• bladder;
  • miRNA;
  • expression profiling;
  • homozygous deletion;
  • FGFR3 mutation


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results and discussion
  5. References
  6. Supporting Information

We analyzed 34 cases of urothelial carcinomas by miRNA, mRNA and genomic profiling. Unsupervised hierarchical clustering using expression information for 300 miRNAs produced 3 major clusters of tumors corresponding to Ta, T1 and T2-T3 tumors, respectively. A subsequent SAM analysis identified 51 miRNAs that discriminated the 3 pathological subtypes. A score based on the expression levels of the 51 miRNAs, identified muscle invasive tumors with high precision and sensitivity. MiRNAs showing high expression in muscle invasive tumors included miR-222 and miR-125b and in Ta tumors miR-10a. A miRNA signature for FGFR3 mutated cases was also identified with miR-7 as an important member. MiR-31, located in 9p21, was found to be homozygously deleted in 3 cases and miR-452 and miR-452* were shown to be over expressed in node positive tumors. In addition, these latter miRNAs were shown to be excellent prognostic markers for death by disease as outcome. The presented data shows that pathological subtypes of urothelial carcinoma show distinct miRNA gene expression signatures. © 2008 Wiley-Liss, Inc.

MicroRNAs (miRNAs) are short single-stranded noncoding RNA sequences, 20-22nt in length, that play an important role in posttranscriptional gene regulation via an antisense RNA-RNA interaction. Similar to conventional mRNAs, miRNAs are transcribed by RNA polymerase II as long primary transcripts known as pri-miRNA with a cap and poly (A) tail.1, 2 These pri-miRNAs are then processed in the nucleus to short, 70nt stem-loop structures known as pre-miRNAs, by the RNase III-type endonuclease DROSHA, and the double-stranded-RNA-binding protein DGCR8.3–5 The pre-miRNAs are then exported into the cytoplasm and undergo further processing by the RNase III endonuclease DICER to yield the mature miRNA.6–8 Although the steps in miRNA biogenesis are well characterized, the mechanism by which miRNAs regulate gene expression remains unclear. Mature miRNAs are integrated into the RNA-induced silencing complex, miRISC, base pair with mRNA molecules, and induce mRNA degradation, increase the rate of mRNA degradation by the normal decay pathways or inhibit translation.9, 10 In humans, near-perfect complementary is believed to be prerequisite for RISC-mediated cleavage, but not translational repression.11 Currently, there are close to 700 human miRNAs (miRNABase release 11.0, but recent reports estimate 1,000 or more miRNAs to be present in the human genome.12, 13 Most miRNAs are found in intergenic regions or within introns of either protein-coding or noncoding mRNA transcripts.

Several studies have shown that miRNAs and their machinery are linked to cancer either by acting as tumor suppressor or oncogenes. Components of the miRNA-machinery required for miRNA maturation, DICER and DROSHA have been shown to be down-regulated in lung cancer.14 As DICER has shown to be involved in heterochromatin maintenance and centromeric silencing, reduced protein levels of DICER have been suggested to affect genomic stability.15, 16 Kanellopoulou et al.16 showed that mouse embryonic stem cells lacking DICER fail to differentiate normally. Lu et al.17 used global miRNA expression to classify human cancers and showed that miRNAs are down-regulated in tumors compared with normal tissues. Down regulation of miRNAs has been shown in other studies as well, e.g., down regulation of miR-15a and miR-16-1 occur in leukemias and lymphomas and result in increased expression of BCL2,18 and down regulation of miR-143 and miR-145 occurs in colorectal tumors.19 It has been shown that at least 50% of annotated human miRNAs are located in fragile sites20 which may partially explain their frequent down regulation. For example, miR-143 and miR-145 are located within a fragile site at 5q32-33 and are down-regulated in colon and breast-cancer patients,21, 22 and miR-125b-1 is located in a fragile site on chromosome 11q24 and deleted in subsets of patients with breast, lung, ovarian and cervical cancers.21 These investigations indicate that miRNAs may function as tumor suppressors.23 In contrast to this, studies have also shown a strong correlation between increase in miRNA expression and tumorigensis. For example, Chan et al.24 showed that miR-21 is up-regulated in glioblastoma and the miR-17-92 cluster in 13q31 is known to be amplified in B-cell lymphoma and mantel cell lymphoma with increased miRNA expression as a consequence.25

In the present investigation, we have explored miRNA expression patterns in urothelial carcinomas (UC). UC originate from the epithelial cells of the inner lining of the bladder wall. The majority is papillary and confined to the urothelial mucosa (stage Ta) or to the lamina propria (Stage T1), whereas the remaining invades the underlying muscle tissue (T2), perivesical fat (T3) or surrounding organs (T4). Most Ta tumors are of low or medium grade (G1 or G2), rarely progress, and are associated with a favorable prognosis whereas high grade Ta (TaG3) and T1 tumors represent a significant risk of tumor progression. UCs are characterized by a number of chromosomal and genetic alterations of which cytogenetic loss and loss of heterozygosity (LOH) of chromosome 9 is particularly frequent occurring in >50% of the cases.26–28 Furthermore, activating point mutations in the FGFR3 gene are found in >70% of Ta tumors, but rarely in invasive tumors.29 A reverse pattern is seen for TP53, which has led to the suggestion that UC may develop through two different genetic pathways.30 Here, we show that important pathological entities of UC may readily be identified based on their miRNA expression profiles. We also show that both miRNA signatures as well as the expression of individual miRNAs have the potential to be used as efficient diagnostic and prognostic markers.

Material and methods

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results and discussion
  5. References
  6. Supporting Information

Patients and tissue

Tumors were collected by cold-cup biopsies from the exophytic part of the bladder in patients undergoing resection at the University Hospital of Lund, Sweden, between 2001 and 2004 and kept at −80°C until further use. Sample quality was evaluated by histology. Tumor pathology, based on transurethral and cystectomy specimens, was assessed by an experienced pathologist (GC) and is listed together with other clinical data in Supplementary Table I. Altogether 7 Ta, 10 T1 and 17 T2/3 tumors were included in the study. Of these, 10 were low-grade (grades G1 and G2) and 24 high-grade (G3) tumors. Fourteen tumors were investigated for lymph node metastases and 7 of these were found positive. The investigation was performed with informed consent and approved by the local ethical committee.

Nucleic acid isolation

DNA was isolated using the DNeasy Tissue Kit (Qiagen, Valencia, CA), including the optional RNase H treatment, and verified for high quality by agarose gel electrophoresis. For miRNA expression analyses, we used total RNA extracted using Trizol reagent (Invitrogen, Carlsbad, CA). To perform mRNA expression analyses isolated RNA was purified on Qiagen RNeasy columns (Qiagen). Sample integrities were assessed on an Agilent 2100 Bioanalyzer (Agilent technologies, Palo Alto, CA).

MiRNA hybridizations and data processing

MiRNA hybridizations were performed in 96-well formats, Illumina microRNA Expression Profiling Assay, following the manufacturer's instructions (Illumina, CA). In short, 200 ng of total RNA from each sample is polyadenylated with a poly-A polymerase (PAP) enzyme, polyadenylated RNA is then converted to cDNA using a biotinylated oligo-dT primer with a universal PCR sequence followed by hybridization with the set of miRNA-specific oligos (MSO). These MSOs are extended and added flurophore followed by hybridization to beads on the Sentrix Array Matrix. The array matrix was scanned using Illumina BeadArray Reader which measures fluorescence intensity of the signal that corresponds to the quantity of the miRNA in the original sample. The data were subsequently processed and summarized in the Illumina BeadStudio software v3. After quality control, Rank Invariant normalization was performed for 34 samples and expression values transformed to log2 ratios. The Illumina microRNA Expression Profiling Assay is based on miRBase release 9.0. The sequences of many miRNAs have changed and new genes have been added in the current release (11.0). We, therefore remapped the probe set against all human miRNAs in miRBase to identify perfect matches. A small number of probes were shown to detect multiple homologous miRNAs. When necessary, probes/miRNAs with multiple targets are indicated in the text.

Microarray hybridizations

Labeling of test and reference DNA was performed as previously described,31 with slight modifications. In brief, 1.5 μg of tumor and male reference DNA was fluorescently labeled with Cy3-dCTP and Cy5-dCTP (Amersham Biosciences, Uppsala, Sweden), respectively, using the Array CGH labeling kit (Invitrogen, Carlsbad, CA), and purified using filter-based spin columns (Cyscribe GFX Purification kit, Amersham Biosciences). Differentially labeled DNA was pooled, mixed with 100 μg Human Cot-1 DNA (Invitrogen) and lyophilized prior to resuspension in 55 μL hybridization solution (50% formamide, 10% dextran sulfate, 2× SSC, 2% SDS, 10 μg/μL yeast tRNA). Probes were heated at 70°C for 15 minutes and at 37°C for 30 minutes before hybridization to microarrays for 48–72 hr at 37°C. High-resolution tiling BAC arrays produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden ( using the BAC Re-Array set Ver. 1.0 (32,433 BAC clones) previously described32 was used. The BAC Re-Array set was obtained from the BACPAC Resource Center at Children's Hospital Oakland Research Institute, Oakland (CA). Prior to hybridization, microarrays were UV cross-linked at 500 mJ/cm2 and pretreated using the Universal Microarray Hybridization Kit (Corning, Acton, MA) according to the manufacturer's instructions. Slides were washed and scanned as previously described.33 Oligonucleotide arrays printed with 70-mers from the OPERON v3.0 set were obtained from the Swegene DNA microarray resource centre ( The 36,288 oligonucleotides printed on each slide correspond to 18,466 unique Entrez genes. Sample and Universal Human Reference RNA (Stratagene, La Jolla, CA) labeling and microarray hybridization was performed using the Pronto Plus System (Promega, Madison, WI; Coring, Acton, MA) according to the manufacturer's specification. Arrays were scanned with an Agilent G2565AA microarray scanner (Agilent technologies).

Microarray image and data processing

Primary data were collected using the GenePix Pro 4.0 software (Axon Instruments, Foster City, CA) and raw result files were deposited into the web-based database BioArray Software Environment (BASE).34 For genomic profiling, spots were background-corrected using the median foreground minus the median background signal intensities for both dyes and log2 ratios were calculated. Unreliable features, marked in the feature extraction software and spots not showing signal-to-noise ratios ≥3, for both channels, were removed. Data normalization was performed per array subgrid using Lowess curve fitting35 with a smoothing factor of 0.33. Chromosomes X and Y BAC clones were omitted during the estimation of the normalization function. Homozygous deletions were defined as regions with consistent log2 ratios below −1.2, and with at least one BAC showing a log2 ratio <−1.5. Mapping data were obtained from the UCSC genome browser (May 2004 freeze;

Statistical analyses

For hierarchical cluster analysis (HCA), we used Wards algorithm and 1-Pearson correlation as distance measure. We used significance analysis of microarrays (SAM)36 as implemented in MeV v4.137 to identify miRNAs with significantly altered expression. The delta value in SAM was adjusted to obtain a maximum number of significant miRNA while maintaining an FDR < 0.001. Expression scores were produced by taking the mean expression levels of miRNAs showing correlated expression. To evaluate the extent to which the obtained scores could separate tumor cases into predefined subtypes, we performed receiver operating characteristic (ROC) analyses and used the area under curve (AUC) as a measure for the level of separation. AUC equivalent to 0.5 indicates no discriminative and 1.00 maximal discriminative powers.

MiRNA target predictions

Putative target genes were predicted using TargetScan release 4.0.38 All sites in the 3′ UTR with a 6-mer complementary to nucleotides 2–7 from the 5′ end of a miRNA, so called seed sequence, are considered potential target sites by TargetScan 4.0. Each potential target site is given a context score based on features that have been shown to influence the efficacy of a miRNA target site. These features include (i) the type of seed match (if nucleotide 8 of the miRNA is complementary to the corresponding nucleotide in the target site and if the nucleotide opposite nucleotide 1 in the miRNA is an A), (ii) complementarity outside the seed region, (iii) the AU content 30 nucleotides upstream and downstream of the seed and (iv) the position of the target site relative to the ends of the 3′ UTR. More negative context scores identify stronger target sites or sites more likely to be true target sites. Genes with more than one site in their 3′ UTR are also more likely to be genuine targets. We added the score for all putative target sites in each 3′ UTR and ranked potential target genes according to this score. Only sites with negative scores were considered. The association between miRNA expression and expression of putative target genes were then estimated by a correlation analysis using Pearson correlation and by Gen Set Enrichment Analysis39 using identified target genes as gene sets.

Results and discussion

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results and discussion
  5. References
  6. Supporting Information

A total of 34 cases of urothelial carcinomas were selected for miRNA expression analyses using the Illumina microRNA Expression Profiling Assay. The series comprised 7 Ta, 10 T1 and 17 T2/3 tumors. After filtration and normalization information was available for 244 annotated miRNAs and 56 potential miRNAs included in the Illumina microRNA platform. An initial unsupervised hierarchical cluster analysis (HCA) using all 300 miRNAs resulted in 3 major clusters of tumors, one that contained all the Ta tumors; one all but two T1 cases; and one dominated by muscle invasive tumors, i.e., T2/3 tumors. The HCA showed a 76% concordance between cluster assignment and pathological stage (Fig. 1a). Hence, miRNA expressions profiles distinguish the 3 major pathological subtypes of UC. To identify miRNAs with expression profiles significantly associated with stage, a three-class SAM analysis was performed and a total of 51 miRNAs were identified. The subsequent HCA and the miRNA heat map is seen Figure 1b and show 82% concordance between cluster assignment and stage. The identified miRNAs were grouped by the HCA in 2 clusters, I and II in Figure 1b (Supplementary Table II). MiRNA cluster I showed high expression in the Ta tumor cluster, moderate and variable in the T1 tumor cluster, and low expression in the T2/3 tumors, whereas miRNA cluster II showed high expression in muscle invasive tumors and low expression in Ta and T1 tumors. Hence, Ta and muscle invasive tumor showed almost opposite expression levels for the miRNA clusters I and II. No profile specifically associated with T1 tumors could be discerned. We then calculated a score for each case based on the 51 discriminating miRNAs by subtracting the average level of expression for the cluster II miRNAs with the average level for the cluster I miRNAs, which will result in high scores for muscle-invasive tumors. In Figure 2 the obtained scores are plotted against their ranks. It is obvious from Figure 2 that the score discriminate among Ta, T1, and >T1 tumors, respectively. The extent to which the scoring system separated nonmuscle from muscle invasive tumors was evaluated by a ROC analysis, which resulted in an AUC of 0.96. Hence, a miRNA-based stage profile has the potential to identify high risk patients with high accuracy. Apart from showing excellent separating capacity this result also shows that the signature is firmly associated with tumor stage.

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Figure 1. HCA of urothelial carcinomas based on miRNA expression (a) unsupervised HCA using all 300 miRNAs; (b) HCA based on the 51 miRNAs that discriminate Ta, T1 and T2/3 cases identified by the 3-way SAM analysis. On the left side of the heat map a dendogram obtained after HCA of miRNA expression is shown. The dendogram identifies two clusters, I and II, of miRNAs. White lines in the heat map define the tumor cluster assignments.

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Figure 2. Tumor cases rank-ordered and plotted according to the miRNA expression score. Note the good separation of Ta, T1 and T2/3 cases. Green, Ta tumors; blue, T1 tumors; red, T2/3 tumors.

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We then performed two-class SAM analyses between nonmuscle and muscle invasive tumors, i.e., Ta/T1 versus T2/3 tumors and between Ta and T1/2/3 tumors, respectively, and miRNAs that showed >1.75-fold change in expression were selected. This analysis identified 4 miRNAs with high expression in muscle invasive tumors, 3 with low expression and 2 with high expression in Ta tumors (Table I). The miRNAs associated with Ta and with muscle invasive tumors, respectively, did not overlap and may therefore constitute tumor stage specific miRNAs. Among the miRNAs specific for invasive tumors increased expression of miR-222 has been shown to inhibit CDKN1B (p27) and CDKN1C (p57) expression and prevent quiescence,40 and expression of miR-125b has been show to be a positive regulator of cell proliferation.41 Hence, the coordinate expression of miR-222 and miR-125b may contribute to the more aggressive growth behavior of invasive tumors. Recently, high expression of miR-21 and low expression of miR-205 was shown to characterize invasive tumors.42 These miRs were, however, not among the top ranking miRs in the present study. A possible explanation for this discrepancy may be that Neely et al.42 originally selected their miRs using cell lines with in vitro invasive properties and not from tumor specimens with established pathological invasion, which may have affected the nature of the top ranking genes.

Table I. MiRNA Profiles for Invasive and Ta Tumors
miRNAFold changeΔlog2
High-expression in T2/T3 tumors
High-expression in Ta tumors
Low-expression in Ta tumors

The most significant miRNA in the Ta profile was miR-10a that showed 2.5-fold higher expression in Ta tumor compared with T1/2/3 tumors. MiR-10a is located in 17q21 within the HOXB gene cluster between HOXB4 and HOXB5. Expression of miR-10a parallels that of HOXB4 and HOXB5 in hematopoetic cells suggesting a common regulatory mechanism.43 A link has also been suggested between miR-10a expression and cellular differentiation, during which miR-10a is down regulated.43 Gene expression data was, however, not available for HOXB4 and HOXB5 and it remains to be investigated if miR-10a is important also for urothelial cell differentiation. MiR-7 has been shown to inhibit growth factor receptor expression and impair the antiapoptotic Akt-pathway in glioblastoma and may thus have a similar function in UC.44 The low mir-7 expression in Ta tumors is in line with the frequent activation of FGFR3 signaling in this tumor subtype.

We then investigated the association of miRNA expression with frequent genetic alterations in UC; TP53 mutations, homozygous deletions of the CDKN2A/2B/P14ARF locus and FGFR3 mutations. Three miRNAs, miR-192, miR-148b and miR-326, were expressed differentially between TP53wt and mutated cases, they did, however, not discriminate TP53mut from wild-type cases in a following HCA (data not shown). Three of the investigated cases harbored homozygous deletions in 9p21 that included miR-31. Consequently, these cases showed a distinct down regulation of miR-31 expression compatible with a homozygous deletion. All three identified deletions were accompanied by concomitant homozygous deletions of the CDKN2A/2B/p14ARF locus. Homozygous deletions of this locus are one of the most frequent genomic alterations in urothelial carcinomas occurring in more than 30% of the cases.45, 46 As miR-31 is located only 0.46 Mbp proximal to CDKN2A, this miRNA is expected to be lost together with the CDKN2A/2B/p14ARF locus at high frequencies. Indeed, recent molecular mapping data of homozygous 9p21 deletions46 indicates that simultaneous deletions of miR-31 and CDKN2A may occur in up to 50% of the cases. An additional noncoding RNA was recently mapped to this region, ANRIL47, and hence the CDKN2A/2B/p14ARF locus may be more complex than previously appreciated, containing not only the well established CDKN2A/2B/p14ARF target genes but also noncoding RNA species with at present unknown functions.

A SAM analysis identified 4 genes that showed differential expression between FGFR3wt and mutated cases. The subsequent HCA resulted in one major cluster of wild-type cases and one cluster dominated by mutated cases (Figure 3). The finding that 2 of the miRNAs, miR-10a and miR-7, associated with FGFR3 mutations status were also associated with Ta status is most likely a reflection of the frequent FGFR3 mutations seen in Ta tumors.29 In addition, miR-34a and miR-582 showed decreased expression in FGFR3wt cases compared with FGFR3 mutated cases. MiR-34a has been shown to regulate the cell cycle negatively by reducing the CCND1 and CDK4 levels48 and is induced by DNA damage and oncogenic stress in a p53-dependent manner.49 The decreased expression of this miRNA may therefore contribute the more aggressive behavior of FGFR3wt UC cases. As for the stage miRNA profile, we constructed a score based on the miRNAs included in the FGFR3wt/mut signature and rank-ordered the cases according to score (Supplementary Fig. 1). A ROC analysis based on this score resulted in an AUC of 0.90 and, hence, the miRNA profile separates the cases with respect to FGFR3 mutation status with high precision. The obtained high accuracy emphasizes further that the identified signature is a central feature of urothelial carcinomas with activated FGFR3 signaling.

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Figure 3. A HCA based on miRNAs that discriminate FGFR3mut from wild type cases. Green, FGFR3 wild-type; red, FGFR3-mutated.

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The presence of lymph node metastases and the identification of patients with poor prognosis are two important issues when monitoring UC patients. Fourteen patients had been investigated by sentinel node screening and seven were found to be node positive. An initial SAM analysis unambiguously identified 2 miRNAs, miR-452 and miR-452*, associated with metastases in the lymph nodes. The following HCA on the investigated tumors clearly delineated one cluster with node positive tumors and one without, albeit with one misclassified case (Figure 4a). This latter finding most likely reflects that the development of metastases is dependent on several factors50 and may not be reduced to the expression of single genes or miRNAs. As the presence of metastases is associated with bad prognosis, we explored miR-452 and miR-452* as prognostic markers. Cox regression analyses using all 34 tumors revealed that both miRNAs had a strong prognostic impact on death as endpoint, with hazard ratios equal to 8.6 and 8.2 (95% CI ± 5 and p < 0.025 in both cases, Wald statistic) for miR-452 and miR-452*, respectively. In Figure 4b, a Kaplan-Meier analysis is shown in which the threshold expression for miR-452 is set to the upper third of the expression levels using all tumors. This threshold divides the patients into high risk and low risk patients with strong significance (p < 0.004). Hence, miR-452 and miR-452* has the potential to be used as prognostic markers for patients with urothelial carcinoma.

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Figure 4. Hsa-mir-452 and -452* as prognostic markers. (a) A HCA of node+ and node− tumors based on hsa-mir-452 and -452* expression. (b) A Kaplan-Meier analysis based on the expression level of has-miRNA-452 and with death from disease as endpoint.

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We notice that two of the miRNAs, mir-221 and mir-223, identified by our SAM analysis also were identified by Gottardo et al.51 in a comparison between bladder cancer specimens and normal mucosa. Even though the aim of their investigation is different from the present, i.e., comparison of differences between normal and cancerous tissue versus comparisons between pathological and mutational subtypes of UC, their findings corroborate the importance of miR-221 and miR-223 in bladder cancer development. Hence, we conclude that urothelial carcinomas show distinct miRNA gene expression signatures associated with tumor stage. Furthermore, at least four miRNAs may have major influence on the behavior of this tumor type. MiR-10a may be crucial for the establishment of Ta tumors and distinguish this subtype both from T1 and T2/3 tumors as well as FGFR3mut from FGFR3wt cases. Low miR-7 expression distinguishes the important class of FGFR3 mutated urothelial carcinomas and was also negatively associated with Ta tumors. Because of the high frequency of miR-31 homozygous losses this miRNA may be an important factor in determining features of carcinoma behavior and miR-452 and miR-452* may have a central role in the establishment of metastases. However, a proper understanding of the biological outcome caused by altered expression of these miRNAs needs more detailed knowledge of their target genes. For this purpose, we used the TargetScan software to predict likely targets for several of the identified miRNAs. We did, however, not find any general association between miRNA expression and the mRNA levels of the predicted genes. Irrespective of this, miRNA-based gene expression signatures proved to have excellent discriminative power and a potential as prognostic markers for cancer related death.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results and discussion
  5. References
  6. Supporting Information
  • 1
    Bartel DP. MicroRNAs: genomics biogenesis mechanism, function. Cell 2004; 116: 28197.
  • 2
    Gu J,He T,Pei Y,Li F,Wang X,Zhang J,Zhang X,Li Y. Primary transcripts and expressions of mammal intergenic microRNAs detected by mapping ESTs to their flanking sequences. Mamm Genome 2006; 17: 103341.
  • 3
    Lee Y,Ahn C,Han J,Choi H,Kim J,Yim J,Lee J,Provost P,Radmark O,Kim S,Kim VN. The nuclear RNase III Drosha initiates microRNA processing. Nature 2003; 425: 4159.
  • 4
    Denli AM,Tops BB,Plasterk RH,Ketting RF,Hannon GJ. Processing of primary microRNAs by the microprocessor complex. Nature 2004; 432: 2315.
  • 5
    Gregory RI,Yan KP,Amuthan G,Chendrimada T,Doratotaj B,Cooch N,Shiekhattar R. The microprocessor complex mediates the genesis of microRNAs. Nature 2004; 432: 23540.
  • 6
    Hutvagner G,McLachlan J,Pasquinelli AE,Balint E,Tuschl T,Zamore PDX. A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science 2004; 293: 8348.
  • 7
    Yi R,Qin Y,Macara IG,Cullen BR. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 2003; 17: 30116.
  • 8
    Lund E,Guttinger S,Calado A,Dahlberg JE,Kutay U. Nuclear export of microRNA precursors. Science 2004; 303: 958.
  • 9
    Schwarz DS,Hutvagner G,Haley B,Zamore PD. Evidence that siRNAs function as guides not primers, in the Drosophila and human RNAi pathways. Mol Cell 2002; 10: 53748.
  • 10
    von Roretz C,Gallouzi IE. Decoding ARE-mediated decay: is microRNA part of the equation? J Cell Biol 2008; 18: 18994.
  • 11
    Rhoades MW,Reinhart BJ,Lim LP,Burge CB,Bartel B,Bartel DP. Prediction of plant microRNA targets. Cell 2002; 110: 51320.
  • 12
    Bentwich I,Avniel A,Karov Y,Aharonov R,Gilad S,Barad O,Barzilai A,Einat P,Einav U,Meiri E,Sharon E,Spector Y, et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet 2005; 37: 76670.
  • 13
    Berezikov E,van Tetering G,Verheul M,van de Belt J,van Laake L,Vos J,Verloop R,van de Wetering M,Guryev V,Takada S,van Zonneveld AJ,Mano H,Plasterk R,Cuppen E. Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res 2006; 16: 128998.
  • 14
    Karube Y,Tanaka H,Osada H,Tomida S,Tatematsu Y,Yanagisawa K,Yatabe Y,Takamizawa J,Miyoshi S,Mitsudomi T,Takahashi T. Reduced expression of Dicer associated with poor prognosis in lung cancer patients. Cancer Sci 2005; 9: 1115.
  • 15
    Fukagawa T,Nogami M,Yoshikawa M,Ikeno M,Okazaki T,Takami Y,Nakayama T,Oshimura M. Dicer is essential for formation of the heterochromatin structure in vertebrate cells. Nat Cell Biol 2004; 6: 78491.
  • 16
    Kanellopoulou C,Muljo SA,Kung AL,Ganesan S,Drapkin R,Jenuwein T,Livingston DM,Rajewsky K. Dicer-deficient mouse embryonic stem cells are defective in differentiation and centromeric silencing. Genes Dev 2005; 19: 489501.
  • 17
    Lu J,Getz G,Miska EA,Alvarez-Saavedra E,Lamb J,Peck D,Sweet-Cordero A,Ebert BL,Mak RH,Ferrando AA,Downing JR,Jacks T, et al. MicroRNA expression profiles classify human cancers. Nature 2005; 435: 8348.
  • 18
    Cimmino A,Calin GA,Fabbri M,Iorio MV,Ferracin M,Shimizu M,Wojcik SE,Aqeilan RI,Zup S,Dono M,Rassenti L,Alder H,Volinia S,Liu CG,Kipps TJ,Negrini M,Croce CM. miRNA-15 and miRNA-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci USA 2005; 102: 139449.
  • 19
    Michael MZ,O'Conner SM,van Holst Pellekaan NG,Young GP,James RJ. Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol Cancer Res 2003; 1: 88291.
  • 20
    Calin GA,Sevignani C,Dumitru CD,Hyslop T,Noch E,Yendamuri S,Shimizu M,Rattan S,Bullrich F,Negrini M,Croce CM. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA 2004; 101: 29993004.
  • 21
    Iorio MV,Ferracin M,Liu CG,Veronese A,Spizzo R,Sabbioni S,Magri E,Pedriali M,Fabbri M,Campiglio M,Menard S,Palazzo JP, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005; 65: 706570.
  • 22
    Esquela-Kerscher A,Slack FJ. Oncomirs—microRNAs with a role in cancer. Nat Rev Cancer 2006; 6: 25969.
  • 23
    O'Donnell KA,Wentzel EA,Zeller KI,Dang CV,Mendell JT. c-Myc-regulated microRNAs modulate E2F1 expression. Nature 2005; 435: 83943.
  • 24
    Chan JA,Krichevsky AM,Kosik KS. MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res 2005; 65: 602933.
  • 25
    He L,Thomson JM,Hemann MT,Hernando-Monge E,Mu D,Goodson S,Powers S,Cordon-Cardo C,Lowe SW,Hannon GJ,Hammond SM. A microRNA polycistron as a potential human oncogene. Nature 2005; 435: 82833.
  • 26
    Fadl-Elmula I. Chromosomal changes in uroepithelial carcinomas. Cell Chromosome 2005; 4: 15.
  • 27
    Höglund M,Säll T,Heim S,Mitelman F,Mandahl N,Fadl-Elmula I. Identification of cytogenetic subgroups and karyotypic pathways in transitional cell carcinoma. Cancer Res 2001; 61: 82416.
  • 28
    Knowles MA. Identification of novel bladder tumor suppressor genes. Electrophoresis 1999; 20: 26979.
  • 29
    Billerey C,Chopin D,Aubriot-Lorton MH,Ricol D,Gil Diez de Medina S,Van Rhijn B,Bralet MP,Lefrere-Belda MA,Lahaye JB,Abbou CC,Bonaventure J,Zafrani ES, et al. Frequent FGFR3 mutations in papillary non-invasive bladder (pTa) tumors. Am J Pathol 2001; 158: 19559.
  • 30
    Wu XR. Urothelial tumorigenesis, a tale of divergent pathways. Nat Rev Cancer 2005; 5: 71325.
  • 31
    Jönsson G,Staaf J,Olsson E,Heidenblad M,Vallon-Christersson J,Osoegawa K,de Jong P,Oredsson S,Ringnér M,Höglund M,Borg Å. High-resolution genomic profiles of breast cancer cell lines assessed by tiling BAC array comparative genomic hybridization. Genes Chromosomes Cancer 2007; 46: 54358.
  • 32
    Krzywinski M,Bosdet I,Smailus D,Chiu R,Mathewson C,Wye N,Barber S,Brown-John M,Chan S,Chand S,Cloutier A,Girn N, et al. A set of BAC clones spanning the human genome. Nucleic Acids Res 2004; 32: 365160.
  • 33
    Heidenblad M,Hallor KH,Staaf J,Jönsson G,Borg Å,Höglund M,Mertens F,Mandahl N. Genomic profiling of bone and soft tissue tumors with supernumerary ring chromosomes using tiling resolution bacterial artificial chromosome microarrays. Oncogene 2006; 25: 710616.
  • 34
    Saal LH,Troein C,Vallon-Christersson J,Gruvberger S,Borg Å,Peterson C. BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol 2003; 3:SOFTWARE0003.
  • 35
    Yang YH,Dudoit S,Luu P,Lin DM,Peng V,Ngai J,Speed TP. Normalization for cDNA microarray data, a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002; 30: e15.
  • 36
    Tusher VG,Tibshirani R,Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98: 511621.
  • 37
    Saeed AI,Sharov V,White J,Li J,Liang W,Bhagabati N,Braisted J,Klapa M,Currier T,Thiagarajan M,Sturn A,Snuffin M, et al. TM4 a free open-source system for microarray data management and analysis. Biotechniques 2003; 34: 3748.
  • 38
    Grimson A,Kai-How K,Farh K,Johnston W,Garrett-Engele P,Lim L,Barte D. MicroRNA targeting specificity in mammals determinants beyond seed pairing. Mol Cell 2007; 27: 91105.
  • 39
    Subramanian A,Tamayo P,Mootha VK,Mukherjee S,Ebert BL,Gillette MA,Paulovich A,Pomeroy SL,Golub TR,Lander ES,Mesirov JP. Gene set enrichment analysis a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102: 1554550.
  • 40
    Medina R,Zaidi SK,Liu CG,Stein JL,van Wijnen AJ,Groce CM,Stein GS. MicroRNAs 221 and 222 bypass quiescence and compromise cell survival. Cancer Res 2008; 68: 277380.
  • 41
    Lee YS,Kim HK,Chung S,Kim K-S,Dutta A. Depletion of human micro-RNA mir-125b reveals that it is critical for the proliferation of differentiated cells but not for the down-regulation of putative targets during differentiation. J Biol Chem 2005; 280: 1663541.
  • 42
    Neely LA,Rieger-Christ KM,Neto BS,Eroshkin A,Garver J,Patel S,Phung NA,McLaughlin S,Libertino JA,Whitney D,Summerhayes IC. A microRNA expression ratio defining the invasive phenotype in bladder tumors. Urol Oncol, 2008; Sep. 15 [Epub ahead of print].
  • 43
    Garzon R,Pichiorri F,Palumbo T,Iuliano R,Cimmino A,Aqeilan R,Volinia S,Bhatt D,Alder H,Marcucci G,Calin GA,Liu CG, et al. MicroRNA fingerprints during human megakaryocytopoiesis. Proc Nat Acad Sci 2006; 103: 507883.
  • 44
    Kefas B,Godlewski J,Comeau L,Li Y,Abounander R,Hawkinson M,Lee J,Fine H,Chiocca EA,Lawler S,Purow B. MicroRNA-7 inhibits the epidermal growth factor receptor and the Akt pathway and is down-regulated in glioblastoma Cancer Res 2008; 68: 356672.
  • 45
    Chapman EJ,Harnden P,Chambers P,Johnston C,Knowles M. A comprehensive analysis of CDKN2A status in micro dissected urothelial cell carcinoma reveals potential haploinsufficiency: a high frequency of homozygous co-deletion and associations with clinical phenotype. Clin Cancer Res 2005; 1: 57407.
  • 46
    Heidenblad M,Lindgren D,Jonson T,Liedberg F,Veerla S,Chebil G,Gudjonsson S,Borg A,Månsson W,Höglund M. Tiling resolution array CGH and high density expression profiling of urothelial carcinomas delineate genomic amplicons and candidate target genes specific for advanced tumors. BMC Med Genomics 2008; 1: 3.
  • 47
    Pasmant E,Laurendeau I,Héron D,Vidaud M,Vidaud D,Bièche I. Characterization of a germ-line deletion including the entire INK4/ARF locus, in a melanoma-neural system tumor family, identification of ANRIL, an antisense noncoding RNA whose expression co-clusters with ARF. Cancer Res 2007; 67: 39639.
  • 48
    Sun F,Fu H,Liu Q,Tie Y,Zhu J,Xing R,Sun Z,Zhen X. Downregulation of CCND1 and CDK6 by miR-34a induces cell cycle arrest. FEBS Lett 2008; 582: 15648.
  • 49
    He X,He L,Hannon GJ. The guardian's little helper microRNAs in the p53 tumor suppressor network. Cancer Res 2007; 67: 11099100.
  • 50
    Sanchez-Carbayo M,Socci ND,Lozano J,Saint F,Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol 2006; 24: 77889.
  • 51
    Gottardo F,Liu CG,Ferracin M,Calin GA,Fassan M,Bassi P,Sevignani C,Byrne D,Negrini M,Pagano F,Gomella LG,Croce CM,Baffa R. Micro-RNA profiling in kidney and bladder cancers. Urol Oncol 2007; 25: 38792.

Supporting Information

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results and discussion
  5. References
  6. Supporting Information

Additional Supporting Information may be found in the online version of this article.

IJC_24183_sm_suppinfofigure1.tif76KSupporting Figure 1. Tumor cases rank ordered and plotted according to the miRNA expression score for the FGFR3 miRNA profile.
IJC_24183_sm_suppinfotable1.xls21KSupporting Table 1.
IJC_24183_sm_suppinfotable2.xls18KSupporting Table 2. MiRNA clusters assignments.

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