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Characterization of Kallireins and microRNAs in Urine Sediment for the Discrimination of Prostate Cancer from Benign Prostatic Hyperplasia | OMICS International
ISSN: 1948-5956
Journal of Cancer Science & Therapy

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Characterization of Kallireins and microRNAs in Urine Sediment for the Discrimination of Prostate Cancer from Benign Prostatic Hyperplasia

Maria G. Egidi1#, Gabriella Guelfi2#, Giovanni Cochetti1*, Giulia Poli1, Francesco Barillaro1, Danilo Zampini2, Luca Mechelli2 and Ettore Mearini1

1Department of Surgical and Biomedical Sciences, Institution of Urological, Andrological Surgery and Minimally Invasive Techniques, University of Perugia, Loc. S. Andrea delle Fratte, 06156 Perugia, Italy

2Department of Veterinary Medicine, University of Perugia, Via San Costanzo 4, 06126 Perugia, Italy

#These authors contributed equally to the work

*Corresponding Author:
Giovanni Cochetti
Department of Surgical and Biomedical Sciences
Institution of Urological
Andrological Surgery and Minimally Invasive Techniques
University of Perugia, Loc. S.Andrea delle Fratte
06156 Perugia, Italy
E-mail: [email protected]

Received date: March 05, 2015; Accepted date: April 23, 2015; Published date: April 25, 2015

Citation: Citation: Egidi MG, Guelfi G, Cochetti G, Poli G, Barillaro F, et al. (2015) Characterization of Kallireins and microRNAs in Urine Sediment for the Discrimination of Prostate Cancer from Benign Prostatic Hyperplasia. J Cancer Sci Ther 7:130-136. doi:10.4172/1948-5956.1000337

Copyright: © 2015 Egidi MG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Objectives: Prostate Cancer (PCa) and Benign Prostatic Hyperplasia (BPH) are frequently coexisting in elderly men. The measurement of serum PSA together with Digital Rectal Examination (DRE) represents the primary diagnostic tool to suspect PCa, whereas definitive diagnosis is achieved by prostate biopsy. The low specificity of PSA and the modest detection rate of biopsy convict the patient to a quite often unnecessary and uncomfortable clinical itinerary. There is a urgent need for new and more accurate methodologies to diagnosize PCa. In the present study, the expression of 4 mRNAs and 2 miRNAs was evaluated in post DRE urine cell pellets from patients suffering PCa and age-matched subjects affected by BPH with elevated PSA levels. We also evaluated the diagnostic accuracy of markers in predicting PCa.

Materials and methods: The expression levels of 4 mRNAs (3 kallikreins - KLK3, KLK11, KLK13 and a prostate cancer antigen - PCA3) and 2 microRNAs (miR-9-3p and miR-19a-3p) were assayed by means of real-time PCR in post DRE urine of 79 men undergoing prostate biopsy for PSA levels > 3 ng/mL. The diagnostic power of tested markers was evaluated through logistic regression analysis.

Results: PCA3 was undetectable in 22 out of 38 BPH subjects. KLK3 and KLK11 were significantly upregulated in PCa group (p value < 0.001), while miR-9-3p and miR-19a-3p were up-regulated in BPH group (p value <0.001 and < 0.01, respectively). KLK13 was not differentially expressed between groups. MiR-19a-3p and miR- 9-3p reached the highest specificity (64.29%) and sensitivity (81.08%), respectively. The more accurate bivariate logistic model was obtained combining KLK11 with either miR-9-3p and miR-19a-3p.

Conclusions: Our findings demonstrated that selected kallikreins and miRNAs proved to be an accurate diagnostic tool for PCa. Urine cells pellets obtained after DRE represent a reliable biological matrix for minimally invasive gene expression assays.

 

Keywords

Prostate cancer; Urine sediment; Benign prostatic hyperplasia; Diagnosis; Biomarker; Kallikrein; microRNA

Introduction

Prostate cancer (PCa) is the most common cancer in European elderly men. Over 200 cases every 100.000 subjects are diagnosized in Northern and Western Europe and a continuous increase is observed in Eastern and Southern Europe [1]. Similarly, Benign Prostatic Hyperplasia (BPH) is a condition closely related to ageing [2] and consists of a benign enlargement of prostatic adenoma size that gives rise to Lower Urinary Tract Symptoms (LUTS). These urinary disorders can occur in up to 30% of men older than 65 years [3]. These two conditions are frequently coexisting, and up to 20% of BPH patients subjected to trans urethral resection of prostate are incidentally found to harbour PCa [4]. PCa and BPH also share relevant features at the genetic level [5] and inflammation has been suggested to play an important role in both urologic conditions [6]. Nonetheless, they take place in different anatomic sites [7] and BPH does not affect the risk of developing PCa [8]. The molecular mechanisms responsible for benign or malignant growth of prostate epithelial cells are partially unknown, although cellular overgrowth is a common event. The measurement of serum PSA together with digital rectal examination (DRE) represents the primary diagnostic tool to suspect PCa, whereas definitive diagnosis is achieved by prostate biopsy [9]. In young men, serum PSA levels < 2–3 ng/mL are considered normal: beyond this value, higher levels of PSA are associated with higher risk of having PCa [9]. Nevertheless, poorly differentiated forms of PCa could have low serum levels of PSA as well as high serum PSA levels could be due to benign conditions including BPH [10]. The low specificity of PSA, which is a prostate specific and not a cancer specific marker, is the cause of many unnecessary biopsies. On the other hand, the detection rate of baseline biopsy is only 25-38% [11,12], whereas false-negative amounts to about 23% [13]. When PSA levels remain elevated, rebiopsy is performed and PCa can be detected in 7-22% of cases at the first rebiopsy and 2-10% at the second one [14-16]. Moreover, this maneuver is affected by bleeding, urinary infection risk and pain and discomfort for the patient. Thus, the high false positive rate of PSA together with the low detection rate of biopsy convict the patient to a quite often unnecessary and uncomfortable clinical itinerary. Nevertheless these limitations, prostate biopsy remains the standard tool to confirm the diagnosis of PCa. These many limitations of current diagnostic management of PCa demonstrate the urgent need for new and more accurate tools and methodologies to detect cancer. KLK3 is the gene coding PSA and belongs to the family of kallikreins (KLKs), which accounts for 15 highly conserved serin proteases which have been demonstrated to be involved in hormone-dependent diseases including PCa [17,18]. The scientific interest towards kallikreins in cancer biomarker discovery is justified by their alterations in expression in several cancers which was demonstrated at the protein level [19-21]. KLK11 proved to be altered in PCa at the protein level [22], whereas KLK13 has been described as a favorable prognostic marker of ovarian cancer [23], and shares 51% amino acid identity with KLK11. PCA3 is a new biomarker which is detectable in post DRE urine sediments. Its diagnostic accuracy for PCa is higher than total PSA in men with elevated PSA levels. Though many studies have demonstrated the ability of PCA3 to detect PCa, its employment in clinical routine is under debate [24].

There is a growing scientific interest in microRNAs as markers for PCa; these small RNAs preside over many cellular functions and regulate gene expression [25] and their alteration has been observed in cancer [26]. The first profiling of miRNA involved in PCa dates back in 2007 [27]: from that point, many progresses have been made towards the characterization of different sets of miRNAs for PCa diagnosis and prognosis [28]. Many studies have demonstrated the modulation of miR-9 in various cancers, while miR-19a-3p belongs to the oncogenic miR-17-19 cluster, also named oncomiR-1 [29]. Both miR-9-3p and miR-19a-3p presented in silico affinity binding for PCA3.

The primary aim of the present study was the determination of 4 mRNAs (KLK3, KLK11, KLK13 and PCA3) and 2 miRNAs (miR-9- 3p and miR-19a-3p) expression, in post DRE urine cell pellets from patients suffering PCa and age-matched subjects affected by BPH with elevated PSA levels. Secondary aim was to evaluate the diagnostic accuracy of markers in predicting PCa.

Materials and Methods

Patients

Patients - From June 2012 to April 2013, 41 Caucasian subjects affected by PCa and 38 age-matched subjects with BPH were enrolled. Ethics Committee of the University of Perugia approved the study protocols. All subjects provided written informed consent. PCa group included patients with localized PCa and age < 75 years. Robot assisted radical prostatectomy was performed in all included PCa patients. For PCa group exclusion criteria were age > 75 years, locally advanced or metastatic PCa. BPH group included subjects with negative needle biopsy, PSA levels higher than 3 ng/ml which underwent transurethral prostate resection with confirmed diagnosis of BPH. Exclusion criteria for BPH group were urinary infections, bladder stones, catheterization, age > 75 years. The clinical parameters of all subjects are reported in Table 1. The expression values of KLK3, KLK11, KLK13, miR-9-3p and miR-19a-3p were evaluated in urine sediments and comparisons were made between PCa and BPH groups. Logistic regression analysis was performed to estimate the diagnostic accuracy of markers under analysis in predicting PCa.

Clinical parameters (total subjects: 79) PCa (41) BPH (38)
Median Age 65 (54-75) 66 (48-74)
Median PSA (ng/mL) 7.32 (3.54-50) 7.24 (1.38-21.22)
Clinical Gleason Score 6 (3+3) 32 -
7 (3+4) 6
7 (4+3) 1
8 (4+4) 1
9 (5+4) 1
Clinical staging cT1c 23
cT2a 13
cT2c 3
cT3a 2
Pathological Gleason Score 6 (3+3) 29 -
7 (3+4) 9
7 (4+3) 1
8 (4+4) 1
10 (5+5) 1
Pathological Staging pT2a 4
pT2c 28
pT3a 6
pT3b 3
Positive Surgical Margin 15% -

Table 1: Clinical parameters of subjects included in the study.

Sample processing

Urine collection: First catch voided urine was collected in sterile cups before prostate biopsy and after attentive DRE. Briefly, a firm pressure on the gland from base to apex and from lateral to median side with three stokes for each lobe was performed. Urine was stored at 4°C and processed within 4 hours. After centrifugation (2000×g, 10 min, 4°C) cell pellets were washed twice with 1X calcium-free phosphate buffered saline. Cell lysates were obtained adding 300 μL Lysis and Stabilization Buffer (Total RNA Extraction Kit, Norgen Biotek Corp. Ontario, Canada).

RNA extraction: Total RNA, including miRNAs, was extracted from cell lysates with Total RNA Extraction Kit (Norgen Biotek Corp. Ontario, Canada) following the manufacturer’s recommendations. RNA has been subjected to qualitative and quantitative assessments through Bioanalyzer, Nanodrop and Qubit RNA HS Assay Kit (Life technologies). RNA was stored at -80°C until use.

Reverse transcription and real-time PCR

mRNAs: 15 ng of total RNA were reverse transcribed with SuperScript VILO cDNA Synthesis Kit (Invitrogen, Life Technologies, CA, USA) according to manufacturer’s protocol. Real-time PCR assays were performed with Taqman probes (Applied Biosystems, Foster City, CA) on a BioRad iCycler (BioRad, Hercules, CA). The probe sequences used are listed in Table 2. In a total PCR reaction volume of 20 μL, 4 μL of diluted cDNA (1:20) were added. Each sample was run in triplicate and results were averaged. In parallel, negative controls without template were analyzed. The -ΔCt method was used to calculate the relative expression of the target genes as follows: -ΔCttarget gene= -(Cttarget gene - Ctβ actin).

Gene Name Sequence Exons connected Amplicon
KLK3 kallikrein-related peptidase 3 Hs02576345_m1 NM_001030047.1 1-2 83 bp
KLK11 kallikrein-related peptidase 11Hs01100849_m1 NM_001136032.2 4-5 77 bp
KLK13 kallikrein-related peptidase 13 Hs01087307_m1 NM_015596 4-5 82 bp
PCA3 Prostate cancer antigen 3 Hs01371938_m1 AF_103907.1 2-3 80 bp
ACTB Actin beta Hs01060665_g1
NM_001101 
2-3 63 bp

Table 2: TaqMan Probe sequences used for mRNA analysis.

microRNAs: 7.5 ng of total RNA were reverse transcribed with miRCURY LNA™ Universal RT miR PCR, polyadenylation and cDNA synthesis kit (Exiqon) as described in our previous paper [30]. 0,5 μL of RNA spike-in control (UniSp6, provided with the cDNA synthesis kit) was introduced in RT mix (total volume 10 μL) immediately before retrotrascription and served as positive cDNA synthesis control. miRCURY LNATM specific PCR primer set (Exiqon system) and Exiqon miRCURY LNA Universal RT microRNA PCR SYBR Green master mix were used for real-time PCR amplification. In a total reaction volume of 10 μL, 4 μL of diluted cDNA (1:10). Melting curve analysis was perfomed at the end of amplification steps (45 cycles). Each sample was run in triplicate and the results were averaged; no-template controls were included in the analysis. For each sample, UniSp6 was used as PCR positive control. PCR reactions were performed on a Bio- Rad iCycler (BioRad, Hercules, CA). MiR-191 was used to normalize data based on the gene stability number obtained from NormFinder algorithm [31]. The -ΔCt method was used to calculate the relative expression of the target genes as follows: -ΔCttarget miRNA= -(Cttarget miRNA - CtmiR-191).

Statistical analysis

GraphPad Prism 6.0 (GraphPad Software, Inc., San Diego, CA) was used to evaluate statistical significance between groups. Gene expression data were expressed as -ΔCts. The significance threshold was set at 0.05. Mann-Whitney test was used to compare the expression levels of selected genes between groups. XLSTAT (Microsoft) was used to carry out Receiver Operating Characteristics (ROC) and logistic predictive models of selected markers in order to evaluate if their combination could ameliorate the proportion of correctly predicted diagnosis and to define cut-offs in the ΔCt values for a positive test. Logistic regression analysis was performed to estimate the accuracy of selected genes in predicting PCa. Prediction models were based on mRNA and miRNA expression values. First, univariate logistic regression analysis was made for each independent variable; differentially expressed genes between PCa and BPH groups were included. Bivariate logistic regression analysis was performed in order to establish the best combination of independent variables able to predict PCa.

miRNA target prediction and pathway analysis

Computationally predicted mRNA targets of the miRNAs under analysis were identified by TargetScan Release 6.2 (http://www.targetscan.org) [32] and MicroCosm Targets version 5 (http://www.ebi.ac.uk/enright-srv/microcosm) [33]. The outputs of these target prediction algorithms were merged and all predicted targets (including overlapping targets) were revealed by Microsoft Visual FoxPro 9.0 (Microsoft Corp.) developer tool using the annotation database downloaded from the Ensembl homepage (www.ensembl.org) [34,35]. Targets of significantly differentially expressed miRNAs were subjected to pathway analysis in all pair-wise comparisons. DIANA mirPath software [36] analysis was also performed using DIANA-MicroT-4.0 and TargetScan 5 prediction algorithms.

Results

Qualitative and quantitative assessment of RNA

Total recovery of RNA was not significantly different among samples. Mean RNA integrity number (RIN) was 8.7 (8.3-9.0) and 260/280 ratio was 1.84 (1.84-2.03). Minimal variations in total RNA content were corrected during reverse transcription using fixed RNA input (15 ng total RNA for KLK3, KLK11, KLK13; 7,5 ng for miR-9- 3p and miR-19a-3p). Relative quantification of real-time PCR data was performed referring the levels of selected targets to housekeeping genes (-ΔCt). Expression levels with standard deviations are reported in Figure 1 and Supplementary Table 1.

cancer-science-therapy-interquartile-range

Figure 1: Expression levels expressed as -ΔCt (median with range and interquartile range) of selected mRNAs (KLK3, KLK11 and KLK13, Panel A) and miRNAs (miR-9-3p, miR-19a-3p, Panel B) between prostate cancer patients (PCa, left) and subjects with benign prostatic hyperplasia (BPH, right).*P< 0.05; **P< 0.01; ***P< 0.001.

KLK3, KLK11, KLK13

KLK3 and KLK11 were significantly up-regulated in PCa patients with respect to BPH (P<0.001 and P<0.01, respectively) (Figure 1, Panel A). KLK13 was not significantly different between PCa and BPH groups (Figure 1, Panel A). In the univariate logistic regression analysis, KLK11 reached the highest odds ratio (1.579, 95% CI 1.187- 2.101) followed by KLK3 (odds ratio 1.575, 95% CI 1.229-2.020).

PCA3

PCA3 was not determined in 22 out of 38 BPH patients. In PCa group, PCA3 was detectable in all subjects, with mean expression value of -6.850 ± 3.405.

miR-9-3p and miR-19a-3p

miR-9-3p and miR-19a-3p were downregulated in PCa group with respect to BPH patients (P<0.001 and P<0.01, respectively) (Figure 1, Panel B). In the univariate logistic regression analysis, odds ratios were 0.719 and 0.820 for miR-9-3p and miR-19a-3p, respectively.

Receiver operating characteristics (ROC) and bivariate logistic regression analyses

When treated individually, KLK3 showed the highest AUC (0,837), followed by miR-9-3p (0,769), KLK11 (0,739) and miR-19a-3p (0,723) (Table 3, Figure 3). The bivariate model which gave the highest specificity (76,92%) and sensitivity (86,67%) were obtained combining KLK11 with miR-19a-3p and miR-9-3p, respectively (Table 3). The highest AUC (0,880) was reached combining KLK3 with miR-19a-3p (Table 3, Figure 3). Cut-offs of ΔCt values associated with a positive test with corresponding sensitivities and specificities are shown in Supplementary Table 2.

Variable(s) OR 95% CI P value Sensitivity Specificity Hosmer-Lemeshow test (Pr>c2) |β| AUC
Univariate analysis
KLK3 1.575 [1.229-2.020] <0.0001 73.53% 60.87% 0.398 0.851 0.837
KLK11 1.579 [1.187-2.101] 0.0003 67.65% 62.86% 0.913 0.563 0.739
miR-9-3p 0.719 [0.588-0.881] 0.0002 81.08% 53.33% 0.359 0.580 0.769
miR-19a-3p 0.820 [0.718-0.935] 0.001 76.47% 64.29% 0.091 0.494 0.723
Bivariate analysis
KLK3+KLK11 KLK3 1.500 [1.167-1.928] < 0.0001 85.29% 65.22% 0.692 0.758 0.860
KLK11 1.331 [0.920-1.926] 0.307
KLK3+miR-9-3p KLK3 1.631 [1.200-2.216] < 0.0001 87.10% 72.22% 0.106 0.929 0.877
miR-9-3p 0.827 [0.612-1.118] 0.293
KLK3+miR-19a-3p KLK3 1.833 [1.289-2.607] < 0.0001 85.71% 72.22% 0.312 1.127 0.880
miR-19a-3p 0.932 [0.764-1.137] 0.177
KLK11+miR-9-3p KLK11 1.420 [1.016-1.985] 0.0001 86.67% 71.43% 0.546 0.404 0.828
miR-9-3p 0.729 [0.576-0.924] 0.567
KLK11+miR-19a-3p KLK11 1.435 [1.006-2.049] 0.0004 78.57% 76.92% 0.500 0.395 0.811
miR-19a-3p 0.803 [0.688-0.937] 0.529

Table 3: Logistic regression analysis of variables predicting prostate cancer. Differentially expressed between PCa and BPH group (KLK3, KLK11, miR-9-3p and miR-19a-3p) were included. Prediction models are based on mRNA expression of each gene. |β|, absolute value of regression coefficient; OR, odds ratio; CI, confidence interval; AUC, Area Under Curve.

cancer-science-therapy-Upper-panel

Figure 3: Receiver operating characterstic (ROC) curves of variables under analysis. Upper panel: AUCs of markers treated separately; lower panel: AUCs derived from combinations of markers in logistic regression analysis.

miRNA target prediction and pathway analysis

Computational target prediction of the 2 differentially expressed miRNAs has been performed with two algorithms: Target Scan Release 6.2 and Micro Cosm Targets Version 5. Among these predictions, 774 (for miR-9-3p) and 1044 (for miR-19a-3p) have been given by MicroCosm. 1237 (for miR-9-3p) and 1171 (for miR-19a-3p) transcripts with conserved sites were found in TargetScan. mRNA targets of differentially expressed miRNAs in all pair-wise comparisons have been subjected to pathway analysis to decipher the function of predicted miRNA targets in biological processes and to identify pathogenetic pathways potentially modified by posttranscriptional regulation. Results are shown in Figure 2.

cancer-science-therapy-DIANA-microT

Figure 2: DIANA-mirPath analysis, based on DIANA-microT 4.0 predictions, applied to explore the predicted gene targets of miR-9-3p and miR-19a-3p associated with human prostate cancer. Genes interacting with both miR-9-3p and miR-19a-3p are shown in orange, those interacting with one of the two miRNAs are shown in yellow

Discussion

The early diagnosis of PCa is of crucial importance to allow for an effective treatment, which could improve cancer-specific survival [37]. Currently, diagnosis of PCa is performed through needle biopsy, whose indications are an elevated PSA level and/or a suspicious DRE [7]. The increase in PSA levels also occurs in benign conditions such as BPH, and this fact leads subjects with BPH and altered PSA levels to be subjected to useless and often repeated biopsies, with discomfort for the patient and high impact on sanitary costs. Both PCa and BPH are ageing-related conditions; this issue is particularly relevant considering the increasing age of population. The molecular mechanisms discriminating benign from malignant growth of prostate epithelial cells are partially unknown, although cellular overgrowth is a common event. The characterization of gene expression signatures exclusive for one of these two urological conditions could greatly improve the clinical management of PCa.

In the present report, the expression levels of 4 mRNAs (three kallikreins - KLK3, KLK11, KLK13 - and a PCa marker - PCA3) and 2 miRNAs (miR-9-3p, miR-19a-3p) were assayed in post DRE urine of men suffering PCa and compared with age-matched subjects with BPH. In these two categories of subjects, serum PSA levels were similar (p value 0.49), demonstrating that they do not represent a reliable discrimination tool. To our knowledge, this is the first report evaluating these molecules in post DRE urine sediments of PCa and BPH patients. We found a significant upregulation of KLK3 and KLK11 in cancer group, while KLK13 did not show significant differences. Higher levels of KLK3 and KLK11 mRNAs were positively correlated with risk of harboring PCa, as showed by univariate logistic regression analysis. A recent study on KLK3 mRNA levels in expressed prostatic secretions showed an increase in specimens obtained from PCa patients [38]. Contrary to these findings, the expression of KLK3 has been reported to be decreased in cancerous prostate tissue [39]. KLK11 and, to a lesser extent, KLK13, are both expressed at the mRNA and protein level in normal prostate tissue [40,41] and KLK11 mRNA is much present in PCa tissues with respect to BPH [42]. Higher KLK13 mRNA levels were demonstrated to be an independent favourable prognostic marker for breast carcinoma [43]. In contrast, another study reported the increase in expression of KLK13 as indicator of poor prognosis in epithelial ovarian carcinoma [44]. PCA3 gene, originally named DD3, was discovered in 1990s [45]. Its deregulation in PCa has been evaluated at the tissue level [45,46] and in post DRE urine sediments [46-48] also compared to subjects with BPH [49]. These studies also revealed that the use of this test might raise the specificity of diagnosis of PCa. In our study, PCA3 mRNA was detectable in all subjects of PCa group, whereas it was undetectable in 22 out of 38 patients with BPH. This issue made statistical comparisons between groups unfeasible.

MicroRNAs represent another growing branching the research field of non-invasive diagnostic/prognostic cancer markers [26], because of their intrinsic stability in body fluids such as blood and urine [50]. The convergent alteration of the same sets of miRNAs in much pathology reflects their involvement in many crucial steps of carcinogenesis (Figure 2). miRNAs can act either as oncogenes or oncosuppressors, although their behaviour cannot be generalized to every type of cancer [51]. Several molecular events are potentially responsible for the alteration of miRNAs in cancer: among all, the imbalance of post-transcriptional regulation, genetic modifications and epigenetic alterations seem to play a major role. It is difficult to establish if a particular miRNA exerts its action as a tumor suppressor or oncogene, since this depends on many factors which vary according to tumor characteristic and other unknown contextual factors. We found both miR-9-3p and miR-19a-3p to be up-regulated in urine sediments from BPH group with respect to PCa patients. Many studies have demonstrated the epigenetic silencing of miR-9 loci in gastric tumors [52], breast cancer [53], non-small-cell lung cancer [54], oral squamous cell carcinoma [55] and renal cancer [56]. Contrary to these findings, miR-9 has been shown to increase in Hodgkin’s lymphoma [57] and c-myc-induced mammary tumors [58]; this is an interesting issue, since MYC is responsible for the balanced expression of many miRNAs, such as the oncogenic miR-17-19 cluster, also named oncomiR-1 [26]. The miR-19 family belongs to the miR-17-92 cluster. The overexpression of miR-19a has been observed in prostate tumor tissues compared to adjacent benign tissue [59]. In prostate cancer cell lines, an upregulation has been shown in PC3 androgen-independent cell lines, whereas a downregulation was recorded in LNCaP androgendependent strain, compared to normal prostatic epithelial (PrEC) cells [60]. Recently, miR-19a was found to directly repress RAB13, which is able to tight cellular junctions, favouring metastasis formation in PCa [61]. These findings support our results since cancer patients included in the present study all harboured androgen-responsive and organconfined tumors.

In our univariate logistic regression analysis, all differentially expressed genes between PCa and BPH groups showed acceptable confidence intervals (CI) of odds ratios. KLK3 and KLK11 mRNA levels positively correlated with risk of PCa (odds ratios > 1), whereas higher levels of miR-9-3p and miR-19a-3p trascripts were inversely correlated with cancer outcome (odds ratios < 1). The highest sensitivity and specificity was reached by miR-9-3p and miR-19a-3p, respectively. Bivariate logistic regression models were made in order to verify if a combination of variable could improve predictive power for PCa. The best model was obtained combining KLK11 with either miR-19a-3p or miR-9-3p, which significantly improved specificity (+14.06%) and sensitivity (+19.02%) of KLK11 alone. The highest AUC was reached combining KLK3 with miR-19a-3p. Our findings demonstrated that selected kallikreins and miRNAs proved to be an accurate diagnostic tool for PCa. Urine cells pellets obtained after DRE represent a reliable biological matrix for minimally invasive gene expression assays. We did not perform a stratification of PCa patients into low, intermediate and high risk subgroups since the vast majority of them were affected by low risk disease.

Acknowledgements

This research project was supported by 2007-2013 ESF “Competitiveness and Employment objective” Umbrian Regional Operational Programme (ROP), Avviso pubblico aiuti individuali per la realizzazione di progetti di ricerca.

Conflicts of Interests

The authors declare no conflicts of interests.

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