Printer Friendly

MicroRNA Theranostics in Prostate Cancer Precision Medicine.

Prostate cancer is the second most frequently diagnosed malignancy and the fifth leading cause of cancer-related deaths in men worldwide (1). The limitations of current noninvasive means of prostate cancer diagnosis, such as prostate specific antigen (PSA) [4] testing, are becoming noticeable (2). Therefore, the establishment of predictive biomarkers capable of distinguishing between indolent and aggressive prostate cancer and stratifying patients based on their responsiveness to therapy would reduce the risk of overdiagnosis and overtreatment. One promising approach is the utilization of microRNAs (miRNAs) in the diagnosis and prognosis of prostate cancer patients. Moreover, therapeutic targeting and modulation of miRNAs might enable individualized therapeutic management for prostate cancer patients, paving the way to precision medicine that takes into account individual genetic variability and environmental factors for the prediction of treatment strategies for a particular disease in a particular patient/group of patients (3). This emerging approach has the potential to improve prediction of susceptibility to prostate cancer and disease progression, thereby minimizing the development of metastatic disease and allowing the ability to tailor therapeutic intervention. Moreover, the use of precision medicine might reduce resistance to therapy through the selection of drugs that are most effective for each patient. Consequently, this medical strategy has the potential to considerably improve the quality of life of patients, as well as save time and improve cost of treatment. A prerequisite for the development of precision medicine is the identification of a combination of biomarkers to guide therapeutic decisions. The potential of miRNAs is rapidly becoming evident in this context. In this review, we describe the emerging biomarker and therapeutic potential of miRNAs.

Canonical and Noncanonical Pathways of miRNA Biogenesis

miRNAs are a class of short (approximately 22-nucleotide) noncoding RNAs that regulate gene expression post-transcriptionally. They are derived through canonical and noncanonical pathways from long hairpin precursor molecules, called primary miRNAs (pri-miRNAs), transcribed from miRNA gene loci by the enzyme RNA polymerase II (Fig. 1). During canonical biogenesis (Fig. 1, part I), pri-miRNAs are processed in the nucleus by the Drosha and the double stranded RNA (dsRNA)-binding protein of DiGeorge Syndrome Critical Region Gene 8 (DGCR8) RNase-ribonucleoprotein complex into approximately 70-100 bp long precursor miRNAs (premiRNAs). The pre-miRNAs are exported to the cytoplasm by the Exportin 5 (EXP5)-controlled transport mechanism, where another RNase III enzyme called Dicer produces approximately 22-bp duplexes after binding to TRBP (TAR RNA-binding protein). The miRNA duplexes consist of a 5'-3' strand known as the guide strand, and a 3'-5' strand which usually undergoes degradation. With the help of the RNA-induced silencing complex (RISC) and Argonaute (AGO) 1-4 proteins, the guide strand now becomes the mature miRNA, which plays a key role in posttranscriptional gene regulation by messenger RNA target cleavage or translational repression (4).

Alternatively, a small number of miRNAs are processed through the noncanonical pathways of miRNA biogenesis, which involve the Dicer-independent, terminal uridylyl transferase (TUTase)-dependent, and Drosha DGCR8-independent miRNA biogenesis pathways (5) (Fig. 1, part II). For example, biogenesis of miR-451 uses AGO2 instead of Dicer to form an AGO-cleaved pre-miR-451 (ac-pre-miR-451), which is trimmed down by a poly(A)-specific ribonuclease (PARN) to form the mature miR-451 (5). In the TUTase-dependent pathway, pre-miRNAs with a shorter 3' overhang such as the members of the let-7 family undergo monouridylation by terminal uridyl transferases (TUT), TUT2, TUT4, and TUT7 for efficient Dicer processing (5). The Drosha-DGCR8 independent noncanonical pathway can produce short spliced-out introns using the spliceosome-machinery, called mirtrons, to form pre-miRNA Drosha-mediated processing is also bypassed by miRNAs, such as miR-320, derived from short hairpin RNAs by RNA polymerase. The 7-methylguanosine ([m.sup.7]G) 5' capped pre-miR-320 is exported to the cytoplasm by EXP1 for Dicer processing in which the 3' end forms the mature miRNA as the [m.sup.7]G at the 5' end is thought to interfere with AGO binding (5).

A small percentage of miRNAs follow the noncanonical pathways of biogenesis, whereas the majority of functional miRNAs are produced by the canonical pathway.

Regulation of miRNA Biogenesis

The biogenesis of different miRNAs is regulated in different ways depending on the type of miRNA (Fig. 1). Mutations in cancer cells can perturb miRNA biogenesis and miRNA-mediated regulation by modifying either the components of the biogenesis pathway or the miRNA sequence, as well as their target sequence (5). Epigenetic control, such as DNA methylation and histone modifications also contribute to miRNA biogenesis (5) (Fig. 1, part I, A and B). An example of epigenetic control includes polycomb proteins which are conserved regulators involved in gene silencing. The catalytic subunit, EZH2 (enhancer of zeste homolog 2), of the polycomb protein complex promotes prostate cancer metastasis by epigenetic silencing of the apoptotic miRNAs, miR 205, andmiR-31 (6).

Transcription factors such as p53, v-myc avian myelocytomatosis viral oncogene homolog (MYC), myoblast determination protein 1 (MYOD1), zinc finger E-box binding homeobox (ZEB) 1 and 2, and p27Kip1 (cyclin-dependent kinase inhibitor 1B) regulate the transcription of miR-34, miR-17, miR-1, miR-15a, miR-200, and miR-221 which play an important role in prostate cancer as either tumor promoters or tumor suppressors (7-11) (Fig. 1, part I, C). The biogenesis of many miRNAs is also regulated during the Drosha- and Dicer-mediated processing of miRNAs (5) (Fig. 1, part I, D and E).

It is known that the miRNA biogenesis pathway is tightly regulated and alterations in the transcriptional and genomic levels of the miRNA processing machinery may lead to aberrant expression of specific miRNAs which in turn may contribute to cancer progression. However, further insights are necessary for understanding the causal relationship between perturbed miRNA biogenesis and tumorigenesis for their implications in precision medicine.

Role of miRNAS as Prostate Cancer Biomarkers

A summary of the studies identifying differentially expressed miRNAs in prostate tumor tissues and in circulating fluids such as plasma, serum, urine, and prostatic secretions is provided in Tables 1 and 2 (See the Data Supplement that accompanies the online version of this review at http://www.clinchem.org/content/vol62/ issue10 for references). Some of these studies are described in the next 2 sections.

miRNA EXPRESSION PATTERNS IN PROSTATE TUMOR TISSUES AS DIAGNOSTIC AND PROGNOSTIC TOOLS

Because differential expression of miRNAs correlates with prostate cancer development and progression (12), measurement of miRNA expression patterns represents a promising approach for improving prostate cancer diagnosis and prognosis. In 2008, Ambs and colleagues carried out genomic profiling of miRNAs in 60 tumor tissue samples and 16 controls and found that miR-32, miR-182, miR-31, and miR-26a were the most significantly overexpressed tumor miRNAs (13). This suggested the involvement of these miRNAs in tumor progression. More recent studies demonstrated the oncogenic role of additional miRNAs such as miR-181a and miR-196a (14, 15) by regulating the signaling implicated in prostate cancer (16). In contrast, the tumor suppressing ability of miRNAs has also been well documented in prostate cancer (17--19). For example, in a study by Schaefer et al. evaluation of miRNA expression in 76 prostate tumor tissues revealed 15 differentially regulated miRNAs of which 10 were downregulated and 5 were upregulated, suggesting the tumor suppressive role of the underexpressed miRNAs (20). Martens-Uzunova et al. analyzed 102 patient-derived tissues and used all 513 significantly expressed miRNAs to develop an miR-classifier consisting of 54 miRNAs capable of segregating prostate cancer tissues from normal adjacent tissues (21). The miR-classifier system was further validated on the data set reported by Schaefer et al. (20) and a sensitivity of 1 and specificity of 0.67 was achieved (21). In another study, Larne et al. rationally selected 13 miRNAs based on their biomarker potential in the previous miRNA screening study by Martens-Uzunova et al. (21) and validated 7 differentially expressed miRNAs including upregulated miR-96--5p and miR-183--5p and downregulated miR-145--5p and miR-221--5p in tumor samples from 49 patients and 25 controls. These miRNAs were combined to generate an miRNA index capable of determining the pathological status of prostate cancer patients (22). In another study, miR-182 and miR-187 were identified as the most differentially expressed miRNAs between 50 tumor and 10 normal tissues. The predictive potential of miR-182 and miR-187 was evaluated by combining their high expression with Gleason score and PSA to improve the diagnostic and prognostic ability of miRNA biomarkers resulting in 88.6% sensitivity and 50% specificity (23). Furthermore, Gu et al. used an integrated multistep approach to obtain information on miRNAs as cancer biomarkers from publicly available datasets. Results revealed that miR-182, miR-200c, and miR-221 are associated with pathological status where miR-182 (as shown previously) and miR-200c were upregulated, and miR-221 was downregulated in prostate cancer (24).

Prediction of patient prognosis is an important aspect in the clinical management of prostate cancer. Therefore, several studies have been performed to establish a distinct miRNA expression pattern to segregate patients for their risk of developing aggressive prostate cancer. Various miRNA expression profiling studies specific for prostate cancer tissues have identified differences in the expression of miRNAs in benign prostatic hyperplasia (BPH), localized and metastatic prostate cancer (Tables 1 and 2). For instance, downregulation of the miRNAs (miR-29c, miR-34a, and miR-141) was observed by Lichner et al. in higher (grade 5) compared to lower (grade 3) Gleason grades (25). In another study, they identified lower levels of miR-331--3p and miR-152 in patients with a high risk of biochemical recurrence (BCR) (26). Sun and colleagues observed high expression of miR-221 and miR-222 in castration resistant prostate cancer (CRPC) tissues, and reported that miR-221 stimulates overexpression of genes involved in tumor metastasis (27). More recently, the miR-221/222 cluster was reported as a tumor suppressor, the downregulation of which led to CRPC (28). However, this finding is not in line with what has been reported by Sun and colleagues (27).

Of the several studies performed up to the present time, miRNAs seem to be consistently deregulated in prostate cancer. Although these preliminary findings suggest the diagnostic and prognostic potential of these tissue based miRNAs in prostate cancer, more studies need to be performed in larger cohorts of patients to identify reproducible miRNA signatures.

DIAGNOSTIC AND PROGNOSTIC POTENTIAL OF CIRCULATING miRNAS

Although tissue-based miRNA profiling has potential benefits, it is invasive and requires the use of sophisticated techniques for sample collection. Therefore, circulating miRNAs in body fluids such as plasma, serum, urine, and prostatic secretions offer a cheaper and minimally invasive alternative. Circulating miRNAs are stable and protected by a variety of mechanisms. These include complex-formation between circulating miRNAs and specific proteins (29) and transport within exosomes and microvesicles which protect them from degradation (30). For example, exosome-mediated transfer of miR-105 through the endothelial barrier has been shown to promote breast cancer metastasis (31).

The first study to demonstrate an association between circulating miRNAs and prostate cancer prognosis identified miR-141 which differentiated between metastatic prostate cancer patients and healthy controls (32), and this miRNA has recently been observed to increase with higher Gleason score, indicative of high-risk prostate cancer (33). The same miRNA was found to be up-regulated in another study along with miR-21 and miR-221 in metastatic tumors compared to localized tumors (34). Serum miR-21 and miR-221 showed high diagnostic sensitivity and specificity in men with prostate cancer as indicated by prostate biopsy based on increased PSA or digital rectal examination (DRE) (35).

Circulating miRNAs can also be used to predict BCR and CRPC. Evidence in this direction was provided by Selth and colleagues who identified that miR-194 and miR-146b-3p are up-regulated in the sera of patients who experienced BCR (36). In a separate study, high concentrations of 4 serum miRNAs (miR-141, miR-298, miR346, and miR-375) were identified in transgenic mice with advanced prostate cancer vs nontumor mice by the same group. These miRNAs were then measured and found to be up-regulated (except for miR-298) in the serum of patients with metastatic CRPC as compared to the healthy controls (37). Another study comparing serum miRNAs from men with metastatic CRPC and localized prostate cancer revealed both miR-141 and miR-375 as predictive biomarkers of castration resistance (38). In a recent study, Huang et al. identified exosomal miR-1290 along with miR-375 as prognostic biomarkers in CRPC (39). Furthermore, by using a microarray panel, Bryant et al. identified 12 (11 up-regulated and 1 downregulated) miRNAs used to compare changes in miRNA expression levels in 78 plasma samples from prostate cancer patients and 28 healthy men. Interestingly, miR-107 and miR-574-3p were also present at a higher concentration in the urine of men with prostate cancer compared to the controls, indicating their minimally invasive biomarker potential (40). In a comprehensive study performed by Mihelich et al. of 150 patients, an miRNA signature consisting of a panel of 14 serum miRNAs was proposed to identify patients with a low risk of developing high-grade prostate cancer and BCR (41). Many of the miRNAs in the panel have been previously implicated in prostate cancer as discussed above.

Owing to the ease of collection of urine and the release of prostate cells into the urethra after DRE, urinary miRNAs were explored in many studies. Srivastava et al. found low concentrations of miR-205 and miR-214 in the urine samples of 36 prostate cancer patients compared to 12 healthy men (42). In a separate study, the role of urinary miR-1825 and miR-484 as diagnostic biomarkers of prostate cancer was analyzed in 8 prostate cancer patients, 12 BPH patients, and 10 healthy controls, as these 2 miRNAs were found to target genes related to prostate cancer development and progression (43). miR-484 was downregulated in prostate cancer patients compared to BPH patients and healthy controls, whereas upregulation of miR-1825 was variable among the different comparisons. Combining these results with PSA concentrations outside the reference interval resulted in 40% sensitivity and 81% specificity in prostate cancer diagnosis. In a recent study, miR-483--5p was found to be up-regulated in the urine samples of prostate cancer patients vs healthy men (44).

Finally, Guzel and colleagues identified downregulation of miR-361-3p, miR-133b, and miR-221 and upregulation of miR-203 in prostatic secretion samples from 23 prostate cancer patients and 25 BPH patients. Interestingly, miR-203 is reportedly downregulated in primary prostatic tumors and metastatic prostate cancer cell lines suggesting its tumor suppressive role (45). On the other hand, miR-221 has been reported as oncogenic, but its deregulation in both directions has been previously discussed (34). Recently, Lewis et al. found higher concentrations of miR-888 in expressed prostatic secretions (EPS) urine in high grade vs low grade prostate cancer (46). Although a distinctive and reproducible circulating miRNA signature for prostate cancer diagnosis and prognosis has not yet been found, encouraging results are suggested by the studies summarized in Tables 1 and 2.

On the basis of the diagnostic and prognostic ability of miRNAs, an miRNA panel ("miRview-mets2") has been developed to identify the origin of metastatic cancers in which the primary origin of metastasis is not known. The panel consists of 64 miRNAs validated on 489 samples of which 146 are metastatic tumors covering 42 tissues of origin including prostate. Mueller and colleagues aimed to validate the panel in a cohort of metastatic tumors of known origin and unknown origin removed from the central nervous system. The test correctly identified the samples of known and unknown origin in a majority of the cases; however, prostate cancer metastatic samples were excluded from analysis because the investigators found that the correlation between the primary and metastatic prostate tissue was substantially lower when compared to other tissues (47). In addition to the miRview-mets2 panel, other miRNA panels, such as miRview-lung, miRview-squamous, miRview-meso, and miRview-kidney have also been developed (48). All clinical panels are based upon a tree-classifier system developed by Rosenfeld and colleagues in 2008 (49) and have been validated for clinical use for identifying the origin of metastatic tumors, however, they have not been submitted to the FDA for approval yet (48). Additionally, the use of such panels might not be beneficial in prostate cancer because the correlation between primary and metastatic prostate tissue is lower compared to other tissues, and the prostate is not considered a common tissue of origin and represents a minor percentage of cancers of unknown primary origin. Nevertheless, the use of miRNAs as diagnostics in the clinics may aid in the use of precision medicine in the treatment of different cancers as they differentiate not only between tumor types, but also may aid in identifying the tumor origin.

Methodologies and Strategies for Identification of Circulating miRNAs in Cancers

Altered levels and the stability of miRNAs in body fluids make them ideal biomarkers to be measured in a minimally invasive manner. However, certain technical challenges and analytical and preanalytical factors need to be resolved before their implementation as biomarkers (50). For example, assessment of hemolysis using a spectrophotometer at 540 nm is recommended as some miRNAs, such as miR-16, miR-451, miR-92a, and miR-486--5p are released by red blood cells and may affect circulating miRNA content released from tumor tissues (51). Currently, qRT-PCR (real time PCR) is the most widely used approach for miRNA quantification. However, there is no unanimity regarding the use of housekeeping genes for the normalization of circulating miRNAs (52), and spiked-in Caenorhabditis elegans miRNAs have been commonly used as an alternative (32). miRNA detection is also possible by next-generation sequencing (NGS) which uses 3' and 5' adaptors that bind to both ends of the miRNAs in the total RNA sample. The adaptors act as primer binding sites during complementary DNA (cDNA) library preparation and PCR amplification. This is followed by parallel sequencing of individual cDNA molecules to determine the precise order of nucleotides within an miRNA (53) (Fig. 2A). Although promising and successful in the discovery of new miRNAs, this technology is expensive and requires advanced computational expertise. Another technique which focuses on the screening of miRNA levels using an miRNA microarray, allows the simultaneous analysis of hundreds of miRNAs. All miRNAs in the total RNA extracted are fluorescently labeled for hybridization with immobilized oligonucleotide probes on a microchip. This is followed by several washing steps and the presence of a specific miRNA is visualized by fluorescence measurement of the array (54) (Fig. 2B).

An ideal method for the detection of circulating miRNAs in clinical settings is one that exhibits high sensitivity and specificity, and involves small amounts of starting material. To achieve this, several new techniques have been developed to improve target miRNA capture, amplification, and detection. For instance, Campuzano and colleagues developed a magneto-biosensor for the detection of miR-21 in total RNA from breast cancer cells. In this approach, the p19 viral protein was immobilized to magnetic beads and used to selectively bind to dsRNA formed by the hybridization of biotinylated antimiR-21 probe to the miR-21 target. The hybrid was labeled with streptavidin protein conjugated to horseradish peroxidase (Strep-HRP polymer) and subjected to electrochemical detection which involved capturing of the magnetic beads on a carbon electrode surface and measurement of catalytic current when [H.sub.2][O.sub.2] (hydrogen peroxide) was added. In this way, using a RNA binding viral protein, quantification of miR-21 was possible without the need for sample processing or amplification by PCR (55) (Fig. 2C).

Recently, Hu et al. used a similar method for the detection of serum miR-155 which involved graphene quantum dots (GQDs) as a new platform for HRP immobilization owing to their good biocompatibility, conductivity, and low toxicity. miR-155 hybridized with capture DNA and N[H.sub.2]-DNA to form a dsDNA structure which reacted with GQDs with surface bound HRP. In the presence of HRP, [H.sub.2][O.sub.2] generated an increased electrochemical signal for the detection of miR-155 with a detection limit of 0.14 fM (56). However, strategies with electrochemical readout require surface immobilization for signal transduction which limits platform flexibility, as well as increase susceptibility to interference from matrix components. Therefore, strategies with optical readouts, other than NGS and microarrays, have been developed.

A nanoparticle-based optical method, called the bio-BaGel assay, developed by Nam and colleagues was used for the detection of multiple miRNAs using a conventional gel electrophoresis system. In this method, barcode DNA (artificially designed DNA) probes combined with gold nanoparticles (AuNP) were used for the multiplexed detection of several target miRNAs. Each barcode probe contained 2 regions, one for target miRNA capture and the other for the formation of dsDNA with the barcode DNA complement. Presence of an miRNA induced the conjugation of the magnetic probe and barcode probe by sandwich-capturing of the target which was later separated by a magnetic field. This was followed by dissolution of the AuNP and the double-helix barcode DNA was detected during gel electrophoresis (57) (Fig. 2D).

Another optical method using AuNPs for miRNA detection used surface plasmon resonance (SPR) as the basis for measuring adsorption of miRNA onto the surface of AuNPs. In this detection strategy, DNA-linked AuNPs hybridized with capture DNA which in turn initiated the formation of 2 reporter probes to amplify the SPR signal generated in the presence of the target miRNA. This method has been employed for the sensitive detection of miRNAs at subfemtomole concentrations. Another SPR based biosensor employed mismatched catalytic hairpin assembly (CHA) amplification coupled with streptavidin aptamers for miRNA detection. The presence of the target miRNA initiated the allosteric activation of CHA which in turn activated the streptavidin aptamers. The large amount of CHA products hybridized with the capture probes on the biosensor, and the increased streptavidin aptamers enhanced signal intensity. This method displayed high sensitivity and specificity and could detect target miRNA as low as 1 pmol/L (58).

Zhang and colleagues used a fluorescence biosensor to measure let-7a in cell extracts. They used the CMEA (catalyst-oligomer-mediated-enzymatic amplification) system consisting of DNA1, protector-oligomer, and catalyst-oligomer. DNA1 first hybridized with the protector-oligomer to form a duplex which was displaced by let-7a to generate a new DNA1 and let-7a duplex. Subsequently, let-7a was displaced by the catalyst-oligomer releasing let-7a which was then free to bind to another DNA1/protector-oligomer complex. Therefore, one target miRNA generated many DNA1/catalyst-oligomer duplexes which were cleaved by an endonuclease resulting in an increased fluorescence signal intensity (59).

Although these new strategies have improved the specificity and sensitivity of circulating miRNA detection considerably, the low abundance of miRNAs in serum or plasma of individual patients coupled with sequence homology between miRNAs still remains an unresolved issue when applying these techniques for quantification. In addition, the cost of using such technologies could be a limiting factor; however, the increased use and refinement of new technologies would make it feasible for researchers and clinicians to adopt these methods in translating the current knowledge into clinical practice.

miRNAs in Anticancer Therapy

The rationale for using miRNAs as viable targets for anticancer therapies is based on their deregulation in various cancers, including prostate cancer, and their ability to modulate the cancer phenotype by targeting multiple genes (60). Differentially expressed miRNAs may play an oncogenic or tumor suppressive role in cancer development and progression making them ideal candidates to be therapeutically exploited (61). Some of the strategies employed to target miRNA expression in cancer include the use of small molecule inhibitors, miRNA vectors, miRNA mimics, miRNA sponges, the clustered regularly interspaced short palindromic repeat (CRISPR) technology, antisense oligonucleotides, and miR-mask oligonucleotides (Fig. 3).

Small molecule inhibitors can modulate signaling pathways involved in miRNA biogenesis (Fig. 3A). They may interfere with the transcription of pri-miRNAs by targeting miRNA encoding genes, by inhibiting processing of pri-miRNAs by Dicer, and binding to the AGO2 protein to form an active RISC or by blocking the interaction between RISC and the target mRNA. Gumireddy et al. identified such small molecule compounds by cloning complementary sequences of miR-21 into a luciferase reporter gene and used it for the detection of small molecules (62). Diazobenzene 1 was observed to produce a substantial increase in the luciferase signal intensity suggesting a downregulation of miR-21 in the presence of diazobenzene 1 (62). This approach can be used to screen small molecule inhibitors of cancer promoting miRNAs (oncomiRs) and used in combination with other strategies for cancer treatment, however, there are none in clinical trials to date.

Restoration of downregulated miRNA expression can be achieved with the help of viral based systems which typically use adenovirus-associated virus (AAV) (63), retroviruses, and lentiviruses for delivery (64) (Fig. 3B). Similarly, miRNA mimics are synthetic oligonucleotides that are identical to endogenous miRNAs and are more commonly used for restoring the activity of miRNA tumor suppressors (Fig. 3C). For example, restoration of miR-15a and miR-16--1 cluster targeting the oncogenes BCL2 (B-cell CLL/lymphoma 2), CCND1 (cyclin D1), and WNT3A (wingless-type MMTV integration site family member 3A) in prostate cancer cells has been shown to induce apoptosis (7). Therefore, intratumoral injection of miRNA mimics may prove beneficial in decreasing tumor formation in humans, however, confirmatory in vivo studies are still required.

Inhibition of upregulated miRNAs can be achieved by using miRNA sponges which are oligonucleotide constructs with multiple miRNA binding sites complementary to the target miRNA (Fig. 3D). When introduced into the cell, sponges will "absorb" endogenous miRNAs, decreasing the expression levels of an oncogenic miRNA, thereby, reversing the suppression ofendogenous target genes when delivered into cells (65). Alternatively, targeted genome editing can be achieved by using the CRISPR technology which can modify miRNA genes, thereby, inhibiting the transcription of oncomiRs (66) (Fig. 3E).

Similarly, antisense oligonucleotides, called antimiRs, function as single-stranded competitive inhibitors by binding to the mature target miRNA (Fig. 3F). The antimiR oligonucleotides bind with perfect complementarity to the target miRNA, and are chemically modified for improved resistance to nucleases and efficient miRNA silencing. Commonly used modifications include the addition of 2'-O-methyl groups or 2'-O-methoxyethyl groups or bicyclic sugar modifications in which 2 sugar derivatives are bridged for nuclease resistance and increased affinity to RNA for inhibition of miRNA expression in cancer cell lines (67). Krutzfeldt et al. intravenously injected chemically modified antimiRs conjugated with cholesterol, referred to as antagomirs, to target miR-16, miR-122, miR-192, and miR-194 in vivo in mice and observed a reduction of the corresponding miRNAs in multiple organs. The silencing effect of the antagomirs was found to be efficient and long-lasting (68). Locked nucleic acid (LNA) antimiRs are nucleic acid analogs in which the ribose ring is locked by a methylene bridge to increase solubility, as well as hybridization affinity to complementary ssRNA (single stranded RNA) and ss or dsDNA (69). LNA antimiRs have been used in several miRNA knockdown studies in vivo, one of which reported the effective silencing of miR-122 by LNA antimiR-122 in mice and African green monkeys (70).

Another method to block miRNA activity is by the use of miR-mask oligonucleotides which are synthetic oligonucleotides complementary to the 3' untranslated region (3'UTR) target mRNA that compete with endogenous miRNAs for its target (Fig. 3G). Therefore, the miR-mask is able to block oncogenic miRNA deleterious effects at the target level and activate translation of target mRNAs (71). A list of miRNA-based therapeutics in development is given in Table 3. Some of these compounds targeting miRNAs in prostate cancer may prove beneficial to treatment outcomes. For example, the let-7 mimic is in the preclinical phase as a potential miRNA replacement therapy for cancer (48). On the other hand, miR-21 is overexpressed in prostate cancer and the development of an antimiR against this miRNA is underway which could be an effective treatment option for prostate cancer patients (48). Other miRNA therapeutics in clinical trials for other diseases which might be used in prostate cancer treatment include the miR-221 antimiR, miR-16 mimic, and miR-103/105 antimiR. Although several miRNA therapeutics are in the clinical development pipeline, miR-34 is the first miRNA mimic to enter Phase 1 clinical testing as a replacement therapy (48, 72, 73). Although new strides have been made, miRNA based therapeutics are still in the early stages of development and require rigorous preclinical testing before being used in the clinic. In addition, to that pharmacogenomics of miRNA based cancer therapy should be taken into consideration.

Genetic variation influences a patient's response to therapy, a term referred to as pharmacogenomics, which aims at understanding how genetic variants influence treatment efficacy and toxicity in individuals (74). The role of single nucleotide polymorphisms (SNPs) in the development and progression of cancer and their role in diagnostics and risk prediction is becoming evident in this context. SNPs are known to underlie differences in our susceptibility to diseases and can be easily determined which makes them interesting biomarkers. The rising interest in the role of SNPs in the development and progression of cancer has been highlighted by several studies (75). SNPs in the miRNA processing genes of the miRNA biogenesis pathway and in the miRNA binding sites, referred to as miRSNPs are known to contribute toward cancer risk. For example, the SNPs rs274034 and rs7813 located in GEMIN4 (gem nuclear organelle associated protein 4), one of the miRNA machinery genes were found to contribute to prostate cancer protection/risk in patients carrying the GC and TT variant genotypes (76). A large-scale analysis of genetic variants in miRNA binding sites identified 22 prostate cancer risk associated miRSNPs, the 2 most significant miRSNPs being KLK3 (kallikrein related peptidase 3) rs1058205 (T>C) and VAMP8 (vesicle associated membrane protein 8) rs1010 (A>G), which were targeted by miR-3162-5p and miR-370-5p respectively in an allele specific manner (77). Similarly, the C allele of the rs4245739 SNP (A>C) located in the 3'UTR of the MDM4 (MDM4 p53 regulator) oncogene is a target of miR-191-5p and miR-887 resulting in prostate cancer protection (78). Therefore, in addition to miRNA expression profiles, cancer diagnosis and therapeutics may rely on the presence of miRSNPs in either precursor miRNAs, mature miRNAs or in 3' UTRs of miRNA target genes. The presence of a specific miRSNP allele, especially in its target gene can predict an individual's response to a particular miRNA based therapy, and is thus an emerging research focus in the miRNA field.

Conclusion and Future Challenges

The emerging potential of miRNAs as diagnostic and prognostic biomarkers, as well as modulators for the treatment of a variety of diseases, makes miRNA targeted therapy a growing area of interest in precision medicine. Precision medicine solely depends on a targeted-therapy linked to a diagnostic test designed to determine precisely whether a patient will benefit from the specific treatment. The potential of miRNAs in cancer diagnostics is rapidly becoming evident with the development of several diagnostic miRNA panels as discussed already. Observations that miRNAs display high stability and differential expression in body fluids still make them attractive noninvasive diagnostic and prognostic biomarkers for all cancers, including prostate cancer.

In addition to the advancements in the establishment of miRNAs as diagnostic and prognostic biomarkers, researchers and pharmaceutical companies have progressed well in developing miRNA-based therapies that are in preclinical and clinical development for a variety of diseases. Some of these compounds targeting miRNAs in prostate cancer may prove beneficial to treatment outcomes. Although evidence suggests that antimiR-mediated silencing or mimic mediated restoration of miRNAs could be powerful strategies for the treatment of human cancers, including prostate cancer, it is clear that there are still several unanswered issues that need to be resolved. These include off-target effects and delivery of miRNA-based anticancer therapies. Additionally, there might be potential drug resistance mechanisms, such as miRSNPs, within the body that could neutralize the effects of the miRNA-based drug before it reaches its target. As the field of miRNA therapeutics expands, a better understanding of miRNA biogenesis and function along with their detection methodologies will aid in their development and implications for precision medicine. Despite current obstacles, targeting of miRNA function by mimics or inhibitors has been achieved and this is a clear indication of the developmental progress in the field of miRNA based precision medicine.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: Cancer Australia Priority-Driven Collaborative Grant 1068321 (241544-0358/51); F. Matin, QUT Postgraduate Research Award (QUTPRA) and QUT HDR Tuition Fee Sponsorship; V. Jeet, the Australian Government Department of Health funding to the Australian Prostate Cancer Research Centre-Queensland; J.A. Clements, NHMRC Principal Research Fellowship; J. Batra, NHMRC Career Development Fellowship.

Expert Testimony: None declared.

Patents: None declared.

Acknowledgments: The authors thank Ms. Madeleine Flynn for all the illustrations in the review article.

References

(1.) Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65:87-108.

(2.) Izumi K, Ikeda H, Maolake A, Machioka K, Nohara T, Narimoto K, et al. The relationship between prostate-specific antigen and TNM classification or Gleason score in prostate cancer patients with low prostate-specific-antigen levels. Prostate 2015; 75:1034-42.

(3.) Mullane SA, Van Allen EM. Precision medicine for advanced prostate cancer. Curr Opin Urol 2016; 26:231-9.

(4.) Lin S, Gregory RI. MicroRNA biogenesis pathways in cancer. Nat Rev Cancer 2015; 15:321-33.

(5.) Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol 2014; 15:509-24.

(6.) Zhang Q, Padi SK, Tindall DJ, Guo B. Polycomb protein EZH2 suppresses apoptosis by silencing the proapoptotic miR-31. Cell Death Dis 2014; 5:e1486.

(7.) Bonci D, Coppola V, Musumeci M, Addario A, Giuffrida R, Memeo L, et al. The miR-15a-miR-16-1 cluster controls prostate cancer by targeting multiple oncogenic activities. Nat Med 2008; 14:1271-7.

(8.) Galardi S, Mercatelli N, Giorda E, Massalini S, Frajese GV, CiafreSA, FaraceMG. miR-221 and miR-222 expression affects the proliferation potential of human prostate carcinoma cell lines by targeting p27Kip1. J Biol Chem 2007; 282:23716-24.

(9.) Gong AY, Eischeid AN, Xiao J, Zhao J, Chen D, Wang ZY, et al. miR-17-5ptargets the p300/CBP-associated factor and modulates androgen receptortranscriptional activity in cultured prostate cancer cells. BMC Cancer 2012; 12:492.

(10.) Kong D, Li Y, Wang Z, Banerjee S, Ahmad A, Kim HR, Sarkar FH. miR-200 regulates PDGF-D-mediated epithelial-mesenchymal transition, adhesion, and invasion of prostate cancer cells. Stem Cells 2009; 27: 1712-21.

(11.) Yamakuchi M, Ferlito M, Lowenstein CJ. miR-34a repression of SIRT1 regulates apoptosis. Proc Natl Acad Sci USA2008; 105:13421-6.

(12.) Fang YX, Gao WQ. Roles of microRNAs during prostatic tumorigenesis and tumor progression. Oncogene 2014; 33:135-47.

(13.) Ambs S, Prueitt RL, Yi M, Hudson RS, Howe TM, Petrocca F, et al. Genomic profiling of microRNA and messenger RNA reveals deregulated microRNA expression in prostate cancer. Cancer Res 2008; 68:6162-70.

(14.) Huang F, Tang J, Zhuang X, Zhuang Y, Cheng W, Chen W, et al. MiR-196a promotes pancreatic cancer progression by targeting nuclear factor kappa-B-inhibitor alpha. PLoS One 2014; 9:e87897.

(15.) Parikh A, Lee C, Joseph P, Marchini S, Baccarini A, Kolev V, et al. microRNA-181a has a critical role in ovarian cancer progression through the regulation of the epithelial-mesenchymal transition. Nat Commun 2014; 5:2977.

(16.) Lee SH, Johnson D, Luong R, Sun Z. Crosstalking between androgen and PI3K/AKT signaling pathways in prostate cancer cells. J Biol Chem 2015; 290:2759-68.

(17.) Aqeilan RI, Calin GA, Croce CM. miR-15a and miR-16--1 in cancer: discovery, function and future perspectives. Cell Death Differ 2010; 17:215-20.

(18.) Sun D, Lee YS, Malhotra A, Kim HK, Matecic M, Evans C, et al. miR-99 family of MicroRNAs suppresses the expression of prostate-specific antigen and prostate cancer cell proliferation. Cancer Res 2011; 71:1313-24.

(19.) Boyerinas B, Park SM, Hau A, Murmann AE, Peter ME. The role of let-7 in cell differentiation and cancer. Endocr Relat Cancer 2010; 17:F19-36.

(20.) Schaefer A, Jung M, Mollenkopf HJ, Wagner I, Stephan C, Jentzmik F, et al. Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer 2010; 126:1166-76.

(21.) Martens-Uzunova ES, Jalava SE, Dits NF, van Leenders GJ, Moller S, Trapman J, et al. Diagnostic and prognostic signatures from the small non-coding RNA transcriptome in prostate cancer. Oncogene 2012; 31:978-91.

(22.) Larne O, Martens-Uzunova E, Hagman Z, Edsjo A, Lippolis G, den Berg MS, et al. miQ-a novel microRNA based diagnostic and prognostic tool for prostate cancer. Int J Cancer 2013; 132:2867-75.

(23.) Casanova-Salas I, Rubio-Briones J, Calatrava A, Mancarella C, Masia E, Casanova J, et al. Identification of miR-187 and miR-182 as biomarkers of early diagnosis and prognosis in patients with prostate cancer treated with radical prostatectomy. J Urol 2014; 192:252-9.

(24.) Gu Y, Lei D, Qin X, Chen P, Zou YM, Hu Y. Integrated analysis reveals together miR-182, miR-200c and miR-221 can help in the diagnosis of prostate cancer. PLoS One 2015; 10:e0140862.

(25.) Lichner Z, Ding Q, Samaan S, Saleh C, Nasser A, Al-Haddad S, et al. miRNAs dysregulated in association with Gleason grade regulate extracellular matrix, cytoskeleton and androgen receptor pathways. J Pathol 2015; 237:226-37.

(26.) Lichner Z, Fendler A, Saleh C, Nasser AN, Boles D, Al-Haddad S, et al. MicroRNA signature helps distinguish early from late biochemical failure in prostate cancer. Clin Chem 2013; 59:1595-603.

(27.) Sun T, Wang X, He HH, Sweeney CJ, Liu SX, Brown M, et al. MiR-221 promotesthe development of androgen independence in prostate cancer cells via downregulation of HECTD2 and RAB1A. Oncogene 2014; 33:2790-800.

(28.) Goto Y, Kojima S, Nishikawa R, Kurozumi A, Kato M, Enokida H, et al. MicroRNA expression signature of castration-resistant prostate cancer: the microRNA-221/222 cluster functions as a tumour suppressor and disease progression marker. Br J Cancer 2015; 113: 1055-65.

(29.) Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, RemaleyAT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 2011; 13:423-33.

(30.) Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, Yu L, et al. Detection of microRNA expression in human peripheral blood microvesicles. PLoS One 2008; 3:e3694.

(31.) Zhou W, Fong MY, Min Y, Somlo G, Liu L, Palomares MR, et al. Cancer-secreted miR-105 destroys vascular endothelial barriers to promote metastasis. Cancer Cell 2014; 25:501-15.

(32.) Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA2008; 105:10513-8.

(33.) Westermann AM, Schmidt D, Holdenrieder S, Moritz R, Semjonow A, Schmidt M, et al. Serum microRNAs as biomarkersin patients undergoing prostate biopsy: results from a prospective multi-center study. Anticancer Res 2014; 34:665-9.

(34.) Yaman Agaoglu F, Kovancilar M, Dizdar Y, Darendeliler E, Holdenrieder S, Dalay N, Gezer U. Investigation of miR-21, miR-141, and miR-221 in blood circulation of patients with prostate cancer. Tumour Biol 2011; 32: 583-8.

(35.) Kotb S, Mosharafa A, Essawi M, Hassan H, Meshref A, Morsy A. Circulating miRNAs 21 and 221 asbiomarkers for early diagnosis of prostate cancer. Tumour Biol 2014; 35:12613-7.

(36.) Selth LA, Townley SL, Bert AG, Stricker PD, Sutherland PD, Horvath LG, et al. Circulating microRNAs predict biochemical recurrence in prostate cancer patients. Br J Cancer 2013; 109:641-50.

(37.) Selth LA, Townley S, Gillis JL, Ochnik AM, Murti K, Macfarlane RJ, et al. Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease. Int J Cancer 2012; 131:652-61.

(38.) Nguyen HC, Xie W, Yang M, Hsieh CL, Drouin S, Lee GS, Kantoff PW. Expression differences of circulating microRNAs in metastatic castration resistant prostate cancer and low-risk, localized prostate cancer. Prostate 2013; 73:346-54.

(39.) Huang X, Yuan T, Liang M, Du M, Xia S, Dittmar R, et al. Exosomal miR-1290 and miR-375 as prognostic markers in castration-resistant prostate cancer. Eur Urol 2015; 67:33-41.

(40.) Bryant RJ, Pawlowski T, Catto JW, Marsden G, Vessella RL, Rhees B, et al. Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer 2012; 106:768-74.

(41.) Mihelich BL, Maranville JC, Nolley R, Peehl DM, Nonn L. Elevated serum microRNA levels associate with absence of high-grade prostate cancer in a retrospective cohort. PLoS One 2015; 10:e0124245.

(42.) Srivastava A, Goldberger H, Dimtchev A, Ramalinga M, Chijioke J, Marian C, et al. MicroRNA profiling in prostate cancer-the diagnostic potential of urinary miR-205 and miR-214. PLoS One 2013; 8:e76994.

(43.) Haj-Ahmad TA, Abdalla MA, Haj-Ahmad Y. Potential urinary protein biomarker candidates for the accurate detection of prostate cancer among benign prostatic hyperplasia patients. J Cancer 2014; 5:103-14.

(44.) Korzeniewski N, Tosev G, PahernikS, Hadaschik B, Hohenfellner M, Duensing S. Identification of cell-free microRNAs in the urine of patients with prostate cancer. Urol Oncol 2015; 33:16.e7-22.

(45.) Viticchie G, Lena AM, Latina A, Formosa A, Gregersen LH, Lund AH, et al. MiR-203 controls proliferation, migration and invasive potential of prostate cancer cell lines. Cell Cycle 2011; 10:1121-31.

(46.) Lewis H, Lance R, Troyer D, Beydoun H, Hadley M, Orians J, et al. miR-888 is an expressed prostatic secretions-derived microRNA that promotes prostate cell growth and migration. Cell Cycle 2014; 13:227-39.

(47.) Mueller WC, Spector Y, Edmonston TB, St Cyr B, Jaeger D, Lass U, et al. Accurate classification of metastatic brain tumors using a novel microRNA-based test. Oncologist 2011; 16:165-74.

(48.) Hydbring P, Badalian-Very G. Clinical applications of microRNAs. F1000 Res 2013; 2:136.

(49.) Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, et al. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 2008; 26:462-9.

(50.) Filella X, Foj L. miRNAs as novel biomarkersin the management of prostate cancer. Clin Chem Lab Med [Epub ahead of print 2016 Jan 9].

(51.) Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N, Reid G. Haemolysis during sample preparation alters microRNA content of plasma. PLoS One 2011; 6:e24145.

(52.) Moldovan L, Batte KE, Trgovcich J, Wisler J, Marsh CB, Piper M. Methodological challenges in utilizing miRNAs as circulating biomarkers. J Cell Mol Med 2014; 18:371-90.

(53.) Gomes CP, Cho JH, Hood L, Franco OL, Pereira RW, Wang K. A review of computational tools in microRNA discovery. Front Genet 2013; 4:81.

(54.) Li W, Ruan K. MicroRNA detection by microarray. Anal Bioanal Chem 2009; 394:1117-24.

(55.) Campuzano S, Torrente-Rodriguez RM, Lopez-Hernandez E, Conzuelo F, Granados R, Sanchez-Puelles JM, Pingarron JM. Magnetobiosensors based on viral protein p19 for microRNA determination in cancer cells and tissues. Angew Chem Int Ed Engl 2014; 53:6168-71.

(56.) Hu T, Zhang L, Wen W, Zhang X, Wang S. Enzyme catalytic amplification of miRNA-155 detection with graphene quantum dot-based electrochemical biosensor. Biosens Bioelectron 2016; 77:451-6.

(57.) Lee H, Park JE, Nam JM. Bio-barcode gel assay for microRNA. Nat Commun 2014; 5:3367.

(58.) Li J, Lei P, Ding S, Zhang Y, Yang J, Cheng Q, Yan Y. An enzyme-free surface plasmon resonance biosensor for real-time detecting microRNA based on allosteric effect of mismatched catalytic hairpin assembly. Biosens Bioelectron 2015; 77:435-41.

(59.) Zhang K, Wang K, Zhu X, Xie M. A one-pot strategy for the sensitive detection of miRNA by catalyst-oligomer-mediated enzymatic amplification-based fluorescence biosensor. Sens Actuators B Chem 2016; 223:586-90.

(60.) Di Leva G, Garofalo M, Croce CM. MicroRNAs in cancer. Annu Rev Pathol 2014; 9:287-314.

(61.) Taylor MA, Schiemann WP. Therapeutic opportunities for targeting microRNAs in cancer. Mol Cell Ther 2014; 2:1-13.

(62.) Gumireddy K, Young DD, Xiong X, Hogenesch JB, Huang Q, Deiters A. Small-molecule inhibitors of mi crorna miR-21 function. Angew Chem Int Ed Engl 2008; 47:7482-4.

(63.) Kota J, Chivukula RR, O'Donnell KA, Wentzel EA, Montgomery CL, Hwang HW, et al. Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model. Cell 2009; 137:1005-17.

(64.) Yang N. An overview of viral and nonviral delivery systems for microRNA. Int J Pharm Investig 2015; 5:179-81.

(65.) Ebert MS, Neilson JR, Sharp PA. MicroRNA sponges: competitive inhibitors of small RNAs in mammalian cells. Nat Methods 2007; 4:721-6.

(66.) Sander JD, Joung JK. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat Biotechnol 2014; 32:347-55.

(67.) Garzon R, Marcucci G, Croce CM. Targeting microRNAs in cancer: rationale, strategies and challenges. Nat Rev Drug Discov 2010; 9:775-89.

(68.) Krutzfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M. Silencing of microRNAs in vivo with 'antagomirs'. Nature 2005; 438:685-9.

(69.) Vester B, Wengel J. LNA (locked nucleic acid): high-affinity targeting of complementary RNA and DNA. Biochemistry 2004; 43:13233-41.

(70.) Elmen J, Lindow M, Schutz S, Lawrence M, Petri A, Obad S, et al. LNA-mediated microRNA silencing in nonhuman primates. Nature 2008; 452:896-9.

(71.) Wang Z. The principles of MiRNA-masking antisense oligonucleotides technology. Methods Mol Biol 2011; 676:43-9.

(72.) Li Z, Rana TM. Therapeutic targeting of microRNAs: current status

and future challenges. Nat Rev Drug Discov 2014; 13:622-38.

(73.) van Rooij E, Kauppinen S. Development of microRNA therapeutics is coming of age. EMBO Mol Med 2014; 6: 851-64.

(74.) Wheeler HE, Maitland ML, Dolan ME, Cox NJ, Ratain MJ. Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet 2013; 14:23-34.

(75.) Van den Broeck T, Joniau S, Clinckemalie L, Helsen C, Prekovic S, Spans L, et al. The role of single nucleotide polymorphisms in predicting prostate cancer risk and therapeutic decision making. Biomed Res Int 2014: 627510, 2014.

(76.) Liu J, Liu J, Wei M, He Y, Liao B, Liao G, et al. Genetic variants in the microRNA machinery gene GEMIN4 are associated with risk of prostate cancer: a case-control study of the Chinese Han population. DNA Cell Biol 2012; 31:1296-302.

(77.) Stegeman S, Amankwah E, Klein K, O'Mara TA, Kim D, Lin HY, et al. A large-scale analysis of genetic variants within putative miRNA binding sites in prostate cancer. Cancer Discov 2015; 5:368-79.

(78.) Stegeman S, Moya L, Selth LA, Spurdle AB, Clements JA, Batra J. A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer. Endocr Relat Cancer 2015; 22:265-76.

(79.) Wallace TJ, Torre T, Grob M, Yu J, Avital I, Brucher B, et al. Current approaches, challenges and future directions for monitoring treatment response in prostate cancer. J Cancer 2014; 5:3-24.

Farhana Matin, [1,2] Varinder Jeet, [1,2] Judith A. Clements, [1,2] George M. Yousef, [3] and Jyotsna Batra [1,2], *

[1] School of Biomedical Sciences, Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Australian Prostate Cancer Research Centre-Queensland (APCRC-Q), Australia; [2] Translational Research Institute, Queensland University of Technology, Brisbane, Australia; [3] Molecular Diagnostics, Department of Laboratory Medicine, St. Michael's Hospital, Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.

* Address correspondence to this author at: Translational Research Institute, 37 Kent street, Woollongabba, QLD-4102, Brisbane, Australia. Fax +61-734437364; e-mail; jyotsna.batra@qut.edu.au.

Received January 11, 2016; accepted June 10, 2016.

Previously published online at DOI: 10.1373/clinchem.2015.242800

[4] Nonstandard abbreviations: PSA, prostate specific antigen; miRNA, microRNA; primiRNA, primary miRNA; pre-miRNA, precursor miRNA; dsRNA, double stranded RNA; DGCR8, dsRNA-binding protein of DiGeorge Syndrome Critical Region Gene 8; EXP5, exportin 5; RISC, RNA induced silencing complex; AGO, argonaute protein; TUTase, terminal uridylyl transferase; ac-pre-miR-451, AGO-cleaved pre-miR-451; PARN, poly(A)-specific ribonuclease; TUT, terminal uridyl transferase; [m.sup.7]G, 7-methylguanosine; MYC, v-myc avian myelocytomatosis viral oncogene homolog; MYOD1, myoblast determination protein 1; ZEB, zinc finger E-box binding homeobox; BPH, benign prostatic hyperplasia; BCR, biochemical recurrence; CRPC, castration resistant prostate cancer; DRE, digital rectal examination; EPS, expressed prostatic secretions; NGS, next-generation sequencing; cDNA, complementary DNA; Strep-HRP polymer, streptavidin protein conjugated to horseradish peroxidase polymer; GQD, graphene quantum dot; AuNP, gold nanoparticles; SPR, surface plasmon resonance; CHA, catalytic hairpin assembly; CRISPR, clustered regularly interspaced short palindromic repeat; oncomiR, tumor promoting miRNA; AAV, adenovirus-associated vector; LNA, locked nucleic acid; 3"UTR, 3" untranslated region; SNP, single nucleotide polymorphism.

Caption: Fig. 1. Regulation of canonical and noncanonical pathways of miRNA biogenesis.

I. Regulation of canonical biogenesis of various miRNAs. (A), Epigenetic silencing mediated miRNA processing. (B), Genetic alterations mediated miRNA processing. Epigenetic modifications and genetic alterations regulate the transcription of miR-148a, miR-10b, miR-15/16 cluster, and miR101 which are processed through the canonical pathway. (C), Oncogenes, tumor suppressors, and transcription factors mediated miRNA processing. p53, MYC, MYOD1, ZEB 1 and 2, and p27Kip1 regulate the transcription of miR-34, miR-17, miR-1, miR-15a, miR-200, and miR-221 clusters respectively. (D), Regulation of Drosha mediated miRNA processing. Homologous proteins, such as p68 and p72, KH-type splicing regulatory protein (KSRP), HNRNPA1 (heterogeneous nuclear ribonucleoprotein A1) and LIN28(lin-28 homolog A) regulate Drosha-mediated pri -miRNA processing of miR-16, miR-143, miR-145, miR-21, miR-199a, miR-18a, and let-7. Adenosine deaminase RNA-specific (ADAR) 1 and 2 mediate RNA editing from adenosine to inosine, which interferes with Drosha processing of miR-142. (E), Regulation of Dicer mediated miRNA processing. KSRP also regulate the biogenesis of let-7, miR-21, and miR-16 at the Dicer processing step. miRNA tailing byTUT4 and TUT7, regulation by BCDIN3D (BCDIN3 domain containing RNA methyltransfease), RNA editing by ADAR1, a reduction in RNA stability controlled by MCPIP1 (MCP-induced protein 1), and IRE1[alpha] (Ser/Thr protein kinase/endoribonuclease) regulate Dicer processing of let-7, miR-145, miR-23b, miR-146a, miR-135b, miR-17, miR-34a, miR-96, miR-125b, and miR-151 respectively. II. Regulation of non-canonical biogenesis of miRNAs. In the Dicer independent miRNA biogenesis of miR-451, pre-miR-451 is thoughtto be exported to the cytoplasm by EXP5 where it utilizes AGO2 to form an ac-pre-miR-451 which is processed by a PARN to form the mature miR-451. In the TUTase dependent pathway, pre-miRNAs with a shorter 3" overhang such as the members of the let-7 family undergo monouridylation by TUTases, TUT2, TUT4, and TUT7 for efficient Dicer processing. The [m.sup.7]G-capped pre-miR-320 bypasses Drosha processing and it is exported to the cytoplasm by EXP1 for Dicer processing. The Drosha-DGCR8 independent noncanonical pathway can also produce short spliced-out introns using the spliceosome-machinery, called mirtrons, to form pre-miRNAs which are exported to the cytoplasm by EXP5 for Dicer processing.

Caption: Fig. 2. Methods of miRNA detection.

(A), Next generation sequencing involves the use of 3' and 5' adaptors which bind to both ends of the miRNAs in the total RNA sample. The adaptors act as primer binding sites during cDNA library preparation and PCR amplification. This is followed by parallel sequencing of individual cDNA molecules to determine the precise order of nucleotides within an miRNA. (B), miRNA microarrays enable screening of miRNA concentrations using a single microchip which allows the analysis of hundreds of miRNA sequences simultaneously. The total RNA extracted is first treated to fluorescently label all miRNAs and then used for hybridization with oligonucleotide probes immobilized on a microchip, followed by visualization of miRNAs by fluorescence read-out of the array. (C), Electrochemical measurement is based on the selective binding between p19 viral protein and dsRNA formed by the hybridization of biotinylated antimiR-21 probe to the miR-21 target. The hybrid is labelled with Strep-HRP polymer and subjected to electrochemical detection using a magneto-biosensor which detects miRNAs in total RNA without sample processing or PCR amplification. (D), Electrophoresis-based method (bio-barcode gel or bio-BaGel assay) detects multiple miRNAs using a conventional gel electrophoresis platform. The final measuring signal of the bio-BaGel assay is obtained from the amount of barcode DNA(an artificially designed DNA representing each miRNA target sequence in such a way that a specific barcode label represents a specific product during gel electrophoresis).

Caption: Fig. 3. miRNA based anticancer therapies.

(A), Small-molecule inhibitors can modulate miRNA expression during transcription of miRNAs. (B), miRNA vectors such as the AAV can be used to restore downregulated miRNA expression by using a viral based delivery system. (C), miRNA mimics are synthetic miRNA oligonucleotides which mimic endogenous miRNAs and can be used to restore downregulated miRNAs. (D), miRNA sponges are oligonucleotide constructs with multiple miRNA binding sites which capture the target miRNA, decreasing the expression levels of an oncogenic miRNA. (E),The CRISPR-Cas9 technology uses small RNAs with an on-target specificity of approximately 18-20 nucleotides. This system can be used to edit miRNA genes to prevent biogenesis of tumor causing miRNAs. (F), Antisense oligonucleotides can bind to the target miRNAs to induce either degradation or duplex formation which prevents miRNA binding to tumor suppressor genes. (G), miR-masks are synthetic oligonucleotides complementary to the 3'UTR of the target mRNA that compete with endogenous miRNAs for its target to block oncogenic miRNA binding and activate translation of target mRNAs.
Table 1. miRNAs associated with prostate cancer diagnosis (studies
from 2008-2015 have been compiled; see online Supplemental Data for
references that are not included in the reference list of the
published article).

Sample type                            miRNAs
                                    deregulated

Prostate cancer tissues (n =     9 upregulated, 76
16)and normal tissues            downregulated
(n = 10)

Prostate cancer tissues (n =     1 downregulated
31) and matched normal
tissues (n =31)

Prostate cancer tissues (n =     1 upregulated
85) vs BPH tissues (n = 53)

Prostate cancer tissues (n =     5 upregulated, 10
76) and adjacent normal          downregulated
tissues (n = 76)

Prostate cancer tissues (n =     16 upregulated, 17
26) and normal tissues           downregulated
(n = 26)

Prostate cancer tissues (n =     30 deregulated
20) and adjacent normal
tissues (n = 20)

Prostate cancer tissues (n =     12 upregulated, 13
102) and adjacent normal         downregulated
tissues (n = 102)

Prostate cancer tissues (n =     56 downregulated
15) and non-prostate
cancer tissues (n = 17)

Prostate cancer tissues (n =     25 deregulated
20) and adjacent non-
cancerous tissues (n = 20)

Prostate cancer tissues (n =     1 downregulated
73) and BPH tissues
(n =64)

Prostate cancer tissues (n =     1 downregulated
149) and matched normal
tissues (n = 30)

Primary prostate cancer          5 upregulated, 5
tissues (n = 4) and              downregulated
adjacent BPH tissues
(n =4)

Prostate cancer tissues (n =     7 deregulated
49) and normal tissues
(n = 25)

Prostate cancer tissues (n =     2 downregulated
40) and adjacent normal
tissues (n = 40)

Prostate cancer tissues (n =     4 deregulated
53) and adjacent normal
tissues (n = 53)

Prostate cancer tissues (n =     6 deregulated
40) and normal tissues
(n = 40)

Prostate cancer tissues          2 downregulated
(n = 54), noncancerous

prostate cancer tissues
(n = 38) and CRPC tissues
(n =8)

Prostate cancer (n = 12 low,     6 upregulated, 4
n = 12 medium and n = 12         downregulated
high risk) (n = 36) vs
healthy controls (n = 12)
serum

Metastatic (n = 12) vs low       1 upregulated
and medium risk
(n = 24) prostate cancer
serum

Prostate cancer (ADb and AI)     1 upregulated
(n = 15 and n = 8) vs
healthy controls (n = 20)
plasma

AD (n = 15) vs AI (n = 8)
prostate cancer plasma

Prostate cancer (n = 21) vs      1 upregulated
controls (n = 0) plasma

Prostate cancer (n = 78) vs      11 upregulated, 1
healthy controls (n = 28)        downregulated
plasma

Non-metastatic                   9 upregulated, 1
(n = 55) vs healthy              downregulated
controls (n = 28) plasma

Metastatic (n = 16) vs non-      15 upregulated, 1
metastatic (n = 55) plasma       downregulated

Prostate cancer (n = 118) vs     5 upregulated
healthy controls (n = 17)
urine

Prostate cancer (n = 36) vs      2 downregulated
healthy controls (n = 12)
urine

Prostate cancer patients (n =    2 upregulated
not given) vs healthy
controls (n = not given)
serum

Prostate cancer (n = 31) vs      10 upregulated
BPH patients (n = 13)
serum

Prostate cancer (n = 54) vs      1 upregulated
non-malignant prostate
cancer patients (n = 79)
serum

Prostate cancer (n = 8) vs       2 deregulated
BPH patients (n = 12) and
healthy controls (n = 10)
urine

Prostate cancer (n = 59) vs      2 deregulated
BPH patients (n = 16)
plasma

Prostate cancer (n = 71) vs      3 upregulated
healthy controls (n = 18)
urine

Sample type                                 miRNAs selected
                                             as candidate
                                            biomarkers (a)

Prostate cancer tissues (n =     let-7, miR-30,
16)and normal tissues            miR-16 downregulated
(n = 10)

Prostate cancer tissues (n =     miR-205 downregulated
31) and matched normal
tissues (n =31)

Prostate cancer tissues (n =     miR-20a upregulated
85) vs BPH tissues (n = 53)

Prostate cancer tissues (n =     miR-96 upregulated
76) and adjacent normal
tissues (n = 76)

Prostate cancer tissues (n =     miR-375, miR-148a, miR-200c
26) and normal tissues           upregulated; miR-143, miR145,
(n = 26)                         miR-223 downregulated

Prostate cancer tissues (n =     miR-622, miR-30d, miR-425,
20) and adjacent normal          miR-342-3p upregulated; miR-126,
tissues (n = 20)                 * miR-34a, * miR-195, miR-26a,
                                 miR-29a * downregulated

Prostate cancer tissues (n =     miR-19a, miR-130a, miR-20a, miR-106,
102) and adjacent normal         miR-93 upregulated; miR-27, miR-143,
tissues (n = 102)                miR-221, miR-222 downregulated

Prostate cancer tissues (n =     miR-222, miR-31 downregulated
15) and non-prostate
cancer tissues (n = 17)

Prostate cancer tissues (n =     miR-375 upregulated; miR-143,
20) and adjacent non-            miR-145 downregulated
cancerous tissues (n = 20)

Prostate cancer tissues (n =     miR-145 downregulated
73) and BPH tissues
(n =64)

Prostate cancer tissues (n =     miR-205 downregulated
149) and matched normal
tissues (n = 30)

Primary prostate cancer          miR-1224-5p, miR-1249, miR-663
tissues (n = 4) and              upregulated miR-205, miR- 221,
adjacent BPH tissues             miR-155, miR-455-3P, miR-193a-5p
(n =4)                           downregulated

Prostate cancer tissues (n =     miR-96-5p, miR-183-5p upregulated;
49) and normal tissues           miR-145-5p, miR-221-5p
(n = 25)                         downregulated

Prostate cancer tissues (n =     miR-205, miR-214 downregulated
40) and adjacent normal
tissues (n = 40)

Prostate cancer tissues (n =     miR-182-5p upregulated
53) and adjacent normal
tissues (n = 53)

Prostate cancer tissues (n =     miR-20a, miR-148a, miR-200b,
40) and normal tissues           miR-375 upregulated; miR-143,
(n = 40)                         miR-145 downregulated

Prostate cancer tissues          miR-221/222 downregulated
(n = 54), noncancerous

prostate cancer tissues
(n = 38) and CRPC tissues
(n =8)

Prostate cancer (n = 12 low,     miR-1207-5p, miR-874 upregulated;
n = 12 medium and n = 12         miR-223 downregulated
high risk) (n = 36) vs
healthy controls (n = 12)
serum

Metastatic (n = 12) vs low       miR-451 upregulated
and medium risk
(n = 24) prostate cancer
serum

Prostate cancer (ADb and AI)     miR-221 upregulated
(n = 15 and n = 8) vs
healthy controls (n = 20)
plasma

AD (n = 15) vs AI (n = 8)
prostate cancer plasma

Prostate cancer (n = 21) vs      miR-141 upregulated (in combination
controls (n = 0) plasma          with PSA and LDH levels)

Prostate cancer (n = 78) vs      miR-141 upregulated; miR-181a-2 *
healthy controls (n = 28)        downregulated
plasma

Non-metastatic                   miR-107, miR-574-3p upregulated;
(n = 55) vs healthy              miR-181a-2 * downregulated
controls (n = 28) plasma

Metastatic (n = 16) vs non-      miR-141, miR-375, miR-200b
metastatic (n = 55) plasma       upregulated; miR-572 downregulated

Prostate cancer (n = 118) vs     miR-107, miR-574-3p upregulated
healthy controls (n = 17)
urine

Prostate cancer (n = 36) vs      miR-205, miR-214 downregulated
healthy controls (n = 12)
urine

Prostate cancer patients (n =    miR-21-5p, miR-93-5p upregulated
not given) vs healthy
controls (n = not given)
serum

Prostate cancer (n = 31) vs      miR-562, miR-210, miR-501-3p,
BPH patients (n = 13)            miR-375, miR-551b, let-7a,
serum                            * miR-616, miR-708, miR-1203,
                                 miR-200a upregulated

Prostate cancer (n = 54) vs      miR-141 upregulated
non-malignant prostate
cancer patients (n = 79)
serum

Prostate cancer (n = 8) vs       miR-1825 (only in prostate cancer)
BPH patients (n = 12) and        upregulated; miR-484 (in prostate
healthy controls (n = 10)        cancer and BPH) downregulated
urine

Prostate cancer (n = 59) vs      miR-375 downregulated
BPH patients (n = 16)
plasma

Prostate cancer (n = 71) vs      miR-483-5p upregulated
healthy controls (n = 18)
urine

Sample type                          References

Prostate cancer tissues (n =     Ozen etal. (2008)
16)and normal tissues
(n = 10)

Prostate cancer tissues (n =     Gandellini et al.
31) and matched normal           (2009)
tissues (n =31)

Prostate cancer tissues (n =     Pesta etal. (2010)
85) vs BPH tissues (n = 53)

Prostate cancer tissues (n =     Schaefer et al.
76) and adjacent normal          (2010) (20)
tissues (n = 76)

Prostate cancer tissues (n =     Szczyrba et al.
26) and normal tissues           (2010)
(n = 26)

Prostate cancer tissues (n =     Carlsson et al.
20) and adjacent normal          (2011)
tissues (n = 20)

Prostate cancer tissues (n =     Martens-Uzunova
102) and adjacent normal         etal. (2012)
tissues (n = 102)                (21)

Prostate cancer tissues (n =     Fuse etal. (2012)
15) and non-prostate
cancer tissues (n = 17)

Prostate cancer tissues (n =     Wach et al.
20) and adjacent non-            (2012)
cancerous tissues (n = 20)

Prostate cancer tissues (n =     Avgeris et al.
73) and BPH tissues              (2013)
(n =64)

Prostate cancer tissues (n =     Hulf et al. (2013)
149) and matched normal
tissues (n = 30)

Primary prostate cancer          He etal. (2013)
tissues (n = 4) and
adjacent BPH tissues
(n =4)

Prostate cancer tissues (n =     Larne et al. (22)
49) and normal tissues
(n = 25)

Prostate cancer tissues (n =     Srivastava et al.
40) and adjacent normal          (2013) (42)
tissues (n = 40)

Prostate cancer tissues (n =     Tsuchiyama et al.
53) and adjacent normal          (2013)
tissues (n = 53)

Prostate cancer tissues (n =     Hart etal. (2014)
40) and normal tissues
(n = 40)

Prostate cancer tissues          Goto et al. (2015)
(n = 54), noncancerous           (28)

prostate cancer tissues
(n = 38) and CRPC tissues
(n =8)

Prostate cancer (n = 12 low,     Moltzahn et al.
n = 12 medium and n = 12         (2011)
high risk) (n = 36) vs
healthy controls (n = 12)
serum

Metastatic (n = 12) vs low
and medium risk
(n = 24) prostate cancer
serum

Prostate cancer (ADb and AI)     Zheng et al.
(n = 15 and n = 8) vs            (2011)
healthy controls (n = 20)
plasma

AD (n = 15) vs AI (n = 8)
prostate cancer plasma

Prostate cancer (n = 21) vs      Gonzales et al.
controls (n = 0) plasma          (2011)

Prostate cancer (n = 78) vs      Bryant et al.
healthy controls (n = 28)        (2012) (40)
plasma

Non-metastatic
(n = 55) vs healthy
controls (n = 28) plasma

Metastatic (n = 16) vs non-
metastatic (n = 55) plasma

Prostate cancer (n = 118) vs     Bryant et al.
healthy controls (n = 17)        (2012) (40)
urine

Prostate cancer (n = 36) vs      Srivastava et al.
healthy controls (n = 12)        (2013) (42)
urine

Prostate cancer patients (n =    Farina et al.
not given) vs healthy            (2014)
controls (n = not given)
serum

Prostate cancer (n = 31) vs      Haldrup et al.
BPH patients (n = 13)            (2014)
serum

Prostate cancer (n = 54) vs      Westermann
non-malignant prostate           et al. (2014)
cancer patients (n = 79)         (33)
serum

Prostate cancer (n = 8) vs       Haj-Ahmad et al.
BPH patients (n = 12) and        (2014) (43)
healthy controls (n = 10)
urine

Prostate cancer (n = 59) vs      Kachakova et al.
BPH patients (n = 16)            (2015)
plasma

Prostate cancer (n = 71) vs      Korzeniewski
healthy controls (n = 18)        et al. (2015)
urine                            (44)

(a) *, Form of miRNA present in low levels; low risk/grade prostate
cancer, PSA < 10 ng/mL and Gleason score [less than or equal to] 6
and clinical stage T1-T2a (79); medium risk/grade prostate cancer,
PSA 10-20 ng/mL or Gleason score 7 or clinical stage T2b-T2c (79);
high risk/grade prostate cancer, PSA [greater than or equal to] 20
ng/mL or Gleason score 8-10 or clinical stage T3a (79).

(b) AD, androgen dependent prostate cancer; AI, androgen independent
prostate cancer; LDH, lactate dehydrogenase.

Table 2. miRNAs associated with prostate cancer prognosis (studies
from 2008-2015 have been compiled; see online Supplemental data for
references that are not included in the reference list of the
published article).

Sample type                             miRNAs
                                     deregulated

Micro-dissected tumor tissues     21 upregulated, 21
(n = 60) and normal tissues (n    downregulated
= 16)

FFPEa prostatectomy specimens     2 upregulated, 4
(n = 40) (n = 20 without and n    downregulated
= 20 with early BCR)

Primary prostate cancer (n =      5 downregulated
6) and bone metastatic tissues
(n = 7)

Primary prostate cancer (n =      4 upregulated, 3
28), CRPC (n = 14) and BPH        downregulated
tissues (n = 12)

Prostate cancer (n = 73) vs       1 downregulated
BPH tissues (n = 66)

FFPE High-risk prostate cancer    2 downregulated
(n = 13) (CPFS n = 7 and CF n
= 6) vs BPH tissues (n = 6)
(validated on entire high-risk
cohort n = 98)

FFPE High grade (Gleason grade    3 upregulated
5) (n = 12) vs intermediate
grade (Gleason grade 4)
prostate tumor tissues

Prostate cancer tissues (n =      3 downregulated
82) (BCR n = 41 and
disease-free n = 41)

CRPC (n = 14) vs BPH tissues      7 deregulated
(n = 7)

Prostate cancer (n = 54),         2 downregulated
noncancerous prostate cancer
(n = 38) and CRPC tissues (n =
8)

Metastatic prostate cancer (n     5 upregulated
= 25) vs healthy controls (n =
25) serum

Metastatic (n = 7)vs localized    5 upregulated
(n = 14) prostate cancer serum

Localized prostate cancer (n =    5 upregulated
37) vs BPH (n = 18)serum

Metastatic (n = 25) vs            3 upregulated
localized/ locally advanced (n
= 26) prostate cancer plasma

Prostate cancer (20 localized,    1 upregulated
20 AD, 10 HRPC) (n = 50) vs
BPH (n = 6) serum

Prostate cancer (AD and AI) (n    1 upregulated
= 15 and n = 8) vs healthy
controls (n = 20) plasma AD (n
= 15) vs AI (n = 8) prostate
cancer plasma

Metastatic (n = 47) vs            2 upregulated
non-recurrent (n = 72)
prostate cancer serum

Prostate cancer (15               4 upregulated, 2
non-metastatic and 10             downregulated
metastatic) (n = 25) vs BPH
(n = 17) plasma

Metastatic CRPC (n = 25) vs       4 upregulated
healthy controls (n = 25)
serum

Intermediate (n = 21) and high    2 upregulated
(n = 9) vs low risk (n = 52)
prostate cancer plasma (CAPRA
score)

Intermediate (n = 27) and high    2 upregulated
(n = 17) vs low risk (n = 38)
prostate cancer plasma
(D'Amico score)

High (n = 17) vs low risk (n =    4 upregulated
38) prostate cancer plasma
(D'Amico score)

Metastatic CRPC (n = 25) vs       10 deregulated
localized (n = 25) prostate
cancer plasma

Metastatic CRPC (n = 25) vs       5 upregulated
healthy controls (n = 25)
serum

Metastatic CRPC (n = 26) vs       3 upregulated, 1
localized (n = 58) prostate       downregulated
cancer serum

BCR (n = 8) vs healthy            3 upregulated
controls (n = 8) serum

Prostate cancer (Gleason          3 deregulated
grade [greater than or
equal to] 7) (n = 48) vs
(Gleason grade <6) (n = 48)
serum

High grade (n = 6) vs low         1 upregulated, 2
grade (n = 25) prostate           downregulated
cancer EPS urine

High grade (Gleason grade 4       14 downregulated
and/or 5) (n = 50) vs low
grade (Gleason grade 3)
(n = 50) and BPH (n = 50)
serum

Prostate cancer (n = 23) vs       1 upregulated, 3
BPH (n = 25) PSS                  downregulated

Prostate cancer (n = 75) vs       4 deregulated
BPH (n = 27) blood

Sample type                          miRNAs selected as candidate
                                              biomarkers

Micro-dissected tumor tissues     miR-32, miR-26a, miR-182, miR-
(n = 60) and normal tissues (n    31, miR-106b, miR-93, miR-25,
= 16)                             miR-99b miR-125a
                                  upregulated

FFPEa prostatectomy specimens     miR-135, miR-194 upregulated;
(n = 40) (n = 20 without and n    miR-145, miR-221, miR-222
= 20 with early BCR)              downregulated

Primary prostate cancer (n =      miR-508-5p, miR-143, miR-145,
6) and bone metastatic tissues    miR-33a, miR-100
(n = 7)                           downregulated

Primary prostate cancer (n =      miR-32, miR-148a, miR-590-5p,
28), CRPC (n = 14) and BPH        miR-21 upregulated; miR-99a,
tissues (n = 12)                  miR-99b, miR-221
                                  downregulated

Prostate cancer (n = 73) vs       miR-224 downregulated
BPH tissues (n = 66)

FFPE High-risk prostate cancer    let-7b downregulated
(n = 13) (CPFS n = 7 and CF n
= 6) vs BPH tissues (n = 6)
(validated on entire high-risk
cohort n = 98)

FFPE High grade (Gleason grade    miR-31-5p, miR-182-5p,
5) (n = 12) vs intermediate       miR-205-5p
grade (Gleason grade 4)
prostate tumor tissues

Prostate cancer tissues (n =      miR-1, miR-133b downregulated
82) (BCR n = 41 and
disease-free n = 41)

CRPC (n = 14) vs BPH tissues      miR-1247-5p upregulated
(n = 7)

Prostate cancer (n = 54),         miR-221/222 downregulated
noncancerous prostate cancer
(n = 38) and CRPC tissues (n =
8)

Metastatic prostate cancer (n     miR-141 upregulated
= 25) vs healthy controls (n =
25) serum

Metastatic (n = 7)vs localized    miR-141, miR-375 upregulated
(n = 14) prostate cancer serum

Localized prostate cancer (n =    miR-26a upregulated
37) vs BPH (n = 18)serum

Metastatic (n = 25) vs            miR-21, miR-141, miR-221
localized/ locally advanced (n    upregulated
= 26) prostate cancer plasma

Prostate cancer (20 localized,    miR-21 upregulated
20 AD, 10 HRPC) (n = 50) vs
BPH (n = 6) serum

Prostate cancer (AD and AI) (n    miR-221 upregulated
= 15 and n = 8) vs healthy
controls (n = 20) plasma AD (n
= 15) vs AI (n = 8) prostate
cancer plasma

Metastatic (n = 47) vs            miR-141, miR-375 upregulated
non-recurrent (n = 72)
prostate cancer serum

Prostate cancer (15               miR-346, miR-622, miR-940,
non-metastatic and 10             miR-1285 upregulated; let-7e,
metastatic) (n = 25) vs BPH       let-7c downregulated
(n = 17) plasma

Metastatic CRPC (n = 25) vs       miR-141, miR-298, miR-346,
healthy controls (n = 25)         miR-375 upregulated
serum

Intermediate (n = 21) and high    miR-20a, miR-21 upregulated
(n = 9) vs low risk (n = 52)
prostate cancer plasma (CAPRA
score)

Intermediate (n = 27) and high    miR-21, miR-145 upregulated
(n = 17) vs low risk (n = 38)
prostate cancer plasma
(D'Amico score)

High (n = 17) vs low risk (n =    miR-20a, miR-21, miR-145,
38) prostate cancer plasma        miR-221 upregulated
(D'Amico score)

Metastatic CRPC (n = 25) vs       miR-141, miR-151-3p, miR-16
localized (n = 25) prostate       upregulated
cancer plasma

Metastatic CRPC (n = 25) vs       miR-210 upregulated
healthy controls (n = 25)
serum

Metastatic CRPC (n = 26) vs       miR-141, miR-375, miR-378b
localized (n = 58) prostate       upregulated; miR-409-3p
cancer serum                      downregulated

BCR (n = 8) vs healthy            miR-194, miR-146-3p
controls (n = 8) serum            upregulated

Prostate cancer (Gleason          miR-19, miR-345, miR-519c-5p
grade [greater than or            upregulated
equal to] 7) (n = 48) vs
(Gleason grade <6) (n = 48)
serum

High grade (n = 6) vs low         miR-888 upregulated
grade (n = 25) prostate
cancer EPS urine

High grade (Gleason grade 4       let-7a, miR-24, miR-26b, miR-30c,
and/or 5) (n = 50) vs low         miR-93, miR-100, miR-103,
grade (Gleason grade 3)           miR-106a, miR-107, miR-130b,
(n = 50) and BPH (n = 50)         miR-146a, miR-223, miR-451,
serum                             miR-874 downregulated

Prostate cancer (n = 23) vs       miR-203 upregulated; miR-361-
BPH (n = 25) PSS                  3p, miR-133b, miR-221
                                  downregulated

Prostate cancer (n = 75) vs       miR-141, miR-145, miR-155, let-
BPH (n = 27) blood                7a upregulated

Sample type                          References

Micro-dissected tumor tissues     Ambs et al.
(n = 60) and normal tissues (n    (2008) (13)
= 16)

FFPEa prostatectomy specimens     Tong et al.
(n = 40) (n = 20 without and n    (2009)
= 20 with early BCR)

Primary prostate cancer (n =      Peng et al.
6) and bone metastatic tissues    (2011)
(n = 7)

Primary prostate cancer (n =      Jalava et al.
28), CRPC (n = 14) and BPH        (2012)
tissues (n = 12)

Prostate cancer (n = 73) vs       Mavridis et al.
BPH tissues (n = 66)              (2013)

FFPE High-risk prostate cancer    Schubert et al.
(n = 13) (CPFS n = 7 and CF n     (2013)
= 6) vs BPH tissues (n = 6)
(validated on entire high-risk
cohort n = 98)

FFPE High grade (Gleason grade    Tsuchiyama
5) (n = 12) vs intermediate       etal.(2013)
grade (Gleason grade 4)
prostate tumor tissues

Prostate cancer tissues (n =      Karatas et al.
82) (BCR n = 41 and               (2014)
disease-free n = 41)

CRPC (n = 14) vs BPH tissues      Scaravilli et al.
(n = 7)                           (2015)

Prostate cancer (n = 54),         Goto et al.
noncancerous prostate cancer      (2015) (28)
(n = 38) and CRPC tissues (n =
8)

Metastatic prostate cancer (n     Mitchell et al.
= 25) vs healthy controls (n =    (2008) (32)
25) serum

Metastatic (n = 7)vs localized    Brase et al.
(n = 14) prostate cancer serum    (2011)

Localized prostate cancer (n =    Mahn et al.
37) vs BPH (n = 18)serum          (2011)

Metastatic (n = 25) vs            Yaman Agaoglu
localized/ locally advanced (n    etal. (2011)
= 26) prostate cancer plasma      (34)

Prostate cancer (20 localized,    Zhang et al.
20 AD, 10 HRPC) (n = 50) vs       (2011)
BPH (n = 6) serum

Prostate cancer (AD and AI) (n    Zheng et al.
= 15 and n = 8) vs healthy        (2011)
controls (n = 20) plasma AD (n
= 15) vs AI (n = 8) prostate
cancer plasma

Metastatic (n = 47) vs            Bryant et al.
non-recurrent (n = 72)            (2012) (40)
prostate cancer serum

Prostate cancer (15               Chen et al.
non-metastatic and 10             (2012)
metastatic) (n = 25) vs BPH
(n = 17) plasma

Metastatic CRPC (n = 25) vs       Selth et al.
healthy controls (n = 25)         (2012) (37)
serum

Intermediate (n = 21) and high    Shen et al.
(n = 9) vs low risk (n = 52)      (2012)
prostate cancer plasma (CAPRA
score)

Intermediate (n = 27) and high
(n = 17) vs low risk (n = 38)
prostate cancer plasma
(D'Amico score)

High (n = 17) vs low risk (n =
38) prostate cancer plasma
(D'Amico score)

Metastatic CRPC (n = 25) vs       Watahiki et al.
localized (n = 25) prostate       (2013)
cancer plasma

Metastatic CRPC (n = 25) vs       Cheng et al.
healthy controls (n = 25)         (2013)
serum

Metastatic CRPC (n = 26) vs       Nguyen et al.
localized (n = 58) prostate       (2013) (38)
cancer serum

BCR (n = 8) vs healthy            Selth et al.
controls (n = 8) serum            (2013) (37)

Prostate cancer (Gleason          Wang et al.
grade [greater than or            (2014)
equal to] 7) (n = 48) vs
(Gleason grade <6) (n = 48)
serum

High grade (n = 6) vs low         Lewis et al.
grade (n = 25) prostate           (2014) (46)
cancer EPS urine

High grade (Gleason grade 4       Mihelich et al.
and/or 5) (n = 50) vs low         (2015) (41)
grade (Gleason grade 3)
(n = 50) and BPH (n = 50)
serum

Prostate cancer (n = 23) vs       Guzel et al.
BPH (n = 25) PSS                  (2015)

Prostate cancer (n = 75) vs       Kelly etal.
BPH (n = 27) blood                (2015)

(a) FFPE, formalin fixed paraffin embedded; CPFS, clinical
progression free survival; CF, clinical failure; HRPC, hormone
refractory prostate cancer; AD, androgen dependent prostate cancer;
AI, androgen independent prostate cancer; EPS, expressed prostatic
secretions; PSS, prostatic secretion samples.

(b) Form of miRNA present in low levels; low risk/grade prostate
cancer, PSA < 10 ng/mL and Gleason score [less than or equal to] 6
and clinical stage T1-T2a (79); medium risk/grade prostate cancer,
PSA 10-20 ng/mL or Gleason score 7 or clinical stage T2b-T2c (79);
high risk/grade prostate cancer, PSA [greater than or equal to] 20
ng/mL or Gleason score 8-10 or clinical stage T3a (79).

Table 3. miRNA based anticancer therapies in development. (a)

miRNA target         Mode of action

miR-122        antimiR (LNA modified)
miR-122        antimiR (GalNAc conjugated)
miR-34         miRNA mimic

Let-7          miRNA mimic

miR-21         antimiR (bicyclic sugar
                 modified)
miR-21         antimiR (bicyclic sugar
                 modified)
miR-208        antimiR
miR-195/15     antimiR

miR-221        antimiR
miR-103/105    antimiR
miR-10b        antimiR
miR-16         miRNA mimic (TargomiRs)

miR-145        antimiR
miR-451        antimiR

miR-92a        antimiR
miR-29b        miRNA mimic

miRNA target             Disease               Sponsor/Company

miR-122        HCV infection                 Santaris Pharma
miR-122        HCV infection                 Regulus Therapeutics
miR-34         Unresectable primary          Mirna Therapeutics
                 liver cancer
Let-7          Cancer (details               Mirna Therapeutics
                 undisclosed)
miR-21         Hepatocellular carcinoma      Regulus Therapeutics

miR-21         Kidney fibrosis               Regulus Therapeutics

miR-208        Heart failure                 miRagen/Sevier
miR-195/15     Post-myocardial infarction    miRagen/Sevier
                 remodelling
miR-221        Hepatocellular carcinoma      Regulus Therapeutics
miR-103/105    Insulin resistance            Regulus Therapeutics
miR-10b        Glioblastoma                  Regulus Therapeutics
miR-16         Pleural mesothelioma and      University of Sydney
                 non-small cell lung
                 cancer
miR-145        Vascular disease              miRagen/Sevier
miR-451        Myeloproliferative            miRagen/Sevier
                 disorder
miR-92a        Peripheral arterial disease   miRagen/Sevier
miR-29b        Cutaneous scleroderma         miRagen/Sevier

miRNA target    Clinical
                 status

miR-122        Phase II
miR-122        Phase I
miR-34         Phase I

Let-7          Preclinical

miR-21         Preclinical

miR-21         Preclinical

miR-208        Preclinical
miR-195/15     Preclinical

miR-221        Preclinical
miR-103/105    Preclinical
miR-10b        Preclinical
miR-16         Phase I

miR-145        Preclinical
miR-451        Preclinical

miR-92a        Preclinical
miR-29b        Phase 1

(a) See Hydbring etal. (48), Rana (72), and van Rooij
and Kauppinen (73).
COPYRIGHT 2016 American Association for Clinical Chemistry, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Matin, Farhana; Jeet, Varinder; Clements, Judith A.; Yousef, George M.; Batra, Jyotsna
Publication:Clinical Chemistry
Date:Oct 1, 2016
Words:12287
Previous Article:Commentary.
Next Article:Comprehensive Assessment of M-Proteins Using Nanobody Enrichment Coupled to MALDI-TOF Mass Spectrometry.
Topics:

Terms of use | Privacy policy | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters