miR-141 levels were too low for reliable testing (Mahn et al., 2011). In other studies it was found that plasma miRNA levels were similar in controls (healthy or BPH controls) and patients but could distinguish patients with metastasis from those with localized/local advanced disease (Yaman Agaoglu et al., 2011; Zhang et al., 2013). On the other hand, none of the analyzed miRNAs, including miR-141 in the study of Yaman Agaoglu et al. (2011), reached the power of PSA to discriminate metastatic PC from localized disease.

Similar to miR-141, circulating miR-375 levels have been correlated to the Gleason score, lymph node status, and distant metastasis (Brase et al., 2011). The amount of miR-375 increases from low-risk through high-risk localized disease toward metastatic cancer (Bryant et al., 2012; Selth et al., 2012; Cheng et al., 2013; Nguyen et al., 2013).

Porkka et al. (2007) have shown that let-7c and miR-30c are with decreased expression in tissues of PC patients compared to tissues from BPH patients. Downregulation of miR-30c in PC tissues was observed in several other studies (Song et al., 2013; Ling et al., 2014; Ren et al., 2014). Independently it was shown that let-7c and miR-30c in plasma are able to discriminate with high sensitivity and specificity the PC patients from BPH controls (Chen et al., 2012). In addition, it was found that downregulation of let-7c and miR-30c in PC tissues and cells was associated with metastatic disease and androgen-dependent PC (Ren et al., 2014). miR-30c expression in tissues showed also correlation with a higher Gleason score, advanced pathological stage, and biochemical recurrence (Ling et al., 2014). It was estimated that downregulation of let-7c in PC specimens is inversely correlated with androgen receptor (AR) expression, whereas the expression of Lin28 (a repressor of let-7) is correlated positively with AR expression (Nadiminty et al., 2012).

Materials and Methods

Participants and biological samples

After obtaining approval from the ethics review board (ethics committee) and informed consent from all study participants, blood samples from 59 PC patients and 16 BPH controls were drawn at the Clinic of Urology, Alexandrovska University Hospital, Medical University-Sofia. Blood samples from YAM were collected from 11 volunteers, mainly medical students. All blood samples were collected in EDTA tubes and processed within 1 h of collection. Blood was centrifuged to separate and collect plasma. Plasma samples were stored at −80°C until RNA extraction.

All subjects in the study were ethnic Bulgarians. BPH controls were matched to PC patients by age. Characteristics of PC patients and the two groups of controls are represented in Table 1.

Table 1.

Participants' Demographic and Clinical Characteristics

Characteristics Patients (59) BPH controls (16) Controls YAM (11)
Median age 68 66 27
Range 51–83 55–88 21–35
Median PSA values 14.6 9.65  
Mean PSA values 19.43 11.24  
PSA values      
 <10 ng/mL 17 8  
 10–30 ng/mL 32 8  
 >30 ng/mL 7 0  
 Unknown 3 0  
Gleason score      
 ≤6 13    
 7 24    
 ≥8 22    
T stage      
 T1 9    
 T2 29    
 T3 18    
 Unknown 3    
Lymph node metastasis      
 N0 50    
 N1 6    
 Unknown 3    
Metastasis      
 M0 55    
 M1 2    
 Unknown 2    
Age at diagnosis (PC or BPH)      
 ≤64 21 8  
 >64 38 8  

BPH, benign prostatic hyperplasia; PC, prostate cancer; PSA, prostate-specific antigen; YAM, young asymptomatic men.

RNA isolation, cDNA synthesis, and real-time PCR

Total RNA was extracted from 200 μL plasma using the miRNeasy mini kit (Qiagen) following the manufacturer's instructions. The RNA was eluted in 40 μL nuclease-free water supplied with the kit. The concentrations and quality of RNA samples were evaluated by NanoDrop and Qubit. For reverse transcription reaction with the miScript II RT kit (Qiagen), 50 ng of each sample were used. Before use, every cDNA was diluted as recommended. Quantitative real-time PCR was done on the ABIPrism 7900HT (Applied Biosystems) with miScript Sybr Green PCR kit and miScript Primer Assays (Qiagen). Similar to other studies, RNU6B was used as reference control for normalization (Schaefer et al., 2010a; Chen et al., 2012; Gordanpour et al., 2012).

Relative changes of gene expression levels of studied miRNAs were calculated by the 2−ΔΔCt method.

Calibrators in the analysis were either BPH controls or YAM. Real-time experiments were performed in triplicates and the mean Ct values were calculated.

Statistical analysis

Statistical analysis was carried out with SPSS Statistics v. 20. The correlation between pairs of miRNA expression levels and PSA levels was evaluated using Pearson's correlation coefficient. The Mann–Whitney U test and Spearman's correlation were used for comparison and estimation of correlations between miRNA expression levels and clinicopathological characteristics such as the Gleason score, tumor stage, lymph node metastasis, and age. Receiver operating curve (ROC) analysis was performed for evaluation of specificity and sensitivity of plasma miRNA expression levels for discriminating PC patients from controls. Diagnostic accuracy for combination of biomarkers was also determined by calculating weight coefficients for every biomarker obtaining the largest possible area under the curve (AUC) in ROC analysis. Calculation of coefficients was performed according Pepe and Thomson (2000). Two-tailed p-values were taken into account.

Results

Relative miRNA expression levels in plasma samples of PC patients were obtained by using RNU6B as a reference gene for normalization and BPH controls or YAM as calibrator samples. Relative quantification (RQ) values calculated by the 2−ΔΔCt method were used for evaluation of expression in patients. RQ values between 0.500 and 1.999 show no significant difference in expression, values≤0.499 show decreased expression, and values≥2.00 show increased expression.

Slightly higher Ct values for the RNU6B reference control in the BPH group in comparison with the PC and YAM groups were observed, but the Mann–Whitney test showed there were no significant differences of Ct values between the studied groups.

All studied miRNAs, especially miR-141 and miR-375, showed downregulation in a high percent of patients in comparison with BPH controls (Fig. 1). Decreased expression of miR-141 and miR-375 was found in 71.43% and 83.05% of PC patients, respectively, compared to the BPH group (Table 2). When using YAM as calibrators, relative gene expression levels in a large proportion of patients did not reach statistically significant difference possibly due to the small sample size of the YAM controls. Results from the relative gene expression analysis for studied miRNAs when using BPH as calibrators are shown in Table 2.

FIG. 1.

FIG. 1.

Box plots representing plasma microRNAs (miRNAs) expression levels in prostate cancer (PC) patients and benign prostatic hyperplasia (BPH) controls. Expression levels of the miRNAs (scale of y axis: log 10) are normalized to RNU6B. BPH samples were used as calibrators.

Table 2.

Results from Relative microRNA Expression Analysis in Plasma Samples of Prostate Cancer Patients and Benign Prostatic Hyperplasia Controls as Calibrators

  let-7c miR-30c miR-141 miR-375
Results: PC patients vs. BPH controls Number of PC patients (%)
Without significant difference in expression 10 (16.95) 13 (22.03) 11 (19.64) 1 (1.7)
Decreased expression 40 (67.8) 35 (59.32) 40 (71.43) 49 (83.05)
Increased expression 9 (15.25) 11 (18.64) 5 (8.93) 9 (15.25)

It should be noted that expression of miR-141 was not detected in seven samples (four BPH and three PC samples) and the mean Ct (Cq) values among PC patients, BPH controls, and YAM were as follows: 30, 34, 32, respectively.

ROC was constructed to explore the potential value of analyzed miRNA expression levels as noninvasive diagnostic biomarkers for PC (Fig. 2). The miR-375 allowed most accurate discrimination (AUC=0.809, 95% CI: 0.697–0.922, p=0.00016) of cancer patients and BPH control subjects. At the optimal cutoff values of RQ, the sensitivity was 81.3% and specificity was 72.9%. miR-375 outperformed PSA serum levels (AUC=0.710, 95% CI: 0.559–0.861, p=0.013 with 76.8% sensitivity and 53.3% specificity at a cutoff value of 9.15 ng/mL) as the diagnostic biomarker in our study. We have chosen 9.15 ng/mL as the cutoff value for PSA because in the studied group of patients and BPH controls, only one person showed a PSA level under 4 ng/mL and the mean levels were high in the BPH group. At the cutoff value of 4 ng/mL the sensitivity was 100% but the specificity was barely 6.2%.

FIG. 2.

FIG. 2.

Receiver operating curve (ROC) curve analysis by using four miRNAs and prostate-specific antigen (PSA) to differentiate PC (n=59) from controls without cancer: (A) when BPH group (n=16) was used as calibrator and (B) when young asymptomatic men (YAM) group (n=11) was used as a calibrator.

let-7c could also discriminate PC patients from BPH controls with the following AUC=0.757 (95% CI: 0.622–0.893, p=0.069) and with 75% sensitivity and 61% specificity, but the result did not reach statistical significance. We obtained the following AUCs for miR-30c and miR-141: 0.630 (95% CI: 0.475–0.786, p=0.079) and 0.510 (95% CI: 0.296–0.723, p=0.91), respectively. At the optimal cutoff value, the sensitivity and specificity were 62.5% and 42.4% for miR-30c and 50% and 71.2% for miR-141.

Constructed ROC to determine the diagnostic accuracy of these four miRNAs in differentiating PC from BPH and YAM, when using the last as calibrator samples, had smaller areas under the curve: let-7c, 0.626 (95% CI: 0.498–0.755, p=0.626); miR-30c, 0.586 (95% CI: 0.455–0.717, p=0.204); miR-141, 0.567 (95% CI: 0.412–0.722, p=0.353); and miR-375, 0.711 (95% CI: 0.595–0.826, p=0.02). At the optimal cutoff value, the sensitivity and specificity were let-7c, 63% and 61%; miR-30c, 55.6% and 54.2%; miR-141, 56.5% and 57.1%; miR-375, 77.8% and 62.7%.

Expression levels of miR-375 in our study proved to be the most reliable noninvasive biomarker for discriminating PC patients from the two groups of controls.

Some combinations of the studied miRNAs and PSA improved the diagnostic accuracy in ROC analysis. When using BPH as calibrator samples, the best combination of biomarkers with the highest sensitivity was between miR-30c, miR-141, miR-375, and PSA, but the specificity was lower compared with miR-375 alone. The largest AUC was observed when combining let-7c, miR-141, miR-375, and PSA. Unfortunately, the combination of all five biomarkers did not outperform miR-375. Selected results from the multimarker ROC analysis when BPH samples were used as calibrators are shown in Table 3 and Figure 3A.

Table 3.

Selected Results from Multimarker Receiver Operating Curve Curve Analysis When BPH Samples Are Used as Calibrators

Combination of biomarkers AUC (95% CI) p-Value Sensitivity (%) Specificity (%)
let-7c and miR-141 0.753 (0.598–0.908) 0.002 79.7 81.2
miR-141 and miR-375 0.695 (0.510–0.880) 0.017 74.6 75
let-7c and PSA 0.714 (0.560–0.868) 0.012 69.1 60
miR-30c and PSA 0.702 (0.546–0.858) 0.017 65.5 60
miR-141 and PSA 0.699 (0.551–0.846) 0.019 72.7 53.3
miR-375 and PSA 0.833 (0.727–0.938) 8.5×10−5 85.5 66.7
let-7c, miR-30c, and miR-141 0.753 (0.598–0.908) 0.002 79.7 81.2
let-7c, miR-141, and miR-375 0.816 (0.688–0.943) 1.2×10−4 78 87.5
let-7c, miR-375, and PSA 0.838 (0.732–0.943) 7×10−5 70.9 80
let-7c, miR-30c, and PSA 0.719 (0.567–0.870) 0.01 72.7 53.3
let-7c, miR-141, and PSA 0.705 (0.554–0.857) 0.015 65.5 60
miR-30c, miR-375, and PSA 0.833 (0.725–0.940) 8.6×10−5 70.9 80
miR-30c, miR-141, and PSA 0.699 (0.552–0.847) 0.019 65.5 60
miR-30c, miR-141, and miR-375 0.814 (0.686–0.941) 1.3×10−4 76.3 87.5
let-7c, miR-30c, miR-375 0.662 (0.487–0.837) 0.048 71.2 62.5
miR-141, miR-375, PSA 0.834 (0.724–0.944) 8.0×10−5 70.9 73.3
let-7c, miR-30c, miR-141, and miR-375 0.781 (0.635–0.927) 0.01 81.4 81.2
let-7c, miR-141, miR-375, and PSA 0.839 (0.733–0.944) 6.3×10−5 92.7 66.7
let-7c, miR-30c, miR-375, and PSA 0.839 (0.735–0.943) 6.3×10−5 92.7 66.7
let-7c, miR-30c, miR-141, and PSA 0.777 (0.657–0.897) 0.001 80 60
miR-30c, miR-141, miR-375, and PSA 0.836 (0.726–0.947) 7.1×10−5 94.5 66.7
let-7c, miR-30c, miR-141, miR-375, and PSA 0.782 (0.664–0.899) 0.001 63.6 73.3

AUC, area under the curve.

FIG. 3.

FIG. 3.

Multimarker ROC: (A) BPH samples used as calibrators and (B) YAM samples used as calibrators.

Similar patterns were observed when multimarker ROC analysis was performed when YAM were used as calibrators (Table 4 and Fig. 3B). In this analysis, the combination of all 5 biomarkers significantly outperformed miR-375 and the largest AUC was obtained: 0.877.

Table 4.

Selected Results From Multimarker Receiver Operating Curve Curve Analysis When Young Asymptomatic Men Samples Are Used as Calibrators

Combination of biomarkers AUC (95% CI) p-Value Sensitivity (%) Specificity (%)
miR-375 and PSA 0.823 (0.709–0.936) 1.3×10−4 85.7 73.3
let-7c and miR-375 0.701 (0.575–0.828) 0.003 76.3 66.7
let-7c and PSA 0.786 (0.669–0.903) 0.001 73.2 80
miR-30c and miR-375 0.693 (0.564–0.822) 0.004 74.6 70.4
miR-30c and PSA 0.714 (0.562–0.867) 0.011 76.8 53.3
miR-141 and miR-375 0.747 (0.636–0.858) 0.001 76.8 69.6
miR-141 and PSA 0.748 (0.585–0.911) 0.01 62.3 63.6
let-7c, miR-30c, and miR-375 0.702 (0.574–0.830) 0.003 79.7 63
let-7c, miR-30c, and PSA 0.795 (0.680–0.910) 0.0005 78.6 80
let-7c, miR-141, and miR-375 0.714 (0.579–0.848) 0.003 73.2 73.9
let-7c, miR-375, and PSA 0.815 (0.696–0.935) 0.0002 78.6 80
let-7c, miR-141, and PSA 0.811 (0.676–0.947) 0.001 83 72.7
miR-30c, miR-375, PSA 0.824 (0.714–0.934) 0.0001 71.4 80
miR-30c, miR-141, and miR-375 0.688 (0.543–0.832) 0.009 82.1 60.9
miR-30c, miR-141, and PSA 0.768 (0.620–0.917) 0.005 69.8 63.6
let-7c, miR-30c, miR-141, and miR-375 0.769 (0.650–0.889) 0.0002 76.8 78.3
let-7c, miR-141, miR-375, and PSA 0.875 (0.779–0.971) 0.0001 86.8 81.8
let-7c, miR-30c, miR-375, and PSA 0.815 (0.690–0.940) 0.0002 82.1 80
let-7c, miR-30c, miR-141, and PSA 0.835 (0.726–0.945) 0.001 83 72.7
miR-30c, miR-141, miR-375, and PSA 0.858 (0.750–0.965) 0.00021 88.7 72.7
let-7c, miR-30c, miR-141, miR-375, and PSA 0.877 (0.782–0.971) 9×10−5 86.8 81.8

In addition, we investigated whether any of the studied miRNAs were significantly correlated with clinicopathological characteristics like the Gleason score, tumor stage, and development of lymph node metastasis and then we compared the results obtained for PSA. We were not able to check if the expression levels of miRNAs in plasma were correlated with the risk of distant metastasis development because we had only two patients with metastasis. In our study, only PSA showed a statistically significant strong correlation with the Gleason score, tumor stage, and lymph node metastasis with Spearman correlation coefficients: 0.612 (p=5.31×10−7); 0.576 (p=4.21×10−6); and 0.458 (p=4.34×10−4). The observed correlations were also confirmed by the Mann–Whitney U test. We performed ROC analysis to evaluate the serum PSA values as predictors for the presence of lymph node metastasis and obtained the following results: AUC=0.960 (95% CI: 0.908–1.00, p=0.001), and at the cutoff value of 27.1 ng/mL, the sensitivity was 100% and the specificity was 90%.

To check if the expression levels of the studied miRNAs are correlated with age, we performed the Spearman test, appropriate for not normally distributed data. We did not observe statistically significant correlation between age and expression levels of the studied miRNAs within the three groups (PC patients, BPH, and YAM) or as a whole.

The expression levels of let-7c showed statistically significant correlation with the expression levels of miR-30c, miR-141, and miR-375 with the following Pearson's correlation coefficients: 0.650 (p=2.84×10−10), 0.447 (p=5.7×10−5), and 0.266 (p=0.021). Similar correlation was observed for miR-141 and miR-30c (Pearson correlation coefficient 0.371, p=0.001).

Discussion

PC was the first cancer type to be used as a disease model for the establishment of circulating miRNAs' potential as blood-based biomarkers (Mitchell, 2008). Since the work of Mitchell and colleagues, many studies examined circulating miRNAs and have shown that they are correlated with PC development and progression. In several studies, miR-141, miR-375, let-7c, and miR-30c have been analyzed in plasma and/or serum and have shown great potential (Brase et al., 2011; Gonzales et al., 2011; Mahn et al., 2011; Moltzahn et al., 2011; Yaman Agaoglu et al., 2011; Bryant et al., 2012; Chen et al., 2012; Selth et al., 2012; Nguyen et al., 2013).

In our study, the Ct values for miR-141 were high, while expression was not detected in several samples. In consistency to other studies, the levels of miR-141 were similar in patients and YAM (Mahn et al., 2011; Yaman Agaoglu et al., 2011), but this is in contrast to the findings of Mitchell (2008). The discrepancies between studies may be due to differences in miRNA abundance between serum and plasma, different methods used for analysis (for example, inclusion of preamplification step between reverse transcription and real-time PCR to increase the amount of low-abundant miRNAs) and sample size. Another possible explanation is that Mitchell and coworkers compared miRNA levels between metastatic PC and healthy controls. We were not able to check the correlation of miR-141 with metastasis development due to the small number of such patients. In addition, no correlations with other clinicopathological characteristics (Gleason score, tumor stage, lymph node affliction) were observed, due probably to the small sample size and the very low levels in plasma of this miRNA. ROC analysis in our study proved that miR-141 levels could not reliably discriminate patients from BPH and YAM groups (Fig. 2).

The levels of miR-375 had been compared between different groups of PC patients or between mCRPC cases and healthy controls and it was confirmed that circulating miR-375 levels were elevated in mCRPC in comparison to healthy controls or patients with localized PC (Brase et al., 2011; Bryant et al., 2012; Cheng et al., 2013; Nguyen et al., 2013). In the current study, we have compared plasma expression levels of this miRNA in PC patients and two groups of controls (BPH and YAM). We have found significantly decreased expression of miR-375 in 83.05% of PC patients compared to BPH controls (Table 2). In comparison with YAM, 45.76% of the patients showed decreased expression. The discrepancy could be possibly attributed to the composition of the investigated groups. In our group of patients there were only two with metastasis and six with positive lymph nodes and this may be a reason for the observed decreased expression. The expression of miR-375 is downregulated also in gastric cancer (Ding et al., 2010; Tsukamoto et al., 2010), head and neck squamous cell carcinoma (Avissar et al., 2009; Harris et al., 2012), squamous cell carcinoma of the esophagus (Mathe et al., 2009), oral tumors (Jung et al., 2013), nonsmall lung cancer (Li et al., 2012b), hepatocellular tumors (Ladeiro et al., 2008), pancreatic cancer (Zhou et al., 2012), and colorectal cancer (Dai et al., 2012; Faltejskova et al., 2012). In breast cancer, miR-375 is overexpressed in ERα-positive breast cell lines and it is a key driver of their proliferation (de Souza Rocha Simonini et al., 2010). Since the expression of estrogen receptor is elevated in PC development, upregulation of miR-375 could assist tumor progression. In the study of Jung et al., it was found that miR-375 acts as a tumor suppressor in oral cancer and reduces the expression of CIP2A, resulting in a decrease of MYC protein levels and leading to reduced proliferation, colony formation, migration, and invasion (Jung et al., 2013). In PC, MYC also has shown increased expression during tumorogenesis (Ellwood-Yen et al., 2003), in androgen-dependent cancer, and in CRPC (Jenkins et al., 1997; Hawksworth et al., 2010). The mechanism of MYC upregulation in PC is yet not completely revealed, but it is possible that miR-375 plays a role in its regulation as in oral cancer.

ROC analysis in our study showed that downregulation of miR-375 in plasma is the most accurate diagnostic biomarker and can distinguish PC patients from controls with high sensitivity and specificity (Fig. 2). Our study demonstrated that miR-375 levels in plasma could be used not only as a prognostic but also as a diagnostic biomarker. However, we were not able to find statistically significant correlation of miR-375 with clinocopathological characteristics.

The observed downregulation of miR-375 in the current study could be explained not only with the small number of patients with distant metastasis and positive lymph nodes but also with the possibility for the presence of different comorbidities as diabetes, prostatitis, different pathophysiological processes, and androgen deprivation therapy. In addition to the role of miR-375 in various types of cancer, it has also a critical role in the regulation of key pathophysiological mechanisms as glucose metabolism (Lynn, 2009), hepatitis B virus infections (Li et al., 2010), participates in multiple allergic diseases induced by interleukin-13 (IL-13) (Lu et al., 2012), and in the development of diabetes (Poy et al., 2004; Tang et al., 2008; Erener et al., 2013). It was recently found that AR levels are negatively correlated with the methylation-mediated transcriptional repression of miR-375 in human PC cells (Chu et al., 2014). In AR-negative PCs, the level of miR-375 has been low due to the hypermethylation of its promoter related to high DNA methyltransferases activity. In AR-positive PC, the opposite was observed (Chu et al., 2014).

The other miRNAs studied by us are let-7c and miR-30c. Members of let-7 family were found to be downregulated in various types of cancer with few exceptions. Particularly in PC (Chen et al., 2012; Schubert et al., 2013), breast cancer (Cava et al., 2014), ovarian cancer (Dahiya et al., 2008), nonsmall cell lung cancer (Zhao et al., 2014) and in hepatocellular carcinoma (Li et al., 2013), let-7c is downregulated. The functionality of let-7 has been shown to target oncogenes involved in cell cycle regulation, cell migration, proliferation, differentiation, and EMT progression (Johnson et al., 2005; Kumar et al., 2007; Lee and Dutta, 2007; Dong et al., 2010; Kong et al., 2012).

Expression levels of miR-30c are frequently increased in different tumors, including ovarian cancer (Lee et al., 2012), mesothelioma (Busacca et al., 2010), primary cutaneous anaplastic large cell lymphoma (Benner et al., 2012), but are downregulated in breast cancer (Tanic et al., 2012), renal cell carcinoma (Heinzelmann et al., 2011), prostate cancer (Porkka et al., 2007; Chen et al., 2012; Song et al., 2013; Ling et al., 2014; Ren et al., 2014), and bladder cancer (Wang et al., 2010).

In our study, in consistency with other studies, let-7c and miR-30 were downregulated in the majority of patients compared to BPH samples used as calibrators. In contrast, PC patients were equally distributed in groups (with increased, with decreased, or without difference) according to their expression when compared to YAM samples used as calibrators. Therefore, in our study, when YAM were used as calibrators in ROC analysis for all studied miRNAs, we included BPH samples to see if discrimination between PC versus the BPH and YAM group will be improved. ROC analysis when BPH samples were used as calibrators showed that the let-7c levels in plasma had a potential to be used as diagnostic biomarkers for discriminating PC patients from BPH controls with AUC=0.757 and sensitivity of 75% and specificity of 61% (Fig. 2). The specificity was higher and the sensitivity was similar to those obtained from the ROC analysis of PSA. Similar AUC (0.784) for let-7c was obtained by Chen et al. (2012). When YAM were included as calibrators, a smaller AUC (0.626) was derived in ROC analysis for discriminating PC patients from BPH and YAM subjects. Chen and colleagues had a larger AUC (0.775), but their group of healthy controls consisted of older men without BPH and with a mean age of 72 years. Furthermore, they have analyzed a larger group. This could also be a reason for the smaller AUC produced by us in ROC analysis. Neither let-7c nor miR-30c showed correlation with clinicopathological characteristics in our study.

After its approval by the U.S. Food and Drug Administration (FDA) in 1986, the PSA test revolutionized the PC screening and diagnosis landscape. Nonetheless, there are inherent limitations to using the PSA test for PC screening. First, the test may give false-positive or false-negative results. Most men with an elevated PSA level (above 4.0 ng/mL) are not found to have PC; only about 25% of men who undergo a prostate biopsy due to an elevated PSA level actually have PC. Conversely, a negative result may give false assurances that PC is not detected, when in fact a cancer may exist (NCI, 2014).

Overall, the performance of PSA testing as a screening tool for PC is known to be variable. Depending on the PSA cutoff values applied, the specificity and sensitivity of PSA range from 20% to 40% and 70% to 90%, respectively (Prensner et al., 2012). The AUC of the ROC analysis is between 0.55 and 0.70 for the ability of PSA to identify PC (Prensner et al., 2012). AUC values close to 1 belong to biomarkers that are perfect discriminators. Due to a high false-positive rate, PSA screening for PC demonstrates a positive predictive value of only 25–40% (Schroder et al., 2008). One of the main reasons for the low specificity would be that PSA may be elevated as a result of various noncancerous conditions such as infections, trauma, and BPH (Barry, 2001; Lilja et al., 2008). In addition, around 15–20% from men with low levels of PSA (<4.0 ng/mL) have PC and around 15% of them have high Gleason score values (Thompson et al., 2004).

The results from our study are close to those published in the literature. The AUC was 0.710 (95% CI: 0.559–0.861, p=0.013), sensitivity was 76.8%, and specificity was 53.3% at a cutoff value 9.15 ng/mL (Fig. 2). At the cutoff value of 4 ng/mL, the sensitivity was 100%, but the specificity was barely 6.2%. In contrast from the other studies for evaluation of specificity and sensitivity of PSA, we used a higher cutoff value since in our group of BPH samples, the mean PSA levels were 11.24 and there was only one BPH sample with the PSA level under 4 ng/mL.

Several studies have shown that monitoring the PSA values could be used for evaluating the risk for development of aggressive cancer (advanced tumor stage, high Gleason score, and metastasis) in patients with PC (Antenor et al., 2005; Kundu et al., 2007; Ulmert et al., 2008; Vickers et al., 2010; Corcoran et al., 2012). PSA levels together with Gleason score and tumor stage are used to calculate the risk for development of aggressive PC, for occurrence of biochemical relapse after radical prostatectomy in different classificators as D'Amico, CAPRA score (D'Amico et al., 1998; Patel et al., 2007). In line with the studies that have shown correlation of PSA with clinicopathological characteristics, our study shows statistically significant correlation of PSA with Gleason score, tumor stage, and lymph node metastasis with Spearman correlation coefficients: 0.612 (p=5.31×10−7); 0.576 (p=4.21×10−6); and 0.458 (p=4.34×10−4). PSA levels at diagnosis could predict the presence of lymph node metastasis with 100% sensitivity and 90% specificity when 27.1 ng/mL was used as the cutoff value.

ROC analysis was used to further evaluate the joint diagnostic value of the four studied miRNAs and PSA. More powerful diagnostic values were observed when combining miRNAs with or without PSA. The best discrimination between PC patients and the two groups of controls with 86.8% sensitivity and 81.8% specificity was observed when all biomarkers were combined and YAM were used as calibrator samples (Fig. 3). The obtained AUC was 0.877. When BPH samples were used as calibrators, several combinations could make better discrimination between groups in comparison with single biomarkers. When all four miRNAs were combined, AUC was 0.781 and this set could discriminate PC patients from BPH controls with 81.4% sensitivity and 81.2% specificity. Another reliable combination was between miR-30c, miR-141, miR-375, and PSA with AUC=0.836 and sensitivity 94.5% and specificity 66.7%.

To conclude, the use of panels consisting of miRNAs and PSA has advantages over single miRNAs or PSA because different miRNAs have aberrant expression in different subtypes of cancer and PSA is not cancer specific.

Several specific issues and limitations should be pointed out in our study. For the first time, we report downregulation of miR-141 and miR-375 in plasma of PC patients compared to BPH samples. The plausible reasons for this could be heterogeneous by the disease stage group, with only two patients with distant metastasis included. In the other published studies, the comparisons of expression levels of circulating miR-141 and miR-375 were made between PC patients with or without metastasis or between patients with metastasis and healthy controls to differentiate patients at a high risk. In contrast, we compared the expression levels between all patients and controls. Another explanation for the miR-375 results could be that this miRNA plays a role in several pathophysiological processes and is associated not only with cancer but also with diabetes, allergic conditions, and inflammations. Unfortunately, we did not have sufficient data for the comorbidities of all analyzed patients to test this hypothesis as well. In addition, the expression levels of plasma miR-375 and its CpG methylation in the promoter region could be ethnically different, as this was recently demonstrated in a study of type 2 diabetes (Chang et al., 2014). It was also shown that the AR is negatively correlated with the methylation-mediated transcriptional repression of miR-375 in human PC cells (Chu et al., 2014). Thus, any prior hormone treatment of the patients affecting the level of AR would also have influence on the level of miR-375 in human PC cells, hence on the cirluculating miR-375 in plasma.

For miR-141, we have obtained higher Ct values in comparison with the other studied miRNAs, probably due to the lower level of this miRNA in plasma compared with serum and as we have not used a preamplification step.

The second issue is that we did not observe correlation of the studied miRNAs with the clinicopathological characteristics. Such correlations were observed only for PSA. Probably the miRNA expression was not so dramatically influenced by the Gleason score, tumor stage, and the development of lymph node metastasis as serum PSA levels in the investigated samples. Another limitation is the comparatively small size of the investigated groups. A future larger independent study is needed for making more definitive conclusions on this correlation.

Conclusion

We have shown that the expression level of miR-375 in plasma outperformed serum PSA levels as a diagnostic biomarker for PC. let-7c miRNA also showed potential for discrimination of PC patients and controls. Combinations of let-7c, miR-30c, miR-141, miR-375, and PSA obtained even better discrimination and could be more useful that PSA alone as noninvasive diagnostic biomarkers for screening of PC. PSA levels showed correlation with the Gleason score, tumor stage, and lymph node metastasis, but such correlations were not observed for the studied miRNAs. Because of the still conflicting data in the literature, standardized methodologies and larger sample sets are crucial for exploring the clinical potential of circulating miRNAs.

Acknowledgments

We are grateful to all patients and healthy volunteers for their participation in the study. This work was supported by the following grants: DFNI-B01/28/2012 and DUNK01-2/2009, funded by the National Science Fund, Ministry of Education, Youth and Science, Bulgaria; 7D from 2013 funded by Science Fund, Medical University-Sofia.

Disclosure Statement

The authors declare that there are no conflicts of interest.

References

  1. Albertsen P.C. (1998). Competing risk analysis of men aged 55 to 74 years at diagnosis managed conservatively for clinically localized prostate cancer. JAMA 280,975–980 [DOI] [PubMed] [Google Scholar]
  2. Ambs S., Prueitt R.L., Yi M., Hudson R.S., Howe T.M., Petrocca F., et al. (2008). Genomic profiling of microRNA and messenger RNA reveals deregulated microRNA expression in prostate cancer. Cancer Res 68,6162–6170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Antenor J.A., Roehl K.A., Eggener S.E., Kundu S.D., Han M., and Catalona W.J. (2005). Preoperative PSA and progression-free survival after radical prostatectomy for Stage T1c disease. Urology 66,156–160 [DOI] [PubMed] [Google Scholar]
  4. Avissar M., Christensen B.C., Kelsey K.T., and Marsit C.J. (2009). MicroRNA expression ratio is predictive of head and neck squamous cell carcinoma. Clin Cancer Res 15,2850–2855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Backer H. (1999). Prostate cancer screening: exploring the debate. Permanente J 3,330–340 [Google Scholar]
  6. Barry M.J. (2001). Clinical practice. Prostate-specific-antigen testing for early diagnosis of prostate cancer. N Engl J Med 344,1373–1377 [DOI] [PubMed] [Google Scholar]
  7. Bartel D. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136,215–233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bartel D.P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116,281–297 [DOI] [PubMed] [Google Scholar]
  9. Benner M.F., Ballabio E., van Kester M.S., Saunders N.J., Vermeer M.H., Willemze R., et al. (2012). Primary cutaneous anaplastic large cell lymphoma shows a distinct miRNA expression profile and reveals differences from tumor-stage mycosis fungoides. Exp Dermatol 21,632–634 [DOI] [PubMed] [Google Scholar]
  10. Bonci D., Coppola V., Musumeci M., Addario A., Giuffrida R., Memeo L., et al. (2008). The miR-15a-miR-16-1 cluster controls prostate cancer by targeting multiple oncogenic activities. Nat Med 14,1271–1277 [DOI] [PubMed] [Google Scholar]
  11. Brase J.C., Johannes M., Schlomm T., Falth M., Haese A., Steuber T., et al. (2011). Circulating miRNAs are correlated with tumor progression in prostate cancer. Int J Cancer 128,608–616 [DOI] [PubMed] [Google Scholar]
  12. Brase J.C., Wuttig D., Kuner R., and Sultmann H. (2010). Serum microRNAs as non-invasive biomarkers for cancer. Mol Cancer 9,306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bryant R.J., Pawlowski T., Catto J.W., Marsden G., Vessella R.L., Rhees B., et al. (2012). Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer 106,768–774 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Busacca S., Germano S., De Cecco L., Rinaldi M., Comoglio F., Favero F., et al. (2010). MicroRNA signature of malignant mesothelioma with potential diagnostic and prognostic implications. Am J Respir Cell Mol Biol 42,312–319 [DOI] [PubMed] [Google Scholar]
  15. Catto J.W., Alcaraz A., Bjartell A.S., De Vere White R., Evans C.P., Fussel S., et al. (2011). MicroRNA in prostate, bladder, and kidney cancer: a systematic review. Eur Urol 59,671–681 [DOI] [PubMed] [Google Scholar]
  16. Cava C., Bertoli G., Ripamonti M., Mauri G., Zoppis I., Della Rosa P.A., et al. (2014). Integration of mRNA expression profile, copy number alterations, and microRNA expression levels in breast cancer to improve grade definition. PLoS One 9,e97681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chang X., Li S., Li J., Yin L., Zhou T., Zhang C., et al. (2014). Ethnic differences in microRNA-375 expression level and DNA methylation status in type 2 diabetes of Han and Kazak populations. J Diabetes Res 2014,761938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chen X., Ba Y., Ma L., Cai X., Yin Y., Wang K., et al. (2008). Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18,997–1006 [DOI] [PubMed] [Google Scholar]
  19. Chen Z.H., Zhang G.L., Li H.R., Luo J.D., Li Z.X., Chen G.M., et al. (2012). A panel of five circulating microRNAs as potential biomarkers for prostate cancer. Prostate 72,1443–1452 [DOI] [PubMed] [Google Scholar]
  20. Cheng H.H., Mitchell P.S., Kroh E.M., Dowell A.E., Chery L., Siddiqui J., et al. (2013). Circulating microRNA profiling identifies a subset of metastatic prostate cancer patients with evidence of cancer-associated hypoxia. PLoS One 8,e69239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Chu M., Chang Y., Li P., Guo Y., Zhang K., and Gao W. (2014). Androgen receptor is negatively correlated with the methylation-mediated transcriptional repression of miR-375 in human prostate cancer cells. Oncol Rep 31,34–40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Coppola V., De Maria R., and Bonci D. (2010). MicroRNAs and prostate cancer. Endocr Relat cancer 17,F1–F17 [DOI] [PubMed] [Google Scholar]
  23. Corcoran N.M., Casey R.G., Hong M.K., Pedersen J., Connolly S., Peters J., et al. (2012). The ability of prostate-specific antigen (PSA) density to predict an upgrade in Gleason score between initial prostate biopsy and prostatectomy diminishes with increasing tumour grade due to reduced PSA secretion per unit tumour volume. BJU Int 110,36–42 [DOI] [PubMed] [Google Scholar]
  24. D'Amico A.V., Whittington R., Malkowicz S.B., Schultz D., Blank K., Broderick G.A., et al. (1998). Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA 280,969–974 [DOI] [PubMed] [Google Scholar]
  25. Dahiya N., Sherman-Baust C.A., Wang T.L., Davidson B., Shih Ie M., Zhang Y., et al. (2008). MicroRNA expression and identification of putative miRNA targets in ovarian cancer. PLoS One 3,e2436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Dai X., Chiang Y., Wang Z., Song Y., Lu C., Gao P., et al. (2012). Expression levels of microRNA-375 in colorectal carcinoma. Mol Med Rep 5,1299–1304 [DOI] [PubMed] [Google Scholar]
  27. Dall'Era M.A., Cooperberg M.R., Chan J.M., Davies B.J., Albertsen P.C., Klotz L.H., et al. (2008). Active surveillance for early-stage prostate cancer: review of the current literature. Cancer 112,1650–1659 [DOI] [PubMed] [Google Scholar]
  28. de Souza Rocha Simonini P., Breiling A., Gupta N., Malekpour M., Youns M., Omranipour R., et al. (2010). Epigenetically deregulated microRNA-375 is involved in a positive feedback loop with estrogen receptor alpha in breast cancer cells. Cancer Res 70,9175–9184 [DOI] [PubMed] [Google Scholar]
  29. DeVere White R.W., Vinall R.L., Tepper C.G., and Shi X.B. (2009). MicroRNAs and their potential for translation in prostate cancer. Urol Oncol 27,307–311 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ding L., Xu Y., Zhang W., Deng Y., Si M., Du Y., et al. (2010). MiR-375 frequently downregulated in gastric cancer inhibits cell proliferation by targeting JAK2. Cell Res 20,784–793 [DOI] [PubMed] [Google Scholar]
  31. Dong Q., Meng P., Wang T., Qin W., Qin W., Wang F., et al. (2010). MicroRNA let-7a inhibits proliferation of human prostate cancer cells in vitro and in vivo by targeting E2F2 and CCND2. PLoS One 5,e10147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ellwood-Yen K., Graeber T.G., Wongvipat J., Iruela-Arispe M.L., Zhang J., Matusik R., et al. (2003). Myc-driven murine prostate cancer shares molecular features with human prostate tumors. Cancer Cell 4,223–238 [DOI] [PubMed] [Google Scholar]
  33. Erener S., Mojibian M., Fox J.K., Denroche H.C., and Kieffer T.J. (2013). Circulating miR-375 as a biomarker of beta-cell death and diabetes in mice. Endocrinology 154,603–608 [DOI] [PubMed] [Google Scholar]
  34. Faltejskova P., Svoboda M., Srutova K., Mlcochova J., Besse A., Nekvindova J., et al. (2012). Identification and functional screening of microRNAs highly deregulated in colorectal cancer. J Cell Mol Med 16,2655–2666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Gandellini P., Folini M., and Zaffaroni N. (2010). Emerging role of microRNAs in prostate cancer: implications for personalized medicine. Discov Med 9,212–218 [PubMed] [Google Scholar]
  36. Garzon R., Fabbri M., Cimmino A., Calin G.A., and Croce C.M. (2006). MicroRNA expression and function in cancer. Trends Mol Med 12,580–587 [DOI] [PubMed] [Google Scholar]
  37. Gonzales J.C., Fink L.M., Goodman O.B., Jr., Symanowski J.T., Vogelzang N.J., and Ward D.C. (2011). Comparison of circulating MicroRNA 141 to circulating tumor cells, lactate dehydrogenase, and prostate-specific antigen for determining treatment response in patients with metastatic prostate cancer. Clin Genitourin Cancer 9,39–45 [DOI] [PubMed] [Google Scholar]
  38. Gordanpour A., Nam R.K., Sugar L., Bacopulos S., and Seth A. (2012). MicroRNA detection in prostate tumors by quantitative real-time PCR (qPCR). J Vis Exp 63,e3874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Haj-Ahmad T.A., Abdalla M.A., and Haj-Ahmad Y. (2014). Potential urinary miRNA biomarker candidates for the accurate detection of prostate cancer among benign prostatic hyperplasia patients. J Cancer 5,182–191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Harris T., Jimenez L., Kawachi N., Fan J.B., Chen J., Belbin T., et al. (2012). Low-level expression of miR-375 correlates with poor outcome and metastasis while altering the invasive properties of head and neck squamous cell carcinomas. Am J Pathol 180,917–928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hawksworth D., Ravindranath L., Chen Y., Furusato B., Sesterhenn I.A., McLeod D.G., et al. (2010). Overexpression of C-MYC oncogene in prostate cancer predicts biochemical recurrence. Prostate Cancer Prostatic Dis 13,311–315 [DOI] [PubMed] [Google Scholar]
  42. Heinzelmann J., Henning B., Sanjmyatav J., Posorski N., Steiner T., Wunderlich H., et al. (2011). Specific miRNA signatures are associated with metastasis and poor prognosis in clear cell renal cell carcinoma. World J Urol 29,367–373 [DOI] [PubMed] [Google Scholar]
  43. Jemal A., Siegel R., Xu J., and Ward E. (2010). Cancer statistics, 2010. CA Cancer J Clin 60,277–300 [DOI] [PubMed] [Google Scholar]
  44. Jenkins R.B., Qian J., Lieber M.M., and Bostwick D.G. (1997). Detection of c-myc oncogene amplification and chromosomal anomalies in metastatic prostatic carcinoma by fluorescence in situ hybridization. Cancer Res 57,524–531 [PubMed] [Google Scholar]
  45. Johnson S.M., Grosshans H., Shingara J., Byrom M., Jarvis R., Cheng A., et al. (2005). RAS is regulated by the let-7 microRNA family. Cell 120,635–647 [DOI] [PubMed] [Google Scholar]
  46. Jung H.M., Patel R.S., Phillips B.L., Wang H., Cohen D.M., Reinhold W.C., et al. (2013). Tumor suppressor miR-375 regulates MYC expression via repression of CIP2A coding sequence through multiple miRNA-mRNA interactions. Mol Biol Cell 24,1638–1648, S1–S7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kong D., Heath E., Chen W., Cher M.L., Powell I., Heilbrun L., et al. (2012). Loss of let-7 up-regulates EZH2 in prostate cancer consistent with the acquisition of cancer stem cell signatures that are attenuated by BR-DIM. PLoS One 7,e33729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kumar M.S., Lu J., Mercer K.L., Golub T.R., and Jacks T. (2007). Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nat Genet 39,673–677 [DOI] [PubMed] [Google Scholar]
  49. Kundu S.D., Roehl K.A., Yu X., Antenor J.A., Suarez B.K., and Catalona W.J. (2007). Prostate specific antigen density correlates with features of prostate cancer aggressiveness. J Urol 177,505–509 [DOI] [PubMed] [Google Scholar]
  50. Ladeiro Y., Couchy G., Balabaud C., Bioulac-Sage P., Pelletier L., Rebouissou S., et al. (2008). MicroRNA profiling in hepatocellular tumors is associated with clinical features and oncogene/tumor suppressor gene mutations. Hepatology 47,1955–1963 [DOI] [PubMed] [Google Scholar]
  51. Lee H., Park C.S., Deftereos G., Morihara J., Stern J.E., Hawes S.E., et al. (2012). MicroRNA expression in ovarian carcinoma and its correlation with clinicopathological features. World J Surg Oncol 10,174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Lee Y.S., and Dutta A. (2007). The tumor suppressor microRNA let-7 represses the HMGA2 oncogene. Genes Dev 21,1025–1030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Li J., Shi W., Gao Y., Yang B., Jing X., Shan S., et al. (2013). Analysis of microRNA expression profiles in human hepatitis B virus-related hepatocellular carcinoma. Clin Lab 59,1009–1015 [DOI] [PubMed] [Google Scholar]
  54. Li L.M., Hu Z.B., Zhou Z.X., Chen X., Liu F.Y., Zhang J.F., et al. (2010). Serum microRNA profiles serve as novel biomarkers for HBV infection and diagnosis of HBV-positive hepatocarcinoma. Cancer Res 70,9798–9807 [DOI] [PubMed] [Google Scholar]
  55. Li T., Li R.S., Li Y.H., Zhong S., Chen Y.Y., Zhang C.M., et al. (2012a). miR-21 as an independent biochemical recurrence predictor and potential therapeutic target for prostate cancer. J Urol 187,1466–1472 [DOI] [PubMed] [Google Scholar]
  56. Li Y., Jiang Q., Xia N., Yang H., and Hu C. (2012b). Decreased expression of microRNA-375 in nonsmall cell lung cancer and its clinical significance. J Int Med Res 40,1662–1669 [DOI] [PubMed] [Google Scholar]
  57. Lichtenstein P., Holm N.V., Verkasalo P.K., Iliadou A., Kaprio J., Koskenvuo M., et al. (2000). Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 343,78–85 [DOI] [PubMed] [Google Scholar]
  58. Lilja H., Ulmert D., and Vickers A.J. (2008). Prostate-specific antigen and prostate cancer: prediction, detection and monitoring. Nat Rev 8,268–278 [DOI] [PubMed] [Google Scholar]
  59. Lin S.L., Chiang A., Chang D., and Ying S.Y. (2008). Loss of mir-146a function in hormone-refractory prostate cancer. RNA 14,417–424 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Ling X.H., Han Z.D., Xia D., He H.C., Jiang F.N., Lin Z.Y., et al. (2014). MicroRNA-30c serves as an independent biochemical recurrence predictor and potential tumor suppressor for prostate cancer. Mol Biol Rep 41,2779–2788 [DOI] [PubMed] [Google Scholar]
  61. Lodes M.J., Caraballo M., Suciu D., Munro S., Kumar A., and Anderson B. (2009). Detection of cancer with serum miRNAs on an oligonucleotide microarray. PLoS One 4,e6229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Loeb S., and Catalona W.J. (2008). What to do with an abnormal PSA test. Oncologist 13,299–305 [DOI] [PubMed] [Google Scholar]
  63. Lu J., Getz G., Miska E.A., Alvarez-Saavedra E., Lamb J., Peck D., et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435,834–838 [DOI] [PubMed] [Google Scholar]
  64. Lu T.X., Lim E.J., Wen T., Plassard A.J., Hogan S.P., Martin L.J., et al. (2012). MiR-375 is downregulated in epithelial cells after IL-13 stimulation and regulates an IL-13-induced epithelial transcriptome. Mucos Immunol 5,388–396 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Lynn F.C. (2009). Meta-regulation: microRNA regulation of glucose and lipid metabolism. Trends Endocrinol Metab 20,452–459 [DOI] [PubMed] [Google Scholar]
  66. Mahn R., Heukamp L.C., Rogenhofer S., von Ruecker A., Muller S.C., and Ellinger J. (2011). Circulating microRNAs (miRNA) in serum of patients with prostate cancer. Urology 77,1265 e9–e16 [DOI] [PubMed] [Google Scholar]
  67. Martens-Uzunova E.S., Jalava S.E., Dits N.F., van Leenders G.J., Moller S., Trapman J., et al. (2012). Diagnostic and prognostic signatures from the small non-coding RNA transcriptome in prostate cancer. Oncogene 31,978–991 [DOI] [PubMed] [Google Scholar]
  68. Mathe E.A., Nguyen G.H., Bowman E.D., Zhao Y., Budhu A., Schetter A.J., et al. (2009). MicroRNA expression in squamous cell carcinoma and adenocarcinoma of the esophagus: associations with survival. Clin Cancer Res 15,6192–6200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Mitchell P. (2008). Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105,10513–10518 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Moltzahn F., Olshen A.B., Baehner L., Peek A., Fong L., Stoppler H., et al. (2011). Microfluidic-based multiplex qRT-PCR identifies diagnostic and prognostic microRNA signatures in the sera of prostate cancer patients. Cancer Res 71,550–560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Nadiminty N., Tummala R., Lou W., Zhu Y., Zhang J., Chen X., et al. (2012). MicroRNA let-7c suppresses androgen receptor expression and activity via regulation of Myc expression in prostate cancer cells. J Biol Chem 287,1527–1537 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. National Cancer Institute. (2014). Prostate-specific antigen (PSA) test. Available at www.cancer.gov.beckerproxy.wustl.edu/cancertopics/factsheet/detection/PSA (last accessed January8, 2014)
  73. Nguyen H.C., Xie W., Yang M., Hsieh C.L., Drouin S., Lee G.S., et al. (2013). Expression differences of circulating microRNAs in metastatic castration resistant prostate cancer and low-risk, localized prostate cancer. Prostate 73,346–354 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Ozen M., Creighton C.J., Ozdemir M., and Ittmann M. (2008). Widespread deregulation of microRNA expression in human prostate cancer. Oncogene 27,1788–1793 [DOI] [PubMed] [Google Scholar]
  75. Patel A.A., Chen M.H., Renshaw A.A., and D'Amico AV. (2007). PSA failure following definitive treatment of prostate cancer having biopsy Gleason score 7 with tertiary grade 5. JAMA 298,1533–1538 [DOI] [PubMed] [Google Scholar]
  76. Peng X., Guo W., Liu T., Wang X., Tu X., Xiong D., et al. (2011). Identification of miRs-143 and -145 that is associated with bone metastasis of prostate cancer and involved in the regulation of EMT. PLoS One 6,e20341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Pepe M.S., and Thompson M.L. (2000). Combining diagnostic test results to increase accuracy. Biostatistics 1,123–140 [DOI] [PubMed] [Google Scholar]
  78. Plata Bello A., and Concepcion Masip T. (2014). Prostate cancer epidemiology. Arch Esp Urol 67,373–382 [PubMed] [Google Scholar]
  79. Porkka K.P., Pfeiffer M.J., Waltering K.K., Vessella R.L., Tammela T.L., and Visakorpi T. (2007). MicroRNA expression profiling in prostate cancer. Cancer Res 67,6130–6135 [DOI] [PubMed] [Google Scholar]
  80. Poy M.N., Eliasson L., Krutzfeldt J., Kuwajima S., Ma X., Macdonald P.E., et al. (2004). A pancreatic islet-specific microRNA regulates insulin secretion. Nature 432,226–230 [DOI] [PubMed] [Google Scholar]
  81. Prensner J.R., Rubin M.A., Wei J.T., and Chinnaiyan A.M. (2012). Beyond PSA: the next generation of prostate cancer biomarkers. Sci Transl Med 4,127rv3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Prueitt R.L., Yi M., Hudson R.S., Wallace T.A., Howe T.M., Yfantis H.G., et al. (2008). Expression of microRNAs and protein-coding genes associated with perineural invasion in prostate cancer. Prostate 68,1152–1164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Ren Q., Liang J., Wei J., Basturk O., Wang J., Daniels G., et al. (2014). Epithelial and stromal expression of miRNAs during prostate cancer progression. Am J Transl Res 6,329–339 [PMC free article] [PubMed] [Google Scholar]
  84. Ribas J., and Lupold S.E. (2010). The transcriptional regulation of miR-21, its multiple transcripts, and their implication in prostate cancer. Cell Cycle 9,923–929 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Ribas J., Ni X., Haffner M., Wentzel E.A., Salmasi A.H., Chowdhury W.H., et al. (2009). miR-21: an androgen receptor-regulated microRNA that promotes hormone-dependent and hormone-independent prostate cancer growth. Cancer Res 69,7165–7169 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Schaefer A., Jung M., Miller K., Lein M., Kristiansen G., Erbersdobler A., et al. (2010a). Suitable reference genes for relative quantification of miRNA expression in prostate cancer. Exp Mol Med 42,749–758 [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Schaefer A., Jung M., Mollenkopf H.J., Wagner I., Stephan C., Jentzmik F., et al. (2010b). Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer 126,1166–1176 [DOI] [PubMed] [Google Scholar]
  88. Schroder F.H., Carter H.B., Wolters T., van den Bergh R.C., Gosselaar C., Bangma C.H., et al. (2008). Early detection of prostate cancer in 2007. Part 1: PSA and PSA kinetics. Eur Urol 53,468–477 [DOI] [PubMed] [Google Scholar]
  89. Schubert M., Spahn M., Kneitz S., Scholz C.J., Joniau S., Stroebel P., et al. (2013). Distinct microRNA expression profile in prostate cancer patients with early clinical failure and the impact of let-7 as prognostic marker in high-risk prostate cancer. PLoS One 8,e65064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Selth L.A., Townley S., Gillis J.L., Ochnik A.M., Murti K., Macfarlane R.J., et al. (2012). Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease. Int J Cancer 131,652–661 [DOI] [PubMed] [Google Scholar]
  91. Shen J., Hruby G.W., McKiernan J.M., Gurvich I., Lipsky M.J., Benson M.C., et al. (2012). Dysregulation of circulating microRNAs and prediction of aggressive prostate cancer. Prostate 72,1469–1477 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Song H., Liu Y., Pan J., and Zhao S.T. (2013). Expression profile analysis reveals putative prostate cancer-related microRNAs. Genet Mol Res 12,4934–4943 [DOI] [PubMed] [Google Scholar]
  93. Spahn M., Kneitz S., Scholz C.J., Stenger N., Rudiger T., Strobel P., et al. (2010). Expression of microRNA-221 is progressively reduced in aggressive prostate cancer and metastasis and predicts clinical recurrence. Int J Cancer 127,394–403 [DOI] [PubMed] [Google Scholar]
  94. Srivastava A., Goldberger H., Dimtchev A., Ramalinga M., Chijioke J., Marian C., et al. (2013). MicroRNA profiling in prostate cancer—the diagnostic potential of urinary miR-205 and miR-214. PLoS One 8,e76994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Takayama K., Tsutsumi S., Katayama S., Okayama T., Horie-Inoue K., Ikeda K., et al. (2011). Integration of cap analysis of gene expression and chromatin immunoprecipitation analysis on array reveals genome-wide androgen receptor signaling in prostate cancer cells. Oncogene 30,619–630 [DOI] [PubMed] [Google Scholar]
  96. Tang X., Tang G., and Ozcan S. (2008). Role of microRNAs in diabetes. Biochim Biophys Acta 1779,697–701 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Tanic M., Yanowsky K., Rodriguez-Antona C., Andres R., Marquez-Rodas I., Osorio A., et al. (2012). Deregulated miRNAs in hereditary breast cancer revealed a role for miR-30c in regulating KRAS oncogene. PLoS One 7,e38847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Thompson I.M., Goodman P.J., Tangen C.M., Lucia M.S., Miller G.J., Ford L.G., et al. (2003). The influence of finasteride on the development of prostate cancer. N Engl J Med 349,215–224 [DOI] [PubMed] [Google Scholar]
  99. Thompson I.M., Pauler D.K., Goodman P.J., Tangen C.M., Lucia M.S., Parnes H.L., et al. (2004). Prevalence of prostate cancer among men with a prostate-specific antigen level<or=4.0 ng per milliliter. N Engl J Med 350,2239–2246 [DOI] [PubMed] [Google Scholar]
  100. Tsukamoto Y., Nakada C., Noguchi T., Tanigawa M., Nguyen L.T., Uchida T., et al. (2010). MicroRNA-375 is downregulated in gastric carcinomas and regulates cell survival by targeting PDK1 and 14-3-3zeta. Cancer Res 70,2339–2349 [DOI] [PubMed] [Google Scholar]
  101. Ulmert D., Cronin A.M., Bjork T., O'Brien M.F., Scardino P.T., Eastham J.A., et al. (2008). Prostate-specific antigen at or before age 50 as a predictor of advanced prostate cancer diagnosed up to 25 years later: a case-control study. BMC Med 6,6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Vickers A.J., Cronin A.M., Bjork T., Manjer J., Nilsson P.M., Dahlin A., et al. (2010). Prostate specific antigen concentration at age 60 and death or metastasis from prostate cancer: case-control study. BMJ 341,c4521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Wach S., Nolte E., Szczyrba J., Stohr R., Hartmann A., Orntoft T., et al. (2012). MicroRNA profiles of prostate carcinoma detected by multiplatform microRNA screening. Int J Cancer 130,611–621 [DOI] [PubMed] [Google Scholar]
  104. Wang G., Zhang H., He H., Tong W., Wang B., Liao G., et al. (2010). Up-regulation of microRNA in bladder tumor tissue is not common. Int Urol Nephrol 42,95–102 [DOI] [PubMed] [Google Scholar]
  105. Watahiki A., Wang Y., Morris J., Dennis K., O'Dwyer H.M., Gleave M., et al. (2011). MicroRNAs associated with metastatic prostate cancer. PLoS One 6,e24950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Westermann A.M., Schmidt D., Holdenrieder S., Moritz R., Semjonow A., Schmidt M., et al. (2014). Serum microRNAs as biomarkers in patients undergoing prostate biopsy: results from a prospective multi-center study. Anticancer Res 34,665–669 [PubMed] [Google Scholar]
  107. Xu J., Li C.X., Lv J.Y., Li Y.S., Xiao Y., Shao T.T., et al. (2011). Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol Cancer Ther 10,1857–1866 [DOI] [PubMed] [Google Scholar]
  108. Yamakuchi M., Ferlito M., and Lowenstein C.J. (2008). miR-34a repression of SIRT1 regulates apoptosis. Proc Natl Acad Sci U S A 105,13421–13426 [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Yaman Agaoglu F., Kovancilar M., Dizdar Y., Darendeliler E., Holdenrieder S., Dalay N., et al. (2011). Investigation of miR-21, miR-141, and miR-221 in blood circulation of patients with prostate cancer. Tumour Biol 32,583–588 [DOI] [PubMed] [Google Scholar]
  110. Zhang H.L., Qin X.J., Cao D.L., Zhu Y., Yao X.D., Zhang S.L., et al. (2013). An elevated serum miR-141 level in patients with bone-metastatic prostate cancer is correlated with more bone lesions. Asian J Androl 15,231–235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Zhang H.L., Yang L.F., Zhu Y., Yao X.D., Zhang S.L., Dai B., et al. (2011). Serum miRNA-21: elevated levels in patients with metastatic hormone-refractory prostate cancer and potential predictive factor for the efficacy of docetaxel-based chemotherapy. Prostate 71,326–331 [DOI] [PubMed] [Google Scholar]
  112. Zhao B., Han H., Chen J., Zhang Z., Li S., Fang F., et al. (2014). MicroRNA let-7c inhibits migration and invasion of human non-small cell lung cancer by targeting ITGB3 and MAP4K3. Cancer Lett 342,43–51 [DOI] [PubMed] [Google Scholar]
  113. Zhou J., Song S., Cen J., Zhu D., Li D., and Zhang Z. (2012). MicroRNA-375 is downregulated in pancreatic cancer and inhibits cell proliferation in vitro. Oncol Res 20,197–203 [DOI] [PubMed] [Google Scholar]

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