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Keywords:

  • microRNA;
  • high risk prostate carcinoma;
  • metastasis;
  • miR-221;
  • prognosis

Abstract

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Emerging evidence shows that microRNAs (miR) are involved in the pathogenesis of a variety of cancers, including prostate carcinoma (PCa). Little information is available regarding miR expression levels in lymph node metastasis of prostate cancer or the potential of miRs as prognostic markers in this disease. Therefore, we analyzed the global expression of miRs in benign, hyperplastic prostate tissue (BPH), primary PCa of a high risk group of PCa patients, and corresponding metastatic tissues by microarray analysis. Consistent with the proposal that some miRs are oncomirs, we found aberrant expression of several miRs, including the downregulation of miR-221, in PCa metastasis. Downregulation of miR-221 was negatively correlated with the expression of the proto-oncogen c-kit in primary carcinoma. In a large study cohort, the prostate-specific oncomir miR-221 was progressively downregulated in aggressive forms of PCa. Downregulation of miR-221 was associated with clinicopathological parameters, including the Gleason score and the clinical recurrence during follow up. Kaplan–Meier estimates and Cox proportional hazard models showed that miR-221 downregulation was linked to tumor progression and recurrence in a high risk prostate cancer cohort. Our results showed that progressive miR-221 downregulation hallmarks metastasis and presents a novel prognostic marker in high risk PCa. This suggests that miR-221 has potential as a diagnostic marker and therapeutic target in PCa.

Prostate cancer is one of the most common visceral malignant neoplasms in men. It was estimated in 2006 that 345,000 cases were newly diagnosed in the European community. 1 The natural history of prostate carcinoma (PCa) varies from an indolent disease that might not cause symptoms during a patient's lifetime to a highly aggressive cancer that metastasises quickly and causes severe pain and untimely death. The marked disparity between these biological behaviors is not currently understood and had an increasing socioeconomic impact. Over the years, several potential prognostic markers for PCa, including mRNA based gene expression signatures, have been identified.2–4 Unfortunately, both clinical criteria and molecular genetics approaches have had only limited success in patient stratification.

While low risk patients rarely develop tumor progression or clinical recurrence after radical prostatectomy, patients with high risk PCa had a 50% risk of progression at 5 years. 5 Moreover we could show recently in a multicenter study on more than 800 high risk PCa patients that 63% of the patients experienced 15-years metastasis free survival after radical prostatectomy (unpublished data). The main known prognostic factors for treatment efficacy and survival, the PSA-value at diagnosis and the Gleason score,1, 6, 7 were not useful in this high risk cohort as predictor for clinical progression and mortality. Therefore new prognostic markers for high and low risk PCa patients are urgently needed to optimize and to individualize therapy strategies.

MicroRNAs (miR) are small (19–25-nt long), noncoding RNA strands that comprise a new class of regulatory molecules. 8 Various alterations in miR expression were detected in malignant tissues. Furthermore, functional studies of individual miRs have shown that they appear to function in a tissue-specific manner, either as tumor suppressors or oncogenes (oncomiRs).9 miR expression profiles have been used successfully to classify various types of tumors by developmental lineage and state of differentiation10 and recent studies have investigated the role of miRs in mediating breast cancer metastasis,11–13 These endeavors gave rise to the notion that miR s might also be involved in the progression and metastasis of PCa. Previous expression studies have shown that there are characteristic miR signatures in PCa14. However, it is unknown whether these potential oncomiRs are relevant in the malignant progression and metastasis of PCa.

In this study, we identified miRs, including miR-221, that were related to metastasis by comparing miR expression patterns in primary carcinoma tissue and in corresponding metastatic tissue. We further demonstrated that the downregulation of miR-221 was associated with tumor progression, poor prognosis and the development of metastasis. These results suggest that mir-221 is a novel prognostic indicator in high risk PCa and might be a potential target for diagnosis and therapy.

Material and Methods

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Patients and samples

This study was approved by the local ethical committee (no. 59/04) and all patients provided written, informed consent. Samples were paraffin-embedded tissue specimens from either radical prostatectomy, lymph node metastasis (regions with >90% cancerous tissue were used) or prostate adenomectomy (BPH) (regions with >80% adenoid tissue were used). Patients had received operations in the Departments of Urology, at the Community Hospital of Karlsruhe and the University Hospital of Würzburg. All patients were recruited from a well characterized group of high risk PCa. 15 All patients were staged preoperatively with DRE, an abdominopelvic computed tomography (CT) scan and bone scan. Clinical node positive disease was not considered as exclusion criteria. None of the patients had received neoadjuvant hormonal-, radiation- or chemotherapy. Lymph node metastasis and prostate specimens (whole mount sections, 4 mm intervals) were staged and graded according to the 2002 TNM classification and the Gleason grading system by one senior pathologist (P.S.) (Table 1). Follow-up was performed every 3 months for the first 2 years after surgery, every 6 months in the following 3 years, and annually thereafter. Biochemical progression (BP) was defined as PSA ≥0.2 ng/ml on 2 consecutive follow-up visits. Clinical progression was defined either as histologically proven local recurrence or distant metastasis confirmed by CT or bone scan. Overall survival was defined as time from RRP to death of any cause, cancer specific survival as the time from RRP to death attributed to PCa or complications of the disease. BPH samples were derived from prostate adenomectomy specimens. All patients had normal PSA levels before surgery and carcinoma was excluded by histopathology.

Microarray analysis

A set of 665 miRs (Probe Set 1564V2 mirVana, Applied Biosystems) was spotted inhouse on SCHOTT Nexterion™ HiSense E microarray slides in quadruplicate. A complete listing of the probes in the probe set can be found at: www.ambion.com/techlib/resources/miR_array/index.html.

Slide processing was performed according to the Applied Biosystems mirVana™ manuals. For RNA purification from formalin-fixed paraffin embedded (FFPE) material the PureLink FFPE Total RNA Isolation Kit (Invitrogen) was used in combination with the RiboMinus Concentration Module (Invitrogen). All steps were performed according to the recommendations of Invitrogen. Each target was hybridized to a separate array using the Ncode™ Rapid miRNA Labeling System, (Invitrogen).

RNA extraction and reverse transcription

Total RNA was extracted from the PCa and normal epithelial tissues with a Total RNA Extraction Kit (Applied Biosystems). The RNA concentration was determined with a Bioanalyzer (Agilent). cDNA was synthesized from total RNA with stem-loop reverse transcription primers according to the TaqMan miR Assay protocol (PE Applied Biosystems).

QRT-PCR

Mature miR expression was quantified in tissue samples with TaqManR miR assay kits and an Applied Biosystems 7,900HT system. We followed the protocol provided in the manufacture's instructions (Applied Biosystems). The expression of snRNA RNU6b and RNU43 was used for normalization. Relative miRexpression was calculated with the comparative ΔCt-method (ΔCtsample = CtsampleCtRNU6b; ΔCtBPH = CtBPHCtRNU6b). Fold changes in miR expression between samples and controls were determined by the 2math image method described previously. 16 mRNA analysis of c-kit and p27Kip1 expression was performed according to standard qRT-PCR procedures. The expression of both GAPDH and β-Actin was used for normalization. All primer sequences are available under request. Mean Ct was always determined from triplicate PCRs.

Statistical and bioinformatics analysis

Spot intensities from scanned slides were quantified using ScanAlyze Software (M. Eisen, LBNL, CA). Data were analyzed with different R packages from the Bioconductor project (www.bioconductor.org). Resulting signal intensities were normalized by variance stabilization. 17 Differentially expressed genes were selected from the microarray data by limma (Linear Models for Microarray Analysis) package implementing the empirical Bayes linear modelling approach described by Smyth et al.,18, 19 The output is a table of the top-ranked genes determined from the linear model fit including a gene list, ratio on the log (base2) scale, average gene intensities, moderated t-statistics, adjusted p value (FDR) and log odds. Correspondence analysis of microarray data to visualize associations between genes and hybridizations was done as described.20 Analysis of the RT-PCR data was performed with the prediction analysis for microarrays (PAMR) package as described previously.21 Different groups were compared with a Welch Two Sample t-test. Correlation between c-kit or p27kip1 mRNA expression and expression of miR-221 is given as Spearman rank correlation coefficient. Survival analysis was assessed as the time from radical prostatectomy to clinical recurrence, and was performed with survival analysis software (Therneau et al., 1990). MiR-221 data were dichotomized with the intermedian median, which was determined by calculating the median from the combined group medians representing an intuitive threshold for group separation. The associations between factors and clinical recurrence were determined by Cox regression with uni- and multivariate models. p values <0.05 were considered significant.

Results

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Microarray detection of miRs differentially expressed in primary PCas and metastasis

We compared the miR expression in 4 pairs of primary carcinoma and metastasis tissues vs. 4 nonmalignant prostate tissues (BPH) by hybridization of miRs on microarrays. A PCa-specific miR signature was identified by the expression of 48 downregulated and 18 upregulated miRs that were significantly different in carcinoma compared with BPH tissues (adj. p < 0.01, expression ratio >5; Supporting Information Table 1). Hierarchical clustering of the microarray data generated a tree with a clear distinction between 2 major groups: miRs related to PCa/metastasis and miRs related to BPH (Fig. 1a and Supporting Information Fig. 1). We confirmed a robust separation of BPHs from carcinoma with correspondence analysis. The clustering of all 4 metastasis samples showed more similarity to their corresponding primary tumor than to each other (Fig. 1). The expression of miRs in pairs of PCa versus metastasis was analyzed to identify miRs that were differentially expressed in the primary PCa and its corresponding synchronous lymph node metastasis. Statistical analysis yielded a list of miRs that were differentially expressed (adj. p < 0.05, expression ratio >1.5) in BPH versus PCa (143 up, 169 down), in BPH versus metastasis (161 up and 196 down), and in PCa versus metastasis (8 up and 6 down). We found that miR-221 was strongly downregulated in PCa and metastasis compared with non malignant control tissue (Fig. 2a). Hierarchical clustering was performed based on the 14 miRs that were differentially expressed between PCa and metastasis. This generated a tree with clear distinctions between PCa, metastases and BPHs (Fig. 2b).

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Figure 1. (a) Hierarchical cluster analysis of 4 primary prostate carcinomas (PCa-1–4), 4 corresponding metastasis (met1–4), and 4 benign prostatic hyperplasias (BPH). A tree was generated that clearly separates BPH from the carcinomas. Pairs of primary carcinomas and corresponding metastases cluster with each other. (b) Correspondence analysis of 4 primary prostate carcinoma (PCa-1–4, green), 4 corresponding metastasis (met1–4, red), and 4 benign prostatic hyperplasias (BPH, blue). The graph represents a low-dimensional projection to display associations between individual patients and distances between miRs (grey letters). Shorter distances between patients or miRs indicate higher similarities. There is a clear separation between benign and malign cases on the main axis (x-axis). Malign cases cluster with the patient of origin rather than to the tissue of origin. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

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Figure 2. (a) A Venn diagram showing relationships between human miRs that were differentially expressed in BPH versus primary carcinoma (PCa), BPH versus metastasis (met), and primary carcinoma versus metastasis (FDR adj. p value <0.05, expression ratio >2, n = 4 in all groups). Circles include the numbers of up or downregulated miRs for each pairwise comparison. Common miRs between different comparisons are shown in the intersections; red = upregulated, green = downregulated miRs. The list of up or downregulated miRs in primary carcinomas vs. metastases is shown (lower arrow points to the relevant box of miR identities). * indicates ambi-miRs. (b) Cluster dendrogram using average linkage cluster analysis with Eisen's correlation metric shows human miRs that exhibited a >1.5-fold increase or decrease in expression (adj. p < 0.01) in 4 prostate carcinomas vs. 4 corresponding metastases. * indicates ambi-miRs.

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Table 1. Clinical demographics of patients with prostate carcinoma (N = 92)
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Validation of microarray data by real-time RT-PCR analysis of miR-221, -29a, -16 and -125b in primary carcinoma and metastasis

To confirm our microarray data, RT-PCR was performed to analyze the expression of the most significantly regulated miRs, including miR-221, -125b, -29a and -16. The qRT-PCR analysis using 9 BPH, 12 PCa and 12 corresponding metastases confirmed that the overall expression levels of miR-221, -125b, -29a and -16 were downregulated in primary PCa and metastasis samples (p < 0.01 for all 4 miRs) (Fig. 3a). Comparison of BPH samples and non cancerous peripheral zone prostatic tissue showed no difference in expression of all 4 miRs (Supporting Information Fig. 2), indicating that these miRs are not dysregulated in hyperplastic prostatic tissue. The expression levels of miR-125b, -29a and -16 were also comparable between PCa and metastasis samples; however, the overall expression level of miR-221 was downregulated (p < 0.01) in metastasis compared with its primary carcinoma, as expected from the microarray data. As shown in Figure 3b, the expression of miR-221 was downregulated in 11 of 12 metastasis samples in a direct comparison with the primary tumor.

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Figure 3. Real-Time PCR assays in benign hyperplastic prostate tissue, primary carcinoma (PCa) and metastasis (met). (a) Box-and-whisker plots of selected miRs predicted to be aberrantly regulated in prostate cancer. Relative expression of indicated miRs in 9 BPHs (blue), 12 primary carcinomas (green), and 12 corresponding lymph node metastasis (red) was analyzed by qRT-PCR. (b) Downregulation of miR-221 expression in metastasis. Expression levels of miR-221 in 12 pairs of carcinoma and metastases were analyzed by qRT-PCR and calculated by the ΔCt-method. The graph shows the log fold change of mir-221 in metastasis (M1-M12) as compared with the value obtained for the corresponding carcinoma. The samples are shown in ascending order based on log fold change. (c) Correlation of miR-221 and c-kit expression in primary prostate cancer. Relative expression of miR-221 and c-kit was analyzed in 9 BPH (blue), 18 carcinoma samples with high miR-221 expression (PCa A; green) and 18 carcinoma samples with low expression of miR-221 (PCa B, yellow) by qRT-PCR. Box- and whisker plots of miR-221 and c-kit show significant increased expression of c-kit in PCa B samples. In (a) and (c): Median expression levels are indicated with black horizontal bars. The relative expression level in BPHs was arbitrarily set as 0. * indicates p < 0.001. p values were calculated using the Welch 2 sample t-test.

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Downregulation of miR-221 is negatively correlated to mRNA over expression of c-kit in primary carcinoma

To assess the regulation of c-kit in primary PCa by miR-221 we analyzed the expression of c-kit and p27Kip1 in groups of PCa samples, characterized by high or low miR-221 expression. We could not find any correlation between mir-221 expression and the level of mRNA expression of p27Kip1 (data not shown). However, tumor samples, that were characterized by low miR-221 expression, had a noticeably high expression of c-kit mRNA levels (Fig. 3c). Comparing the values of c-kit mRNA expression and miR-221 expression an inverse Spearman Rank correlation of ρ = −0.61 was evident.

Downregulation of miR-221 is associated with tumor progression and recurrence

On the basis of the result that miR-221 was progressively downregulated in metastasis, we postulated that dysregulation of miR-221 might also be associated with clinicopathological features or prognosis of PCa in patients. To test this hypothesis, we used qRT-PCR to analyze miR-221 expression in a large study cohort consisting of 92 patients. The clinical demographics of the study cohort are summarized in Table 1. We analyzed the expression in each carcinoma sample and compared them to the median expression in 9 BPH samples. With the ΔΔ Ct method, we determined that miR-221 expression in 90 of 92 (98%) carcinomas was more than 2-fold lower than the median expression in BPH (Table 1). Next we assessed whether the expression of miR-221 was related to the Gleason score (Gleason score ≤7 vs. ≥8), the pathologic stage (pT2 vs. pT3 vs. pT4), or the clinical recurrence of the carcinoma. We found that miR-221 was downregulated in samples from patients that had high Gleason scores, advanced stage tumors and clinical recurrences (Fig. 4a).

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Figure 4. (a) Box- and wisker-plots show the expression of miR-221 in mild and aggressive prostate carcinomas. Relative miR-221 expression was analyzed in 92 prostate carcinoma samples by the ΔCt method using qRT-PCR. Subsequently, the samples were divided into subgroups of tumor severity based on the Gleason score (left plot), the pathological tumor stage (middle plot), or clinical progression (right plot). Specifics of the different subgroups are summarized in Table 1. Significant reductions in the median expression levels (black bars) between subgroups are marked by * (p < 0.01). p values were calculated with the Welch 2 sample t-test; (b) Kaplan–Meier analysis of patients with prostate cancer. Patients were dichotomized by miR-221 expression (miR-221 high = ΔCt ≥ 0.77; miR-221 low = ΔCt < 0.77) or by Gleason score (GS ≤ 7 or GS > 7). The survival curves were generated using the Bioconductor package for survival. Low miR-221 expression or high Gleason scores were significantly associated with earlier clinical recurrence of the tumor (log-rank p < 0.01).

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miR-221 as risk factor in PCa

To determine whether miR-221 could serve as a prognostic indicator for clinical recurrence, we divided the sample population into groups with either low or high miR-221 expression levels, with an intermedian cut-off of ΔCt = 0.77 (i.e., the 39th and 71st percentiles in patients without and with recurrences, respectively). Kaplan–Meier estimates predicted a significant difference between groups in rates of recurrence- free survival. The 10 year survival rate was 87.2% in patients with high miR-221 expression and 53.5% in those with low miR-221 expression. Cox regression showed that dichotomized and continuous miR-221 expression levels, PSA levels, dichotomized pathologic stage and the biomodal Gleason score were univariately significant for the prediction of clinical recurrence; in contrast, the age of the patient at the time point of radical prostatectomy was not a significant factor (Table 2). Kaplan–Meier estimates of cancer-free time and recurrence-free survival confirmed that a Gleason score ≤7 and high miR-221 levels were associated with a good prognosis for the patient (Fig. 4b). When these variables were considered together in a multivariate proportional hazards model, the miR-221 expression level (p = 0.032), the Gleason score (p = 0.05), and the pathological stage (p = 0.01) were still significant for prediction of clinical recurrence. Thus, miR-221 downregulation was significantly associated with tumor progression and clinical recurrence.

Table 2. Cox proportional hazards regression for time to clinical recurrence
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Discussion

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

An ever growing number of articles have been published within the past few years that describe a link between the expression of miR and human cancer, including PCa. 22 However, despite the large body of work that has been published to date, only limited information is available regarding the expression levels of specific miRs in relation to the aggressiveness or the potential of miR expression as a prognostic marker in PCa.14

Therefore, we initially analyzed miR expression patterns in 4 pairs of primary tumors and the corresponding lymph node metastases relative to non cancerous prostate tissue. Our microarray comparison of global miR expression detected a PCa-specific miR signature represented by a set of miRs. This finding is consistent with previous studies on miR expression in PCa. One or more studies have shown that most of the highly significant dysregulated miRs (e.g., miR-145, miR -205, miR-16, miR-29a and let-7c) were also found in PCa miR profiles. 23–26 In this study, the hierarchical cluster analysis of micro array data showed that metastases tended to cluster with their corresponding primary tumor rather than with each other (and vice versa). This result is consistent with a clonal development of metastasis from the primary tumor.27 It shows that miR-expression is not substantially altered in metastasising PCa. In a greater number of samples (12 pairs of metastasis and primary carcinoma), we clearly confirmed that 3 tumorigenic miRs (miR- 16, -125b and -29a) were expressed at comparable levels in the primary tumor and the corresponding metastasis.

Despite the very similar expression signature in metastasis and the primary tumor, we found a metastasis-specific miR profile characterized by 14 differentially expressed miRs (8 up-, 6 downregulated). In a cluster analysis, these metastasis-specific miRs could successfully separate all 4 metastases from the primary carcinoma. Thus, these miRs comprise a novel miR expression profile for PCa metastases. Recently, it has been shown that miR s promoted tumor invasion and metastasis in breast cancer; this suggested that the dysregulation of specific miRs provided a selective advantage in breast cancer metastasis 11–13 Notably, we found no overlap between the metastasis-specific miRs of PCa and those found in breast cancer. These findings suggest that the dysregulation of miRs in metastasis depends on the cellular microenvironment.

Currently, little is known about these PCa metastasis-specific miRs except mir-221. Interestingly, only miR-221 was downregulated in comparisons between the primary tumor vs. metastasis, BPH and the primary tumor, and BPH vs. metastasis. We confirmed a progressive downregulation of miR-221 in metastasis by direct comparison of miR-221 expression in 12 independent pairs of tumor and metastatic tissues. It has previously been shown that miR-221 and miR-222 were over-expressed in tumors of the colon, breast, pancreas and in glioblastoma, papillary thyroid carcinoma and chronic lymphocytic leukaemia. 28–32 Furthermore, the upregulation of miR-221 in chronic lymphocytic leukemia was found to be associated with a poor prognosis.33 In contrast to the upregulation of miR-221 described for different types of cancer, we showed that 98% of the analyzed PCas were characterized by a downregulation of miR-221. Strong downregulation of miR-221 in PCa was clearly confirmed by 4 other miRNA expression studies of PCa.23–25 Therefore, it might be surprising that the miR-221 cluster has been found to regulate the p27Kip1 tumor suppressor in different types of tumor cell lines, including prostate cells.34–36 In recent studies it has further been shown that downregulation of p27Kip1 caused by over-expression of miR-221 is critically involved in the maintenance of androgen independency of PCa cell lines and that inhibition of miR-221 expression impairs the growth of xenografts in a murin tumor model. In contrast, the finding that miR-221 downregulation is a very common event in PCa implies that miR-221 mediated inhibition of p27Kip1 might play no role in the development or progression of primary PCa. This suggestion is supported by our observation that miR-221 expression is not correlated to the mRNA levels of p27Kip1 in primary PCa. The opposite regulation of miR-221 in different cancer types seems also paradoxical, but it might consistent with the observation that miR-mediated regulation of mRNA target molecules may be determined by the specific cellular microenvironment.26 It is also widely accepted that there is not a “one to one” relationship between miR s and target mRNAs and that the relationship strongly depends on an orchestral, dynamic regulation.37 Therefore we suggest that the discrepancy between miR-221 down regulation in primary PCa samples and miR-221 mediated downregulation of p27Kip1 in PCa cell lines might be depend on molecular changes, which occur while a primary tumor adapts to cell culture conditions. Currently we do not precisely know how miRs are regulated or processed in tumors and how many miR-221 targets exist, that might be involved in development of primary PCa. To understand the function of miR-221 and to solve the observed discrepancies in the role of miR-221 for the development of PCa it seems necessary to analyze molecular changes of components in the miRNA processing machinery and of additional potential targets of miR-221 in PCa samples and cancer cell lines. It has been shown that miR-221 downregulation is involved in the growth of erythroleukemic cells via inhibition of the c-kit oncogene.38 In our study we could show that downregulation of miR-221correlates with upregulation of c-kit on mRNA level. However, c-kit is not or only weekly expressed in prostate epithelial cells. Yet a study on c-kit expression in bone metastasis underlined the potential relevance of c-kit as metastasis specific oncogene in PCa.39 But further research is necessary to determine whether miR-221 mediated regulation of c-Kit plays a critical role in carcinogenesis or malignant progression.

Our results show that progressive reduction of miR-221 expression is a prostate specific and very frequent event in metastasis. In recent years, studies have revealed that the signalling steps in the invasion-metastasis cascade are linked to the dysregulation of genes or gene products. 40 Detection of these gene aberrations in the primary tumor is often associated with a poor prognosis for the patient. Thus, we postulated that a reduction in miR-221 expression might also be associated with clinico-pathological parameters in the primary PCa. To test this hypothesis, we analyzed miR-221 expression in a large PCa study group. Most of the studies on radical prostatectomy included patients that presented with localized, intermediate, and low risk PCa following the radical prostatectomy.41, 42 It is difficult to identify biologically relevant parameters that are significantly associated with tumor aggressiveness and progression in these patient cohorts, because these series represent only limited numbers of local tumor recurrence and metastatic disease. To acquire a statistically satisfying number of high grade primary tumors, we used radical prostatectomy specimens from a clinically well-defined, high risk group of patients with PCa.15 A high proportion of the patients had locally advanced, high grade disease and lymph node metastases. In these tissue specimens, we detected a progressive downregulation of miR-221 that was associated with high risk carcinoma and a poor prognosis, based on the Gleason Score, the tumor stage, or the rate of clinical recurrence. In these tissue specimens, we detected a progressive downregulation of miR-221 that was associated with poor prognosis, based on the Gleason Score, the tumor stage, or the rate of clinical recurrence. The sample population was then divided into groups with high or low miR-221 expression. Kaplan–Meier estimates predicted a significantly lower survival rate in patients that had samples with low miR-221 expression compared with those with high miR-221 expression. The Cox proportional hazard analysis for time to clinical recurrence demonstrated the predictive power (i.e., large hazard ratio) of miR-221 expression as a continuous and dichotomized variable. In fact, in uni- and multivariate analysis of the high risk study cohort used, high miR-221 expression as a continuous variable had higher predictive power than the Gleason Score, which is known as one of the strongest conventional predictors of tumor recurrence.6 Because patients with high risk PCa had a 37% risk of clinical recurrence at 15 years after radical prostatectomy (unpublished data), the detection of a powerful prognosticator in high risk carcinoma patients might be helpful for the decision of additional therapy strategies in these patients. Our findings demonstrate a high predictive power and independence of miR-221 as a prognostic indicator in high risk PCa. The role of mir-221 as prognostic indicator in a more consecutive series of PCa patients after radical prostatectomy has to be analyzed in the future using an enlarged study cohort.

To our knowledge, our study is the first to date that identified a single miR that was associated with clinical outcomes and metastasis of PCa. Informative biomarkers are urgently needed to guide clinical interventions for PCa. Our study reports the progressive downregulation of miR-221 in primary PCa and metastasis and provides strong evidence that miR-221 can be used as a novel prognostic indicator in high risk PCa. Further research is needed to analyze the underlying mechanisms of miR-221 mediated tumorigenesis and further support the utility of miR-221 as a potential diagnostic marker and therapeutic target for high risk PCa and metastasis.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The authors thank Ms. S. Müller, Ms. B. Dexler and Ms K. Borschert for excellent technical assistance.

References

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
  • 1
    Ferlay J, Autier P, Boniol M, Heanue M, Colombet M, Boyle P. Estimates of the cancer incidence and mortality in Europe in 2006. Ann Oncol 2007; 18: 58192.
  • 2
    Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM. Delineation of prognostic biomarkers in prostate cancer. Nature 2001; 412: 8226.
  • 3
    Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002; 1: 2039.
  • 4
    Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP, Rubin MA, Chinnaiyan AM. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 2002; 419: 6249.
  • 5
    D'Amico AV, Whittington R, Malkowicz SB, Fondurulia J, Chen MH, Kaplan I, Beard CJ, Tomaszewski JE, Renshaw AA, Wein A, Coleman CN. Pretreatment nomogram for prostate-specific antigen recurrence after radical prostatectomy or external-beam radiation therapy for clinically localized prostate cancer. J Clin Oncol 1999; 17: 16872.
  • 6
    Berney DM, Fisher G, Kattan MW, Oliver RT, Moller H, Fearn P, Eastham J, Scardino P, Cuzick J, Reuter VE, Foster CS. Major shifts in the treatment and prognosis of prostate cancer due to changes in pathological diagnosis and grading. BJU Int 2007; 100: 12404.
  • 7
    Rodriguez-Covarrubias F, Larre S, De La Taille A, Abbou CC, Salomon L. The outcome of patients with pathological Gleason score > or = 8 prostate cancer after radical prostatectomy. BJU Int 2008; 101: 3057.
  • 8
    Du T, Zamore PD. microPrimer: the biogenesis and function of microRNA. Development 2005; 132: 464552.
  • 9
    Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer 2006; 6: 85766.
  • 10
    Lowery AJ, Miller N, McNeill RE, Kerin MJ. MicroRNAs as prognostic indicators and therapeutic targets: potential effect on breast cancer management. Clin Cancer Res 2008; 14: 3605.
  • 11
    Huang Q, Gumireddy K, Schrier M, le Sage C, Nagel R, Nair S, Egan DA, Li A, Huang G, Klein-Szanto AJ, Gimotty PA, Katsaros D, et al. The microRNAs miR-373 and miR-520c promote tumor invasion and metastasis. Nat Cell Biol 2008; 10: 20210.
  • 12
    Ma L, Teruya-Feldstein J, Weinberg RA. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007; 449: 6828.
  • 13
    Tavazoie SF, Alarcon C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massague J. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008; 451: 14752.
  • 14
    Shi XB, Tepper CG, Devere White RW. MicroRNAs and prostate cancer. J Cell Mol Med 2008; 12: 145665.
  • 15
    Spahn M, Bader P, Woehr M, Frohneberg M. PCA with PSA > 20—is there a chance of cure? Eur Urol 2006; 5: 2123.
  • 16
    Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 2008; 3: 11018.
  • 17
    Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002; 18 ( Suppl 1): S96104.
  • 18
    Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005; 21: 206775.
  • 19
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5: 116.
  • 20
    Fellenberg K, Hauser NC, Brors B, Neutzner A, Hoheisel JD, Vingron M. Correspondence analysis applied to microarray data. Proc Natl Acad Sci USA 2001; 98: 107816.
  • 21
    Tibshirani RJ, Efron B. Pre-validation and inference in microarrays. Stat Appl Genet Mol Biol 2002; 1: 118.
  • 22
    Croce CM. Oncogenes and cancer. N Engl J Med 2008; 358: 50211.
  • 23
    Ambs S, Prueitt RL, Yi M, Hudson RS, Howe TM, Petrocca F, Wallace TA, Liu CG, Volinia S, Calin GA, Yfantis HG, Stephens RM, et al. Genomic profiling of microRNA and messenger RNA reveals deregulated microRNA expression in prostate cancer. Cancer Res 2008; 68: 616270.
  • 24
    Porkka KP, Pfeiffer MJ, Waltering KK, Vessella RL, Tammela TL, Visakorpi T. MicroRNA expression profiling in prostate cancer. Cancer Res 2007; 67: 61305.
  • 25
    Ozen M, Creighton CJ, Ozdemir M, Ittmann M. Widespread deregulation of microRNA expression in human prostate cancer. Oncogene 2008; 27: 178893.
  • 26
    Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006; 103: 225761.
  • 27
    Fidler IJ. The pathogenesis of cancer metastasis: the “seed and soil” hypothesis revisited. Nat Rev Cancer 2003; 3: 4538.
  • 28
    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.
  • 29
    Lee EJ, Gusev Y, Jiang J, Nuovo GJ, Lerner MR, Frankel WL, Morgan DL, Postier RG, Brackett DJ, Schmittgen TD. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 2007; 120: 104654.
  • 30
    Takamizawa J, Konishi H, Yanagisawa K, Tomida S, Osada H, Endoh H, Harano T, Yatabe Y, Nagino M, Nimura Y, Mitsudomi T, Takahashi T. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival. Cancer Res 2004; 64: 37536.
  • 31
    Ciafre SA, Galardi S, Mangiola A, Ferracin M, Liu CG, Sabatino G, Negrini M, Maira G, Croce CM, Farace MG. Extensive modulation of a set of microRNAs in primary glioblastoma. Biochem Biophys Res Commun 2005; 334: 13518.
  • 32
    He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin GA, Liu CG, Franssila K, Suster S, Kloos RT, Croce CM, et al. The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci USA 2005; 102: 1907580.
  • 33
    Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R, Sever NI, Fabbri M, Iuliano R, Palumbo T, et al. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 2005; 353: 1793801.
  • 34
    Galardi S, Mercatelli N, Giorda E, Massalini S, Frajese GV, Ciafre SA, Farace MG. miR-221 and miR-222 expression affects the proliferation potential of human prostate carcinoma cell lines by targeting p27Kip1. J Biol Chem 2007; 282: 2371624.
  • 35
    le Sage C, Nagel R, Egan DA, Schrier M, Mesman E, Mangiola A, Anile C, Maira G, Mercatelli N, Ciafre SA, Farace MG, Agami R. Regulation of the p27(Kip1) tumor suppressor by miR-221 and miR-222 promotes cancer cell proliferation. EMBO J 2007; 26: 3699708.
  • 36
    Visone R, Russo L, Pallante P, De Martino I, Ferraro A, Leone V, Borbone E, Petrocca F, Alder H, Croce CM, Fusco A. MicroRNAs (miR)-221 and miR-222, both overexpressed in human thyroid papillary carcinomas, regulate p27Kip1 protein levels and cell cycle. Endocr Relat Cancer 2007; 14: 7918.
  • 37
    Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature 2008; 455: 6471.
  • 38
    Felli N, Fontana L, Pelosi E, Botta R, Bonci D, Facchiano F, Liuzzi F, Lulli V, Morsilli O, Santoro S, Valtieri M, Calin GA, et al. MicroRNAs 221 and 222 inhibit normal erythropoiesis and erythroleukemic cell growth via kit receptor down-modulation. Proc Natl Acad Sci USA 2005; 102: 180816.
  • 39
    Wiesner C, Nabha SM, Dos Santos EB, Yamamoto H, Meng H, Melchior SW, Bittinger F, Thuroff JW, Vessella RL, Cher ML, Bonfil RD. C-kit and its ligand stem cell factor: potential contribution to prostate cancer bone metastasis. Neoplasia 2008; 10: 9961003.
  • 40
    Gupta GP, Massague J. Cancer metastasis: building a framework. Cell 2006; 127: 67995.
  • 41
    Chun FK, Graefen M, Zacharias M, Haese A, Steuber T, Schlomm T, Walz J, Karakiewicz PI, Huland H. Anatomic radical retropubic prostatectomy-long-term recurrence-free survival rates for localized prostate cancer. World J Urol 2006; 24: 27380.
  • 42
    Hernandez DJ, Nielsen ME, Han M, Trock BJ, Partin AW, Walsh PC, Epstein JI. Natural history of pathologically organ-confined (pT2). Gleason score 6 or less, prostate cancer after radical prostatectomy. Urology 2008; 72: 1726.

Supporting Information

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

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

FilenameFormatSizeDescription
IJC_24715_sm_suppinfofig1.tif1196KSupporting Figure 1
IJC_24715_sm_suppinfofig2.tif1125KSupporting Figure 2
IJC_24715_sm_suppinfotable1.tif336KSupporting Table 1

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