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13© М. Гонзалго, Г. Ишханова, 2014 УДК 616.62-006.6-07:616.63-076.5
МИКРОРНК КАК БИОМАРКЕР ДЛЯ ДИАГНОСТИКИ РАКА МОЧЕВОГО ПУЗЫРЯ
М. Гонзалго1, профессор, Г. Ишханова2, кандидат биологических наук
Станфорд университет, Медицинский центр, 875 Блаке Вильбур Драйв, США, 94305, Калифорния, Станфорд; 2Исследовательский центр Эймс, НАСА, Моффетт Филд, США, 94035, Калифорния
E-mail: gishkhanova@mail.ru
Введение. Карцинома мочевого пузыря — наиболее часто встречающаяся опухоль мочеполовой системы. Традиционно диагностика мочевого пузыря основана на его цистоскопии и цитологических исследованиях мочи на атипические клетки. Цитологические исследования мочи специфичны для рака мочевого пузыря, однако чувствительность результатов невысока, особенно для начальных стадий развития болезни. Поэтому необходимо найти новые диагностические маркеры, чтобы избежать болезнетворных процедур цистоскопии и биопсии. Маркеры, используемые в настоящее время для диагностики мочевого пузыря, обладают низкой чувствительностью и специфичностью. Авторы оценивали возможности неинвазивного диагностического метода при помощи биомаркера микроРНК. Очевидная функциональная характеристика всех микроРНК — это «тонкая» регуляция процессов. МикроРНК участвуют в образовании и развитии рака, а также действуют как супрессоры или онкогены.
Цель исследования. Изучение профилей экспрессии микроРНК в моче больных с опухолью и в неизмененной моче.
Материал и методы. Уровень экспрессии измеряли количественным геноспецифичным методом полимеразной цепной реакции (ПЦР) в реальном времени.
Результаты. Идентифицировано несколько аберрантно экспрессируюших микроРНК при изучении мочи больных и здоровых обследованных.
Заключение. Установлено, что miR-126, mirR-96, miR-196a, miR-183 и miR-200c могут служить биомаркерами при диагностике рака мочевого пузыря. Несмотря на то, что многие исследования убедительно доказали возможность применения профиля экспрессии микроРНК для идентификации и классификации малодифференцированных опухолей, многое еще предстоит сделать для применения этой методики в клинической практике.
Ключевые слова: рак мочевого пузыря, микроРНК, экспрессия генов
MICRORNA AS A BIOMARKER FOR DETECTION BLADDER CANCER M. Gonzalgo1, G. Ishkhanova2
1Stanford University School of Medicine, 875Blake Wilbur Drive, USA, 94305, California, Stanford;
2NASA Ames Research Center, Moffett Field, USA, 94035, California
Introduction. Transitional cell carcinoma of the bladder is the second most common malignancy of the genitourinary tract. Cystoscopy and urine cytology are the traditional most used techniques for diagnosis and surveillance of superficial bladder cancer. Urine cytology is specific for diagnosis of bladder cancer but sensitivity results not high, particularly in low-grade disease. A new diagnostic marker for urothelial carcinoma is needed to avoid painful cystoscopy during the initial diagnosis and follow-up period. However, the current urine markers are useless because of the low sensitivities and specificities for bladder cancer detection. Voided urine can be easily obtained and therefore additional diagnostic urine test would be ideal for screening or follow-up of transitional cell carcinoma.
The aim of the study. Our study focused on the evaluation of urinary microRNA markers that hold promise as non-invasive adjuncts to conventional diagnostic.
Methods. MicroRNA (miRNA) are involved in cancer development and progression, acting as tumor suppressors or oncogenes.
Results. We profiled the expression of unique human miRNAs in normal and bladder tumor samples. Expression levels were measured by gene-specific RT2 qPCR Primer Assays optimized for simultaneous use in the PCR Array System.
Conclusion. We identified several differentially expressed miRNAs between normal and cancer urine samples. We speculate that miR-126, mir-R-96, miR-196a, miR-183 and miR-200c can be used as biomarkers for bladder cancer, using urine as non-invasive diagnostic tools. Our results in some extent coincide with data obtained by other researchers. The findings reported here indicate that these miRNAs are differentially regulated in bladder cancer and may form a basis for clinical delivery new biomarkers for bladder cancer.
Key words: bladder cancer, microRNA, gene expression
INTRODUCTION
Molecular medicine is a broad field, where physical, chemical, biological and medical techniques are used to describe molecular structures and mechanisms,
identify fundamental molecular and genetic errors of diseases, and to develop molecular interventions to correct them. Personalization of molecular medicine customizes healthcare with medical decision-making
practices and/or products tailored to the individual patient. The use of genetic information has played a major role in certain aspects of personalized medicine, and the term was first coined in the context of genetics. One of the major goals of personalized medicine is to assess disease risk based on the genetic make-up of an individual. Some tests can determine the individual genetic variation and one of them- the expression profile of microRNA, can be used as a biomarker. Aside from candidate genes, some recent studies suggest that polymorphisms in miRNA genes may serve as novel risk predictors for cancer. Small, noncoding miRNAs are able to induce heritable changes in gene expression without altering DNA sequence and thus contribute to the epigenetic landscape. The early detection, diagnosis, and treatment of cancer are necessary before surgical operations, chemo-, or radio-therapies. Minimally invasive methods and accurate markers of tumors could improve early diagnosis and reduce treatment costs and the mortality rate of cancers [1]. Early detection of tumors is essential for improved prognosis and long-term survival. We used microRNA techniques for bladder cancer detection.
Bladder cancer is the fifth most common and the most expensive cancer to treat per patient because of its potential for frequent recurrence requiring intensive monitoring. Clinical and molecular evidence suggests there are at least two distinct varieties of bladder cancer. Most UCs belong to a low-grade pathway characterized by FGFR3 mutation, chromosome 9 loss, and an indolent clinical phenotype. Around 1/3 of UCs are high-grade in differentiation and arise as lesions initially confined to the bladder mucosa (non-muscle invasive). Progression to muscle invasion occurs in around 50% of high-grade lesions and is associated with an ominous prognosis despite radical treatment [2, 3]. Almost 50% of patients with muscle-invasive bladder cancer already have occult distant metastases. Despite major advances in understanding the key molecular lesions in cellular control pathways that contribute to cancer, microscopic examination of nuclear structure by a pathologist remains the gold standard in cancer diagnosis. However, conventional histopathologic evaluation, encompassing tumor grade and stage, is inadequate to accurately predict the behavior of most bladder cancers. The need to establish which non-muscle-invasive cancers will recur or progress and which invasive cancers will metastasize has led to the identification of a variety of potential prognostic markers for bladder cancer patients. Prediction of disease recurrence and progression for patients with non-muscle-invasive bladder cancer is a major clinical challenge. However, bladder cancer is progressively being regarded as a disease that cannot be treated solely on the basis of clinical and pathological parameters. Understanding the epigenetic events leading to urothelial tumorigenesis and progression is increasing, and this will allow clinicians to identify key epigenetic molecules that can be targeted for detection, prediction, and therapy.
CONVENTIONAL METHODS
FOR DIAGNOSTIC BLADDER CANCER
Fluorescent in situ hybridization (FISH) and cytology are currently used to non-invasively monitor the recurrence of bladder cancer. FISH is more sensitive, but less specific than cytology. In contrast, urine testing can be both highly sensitive and specific. Moreover, compared with FISH and Cytology, urine testing can be much more cost-effective.
The standard method for detection and surveillance is cystoscopy together with urine cytology. Cystoscopy is relatively sensitive but is expensive and invasive. Urinary cytology is a noninvasive method that has poor sensitivity but high specificity; it is relied on for the detection of carcinoma in situ. No currently available bladder cancer urinary marker is sensitive enough to eliminate the need for cystoscopy. Cytology and cystoscopy have been used as detection tests for patients suspected to have bladder cancer or for surveillance of patients at risk for tumor recurrence.
Cystoscopy is highly sensitive for most tumors but has some practical limitations. It may fail to identify smaller, flat tumors such as carcinoma in situ. Also, despite the technical advances in cystoscopes, the procedure is often perceived as invasive and a source of patient anxiety. There is also a significant financial cost related to frequent cystoscopic monitoring, in terms of health care resources and patient time. Conversely, urinary cytology is noninvasive and highly specific but has poor sensitivity for low-grade, well-differentiated lesions. Thus it cannot be used to replace (or prolong the intervals between) cystocopy and is used, rather, as an adjunct to help detect occult tumors. Because cystoscopies are invasive and because cytology has poor sensitivity, noninvasive biomarkers have been sought as alternatives to cystoscopy and cytology for the detection and surveillance of bladder cancer.
MICRORNA AS A BIOMARKER FOR BLADDER CANCER DIAGNOSIS
An ideal test for the detection of bladder tumors should be objective, accurate, rapid and easy to administer; moreover, it should offer high sensitivity and specificity. Whereas sensitivity is defined as the ability of a test to detect disease, specificity is defined as the ability to rule out disease [4—7]. Among the many existing biomarkers for urothelial carcinomas, none qualifies as a universally applicable marker for the definitive prediction of prognosis in day-to-day clinical practice. There is a need, therefore, to identify new molecular markers that can more faithfully predict urothelial tumor progression. An ideal biomarker should be measurable in a reproducible way, have high sensitivity and specificity for the clinical outcome ofinterest, and should reflect an important pathogenetic process. MicroRNAs (miRs or miRNAs) are exciting as potential biomarkers because they fulfill many of these criteria. Researchers hypothesized that alterations in microRNA expression occurs in cancer cells and contributes to the process of carcinogenesis. MiRNAs represent a relatively
new discipline in biomedical research and can be explored for use in developing approaches to personalized medicine, because many cellular and physiological processes in health and disease are associated with changes in miRNA expression. In addition, miRNAs can both regulate and be regulated by other epigenetic mechanisms (Fig. 1). Expression of miRNAs is dysregulated in cancer [8, 9—15], microRNAs may function as either oncogenes [16—20] or tumor suppressor genes [21—23]. MiRNA signatures may provide solutions to old problems [24—30]. Abnormal levels of miRNAs often result in loss of differentiation, a hallmark of cancer. Not surprisingly, therefore, dysfunction of miRNA pathways may affect many cellular processes such as differentiation, proliferation, apoptosis, metastasis and telomere maintenance [31—33]. Conceivably, microRNAs directly or indirectly affect most, if not all cellular pathways.
Although the microRNA era started only a few years ago, it has brought great promises for diagnosis, prognosis and therapy of cancer. Indeed, miRNA expression profiles seem to be more informative than traditional mRNA profiling for the classification of tumors with respect to their tissue of origin and differentiation. Thus, the profile of only 200 miRNAs was sufficient to classify poorly differentiated tumors in a recent study, with greater accuracy then a profile of 30 000 mRNAs [8]. In the past few years several studies demonstrated that miRNAs expression is predictive of the outcome in solid tumors and hematologic malignancies, highlighting their potential diagnostic utility in cancer [11, 34—39]. Currently, the main approach for studying the role of miRNAs in cancer is represented by
Normal = Proliferation, invasion, angiogenesis cell = Cell death, apoptosis
t I
Deregulated miRNA formation Oncogenic miRNA ^ Tumor suppressor miRNA
Deregulated protein formation
Tumor suppressor protein A Oncogenic protein
f
Cancer ^ Proliferation, invasion, angiogenesis cell \ Cell death, apoptosis
Figure 1. Deregulated microRNA biogenesis and tumorigenesis. Both reduced expression of microRNA acting as a tumor suppressor and increased expression of miRNA acting as an oncogene. MiRNA can alter the synthesis of either oncogenic or tumor suppressor proteins and lead to tumor formation. In addition altered miRNA function due to "qualitative" changes of miRNAs or of mRNA binding sites by mutations can similarly cause tumorigenesis [15]
the analysis of miRNAs expression profiling. Although the aforementioned examples clearly establish a role for miRNAs, it is likely that we are just beginning to elucidate the roles of these regulatory RNAs in the networks that control cancer pathogenesis.
The number of studies investigating microRNAs in bladder cancer remains limited. Some microRNA-profiling studies have been conducted to date are shown in Table.
Despite a number of studies investigating miRNA expression in bladder cancer, there are discrepancies in reported data. Possible reasons for these discrepancies may include:
a) Differences in selection of specimens (biopsy tissue, cells, serum, blood)
b) Use of different techniques for microRNA preparation (total RNA or purified microRNA with partial detection of premature microRNAs),
c) Differences in miRNA measurement platforms,
d) Methods of tissue preservation and preparation (for example, frozen versus formalin-fixed paraffin-embedded tissue, and bulk versus microdissected tissue). These differences may have contributed to the different expression profiles obtained in studies that examine the same tissue type.
e) Data analysis: researchers use different references genes for normalization, which play key role in data analysis.
To date, differences in analytical methodologies used in the published microRNA profiling studies have limited the comparability of data. Despite years of research and hundreds of reports on tumor biomarkers in oncology, the number of markers that have clinical utility remains small and there are limited data detailing microRNA in bladder cancer. Often, initially reported studies of a marker show great promise, but subsequent studies on the same or related markers yield inconsistent conclusions or stand in direct contradiction to initial reports. It is imperative that attempting to understand the reasons why multiple studies of the same marker frequently lead to different conclusions. A variety of problems have been cited to explain these discrepancies, such as general methodological differences, poor study design, assays that are not standardized or lack reproducibility, and inappropriate or misleading statistical analyses that are often based on sample sizes too small to draw meaningful conclusions.
MATHERIAL AND METHODS
Urine from bladder cancer patients and healthy donors was used for isolating microRNAs in order to obtain the microRNA profiles of gene expression by the qRT-PCR method.
Urine collection is a noninvasive procedure, requires no special facility of equipment apart from sterile collection containers, as compared to the requirements for serum or plasma collection. Thus, it is important to explore the potential of using urine in the area of noninvasive cancer screening and detection. The extraction of total RNA from urine was performed by the TRIzol method with further
purification, using special columns. The integrity of RNA was checked with the Nano Drop 2000c Spectrophotometer (ThermoScientific, DE) and 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA).
Oligonucleotide microarrays and reverse transcription quantitative real-time PCR (RT-qPCR) are the main methods used to detect and quantify microRNA expression. However, microarray results should always be validated by RT-qPCR. RT-qPCR allows the monitoring of the product during amplification by the incorporation of intercalating fluorescent dyes, making this method highly sensitive and specific. While microarray-based studies are highly sensitive to RNA degradation, RT-qPCR appears to be more robust and tolerates partial degradation of RNA.
Expression levels are measured by gene-specific RT2 qPCR Primer Assays optimized for simultaneous use in the PCR Array System. RT2 qPCR Primer Assays are key components in the PCR Array System. We exploit the single-strand RNA dependence of both the poly-(A) polymerase and the reverse transcriptase. Both enzymes act on highly structured RNA very inefficiently. We used a buffer system which favors formation of RNA secondary structure during the poly-(A) tailing and the reverse transcription process. In this buffer, very short miRNAs do not form higher order structures. As a result, buffer conditions achieve much lower background compared to traditional poly-(A) tailing and RT chemistry. The overall improvement of sensitivity can be as much as 1000-fold. The identity and relative quantity of microRNAs in a sample can be used to provide microRNA profiles [59].
Data analysis is based on the AACt method with normalization of the raw data to one or more of the housekeeping assays. This integrated web-based software package for the miRNA PCR Array System automatically performs all AACt based fold-change calculations from uploaded raw threshold cycle data. The cycle threshold (Ct) values calculated with SABiosciences Software. The software also provides statistical details such as Fold changes and P-values for differentially expressed microRNAsin each cancer types and corresponding normal (healthy) sample. Fold Change Analysis is used to identify genes with expression ratios or differences between a treatment and a control that are outside of a given cutoff or threshold. Fold change is calculated between a Condition 1 and one or more other conditions Condition 2 treated as an aggregate. The ratio
between Condition 2 and Condition 1 is calculated (Fold change = Condition 1/Condition 2). Fold change gives the absolute ratio of normalized intensities (no log scale) between the average intensities of the samples grouped. The entities satisfying the significance analysis are passed on for the fold change analysis [60].
RESULTS
Novel, highly sensitive, and specific urine-based diagnostic tools are particularly attractive for clinical use. Urine is a promising and easily available biological source for application of molecular markers, including RNA. On the technical level, urine samples can be obtained at high quantities and contain less proteins than blood-based samples, which reduces the interference of proteins during RNA preparation and subsequent analyses. On a conceptual level, it is noteworthy that investigation of urine RNA-based tumor markers is not only of particular interest for the detection of bladder cancer but also for diagnosis of other tumor types [53, 54].
We tested the microRNA profiles of urine, obtained from bladder cancer patients and healthy donors by qRT-PCR. Figure 3 displays the results from the Cancer Pathway PCR Array experiment, indicating the altered expression of microRNAs. This PCR Array includes representative genes from the following biological pathways involved in tumorigenesis: adhesion, angiogenesis, apoptosis, cell cycle control, cell senescence, DNA damage repair,
IMPORTANT DEREGULATED MICRORNAS IN BLADDER CANCER
Altered expression in cancer (down or up-regulated) Method and specimens Authors
miR-223, miR-26a-c, miR-221, miR-103-1, miR-185, miR-203, miR-17-5p, miR-23a-b, miR-205 Tissue biopsy, microarray Cottardo [40]
miR-7, miR-146a, miR-188, miR-452-4p, miR-10a Tissue biopsy, microarray Veerla [41]
miR-127 Tissue, cell line, microarray Saito [42]
miR-143 Tissue, microarray Lin [43]
miR-145, miR-133a-b, miR-195, miR-125b, miR-199a, miR-30a-3p Tissue, cell line,RT-PCR Ichmi [44]
miR-143, miR-145, miR-455, miR-126, miR-29, miR-128 Tissue, cell line, microarray Dyrskjot [45]
miR-126, miR-182, miR-199a Urine, RT-PCR Hanke [46]
miR-26a, miR-29a, miR-30c, miR-30c-5p Tissue biopsy, microarray Wang [56]
miR-135b, miR-183, miR-211, miR-133b Urine, RT-PCR Miah [47]
miR-200c, miR-141, miR-30b, miR-99a Tissue, cell line, microarray Wszolek [48]
miR-99a, miR-100 Tissue, cells, RT-PCR Catto [49]
MiR-96, miR-183 Urine, RT-PCR Yamada [50]
miR-125b Tissue, cells, RT-PCR Huang [51]
miR-221 Cell lines, Northern blot Lu [52]
invasion, metastasis, signal transduction molecules, and transcriptions factors. Data represent only large fold-differences in expression between samples from normal patients and bladder cancer patients. Fold Change Analysis is used to identify genes with expression ratios or differences between a cancer and a control that are outside of a given cutoff or threshold. Fold change gives the absolute ratio of normalized intensities (no log scale) between the average intensities of the samples grouped. It is already known that microRNAs are tissue and cancer specific (Fig. 2). We speculate that miR-126,
Figure 2. MicroRNAs are tissue and cancer specific. Profile of MicroRNAs from cancer and normal tissues. Appropriate microRNA levels are critical to maintain cellular function. Dysregulation of microRNA is often associated with human disease. MicroRNA expression signatures have the potential to serve as a biomarker
mir-R-96, miR-196a, miR-183 and miR-200c can be used as biomarkers for bladder cancer, using urine as a non-invasive diagnostic specimen (Fig. 3). Our results coincide with data obtained by other researchers [52, 53, 69]. The upregulation of miR-96-183 cluster may cause a decrease of Foxo1 /3a transcription factors, which in turn leads to the hyperactivation of B and T lymphocytes, immune tolerance breakdown, and development of disease [54, 55].
We believe that these studies will potentially open a new approach for bladder cancer diagnosis and potentially lead to novel strategies for treatment of this disease by altering bladder-specific microRNAs.We envision an increasing shift to integrated cancer research and biomarker-driven adaptive approaches and hypothesis testing clinical trials. A paramount goal is the development of specific cancer medicines to treat the individual patient, with treatment selection being driven by a detailed understanding of the genetics and biology of the patient and their cancer.
Our mission is to develop tests that provide patients and their physicians with accurate and timely information about the current cancer status. We do this by developing and commercializing non-invasive, accurate, genome-based diagnostic tests for use in early stages of disease. Analysis of urine specimens will provide valuable genetic information without risk to the patient, so further studies on larger cohorts of patients will be feasible and can be carried out to validate the utility of molecular marker analsyis in a clinical setting.
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Figure 3. Altered microRNA expression profile for bladder cancer compared with control (Fold change)
Urine could be a better source that serum or plasma for detecting altered microRNAs for diagnosis of bladder cancer and provide a broader range of indications for diagnosing and monitoring different states of bladder cancer. The current diagnostic "gold standard" is based on cystoscopy, which is invasive and relatively expensive. Measurement of microRNAs in urine samples has been shown to identify patients with bladder cancer compared to healthy individuals [46, 47, 50]. The relevance of microRNAs in bladder carcinogenesis was supported by the observation that microRNA-related single-nucleotide polymorphisms are associated with increased bladder cancer risk [56]. Accurate diagnosis of bladder cancer still requires a biopsy. The invasiveness of this procedure and risk for complications preclude routine usage of repetitive biopsies. Additionally, sampling errors and inter-observer variability may complicate accurate diagnosis [57, 58].
CONCLUSION
It is expected that the incorporation of miRNA into current biomarker panels may enhance the sensitivity and specificity of noninvasive diagnostic tests for cancer. Efforts have been put forth to predict disease outcome and response to treatment. The ideal biomarker must be accessible using noninvasive protocols, inexpensive to quantify, specific to the disease of interest, and provide a reliable early indication of disease before clinical symptoms appear.
Dysregulation of miRNA occurs in bladder cancer as well as other malignant diseases. The mechanisms by which miRNA takes part in tumor promotion and
progression are complex and numerous. However, most of them converge on common signaling mechanisms that govern cell proliferation, apoptosis and invasiveness. The relevance of some isolated cell studies to in vivo situation, however, should be assessed with caution for the possible use of nonphysiological levels of miRNA for transfection experiments. Recent advances in the development of in vivo RNA delivery system may open the window for use of miRNA as cancer therapeutics. In addition to restoring the expression of downregulated miRNAs, overexpressed miRNAs may be targeted by a novel class of chemically engineered oligonucleotides known as antagomirs that silence endogenous miRNAs. It is anticipated that, with a more comprehensive understanding of miRNA dysregulation and the associated abnormalities in cellular signaling in cancer, novel therapeutics will emerge.
Despite growing competition from new entrants, microRNA tool providers are witnessing extraordinary growth in their research product portfolios. As evidence of the link between microRNAs and disease grows, the diagnostic and therapeutic potential of these molecules will remain the driving forces behind market expansion. MicroRNA research offers the capability of moving from bench to bedside faster than most research fields, as diagnostic tests have already emerged from this young field.
Our preliminary data have demonstrated that this non-invasive method using microRNAs profiling from urine may have clinical utility as a potential biomarker for the diagnosis of bladder cancer.
REFERENCES
1. Mitchell P.S., Parkin R.K., Kroh E.M., Fritz B.R., Wyman S.K., Pogosova-Agadjanyan E.L. et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA. 2008; 105: 10513-8.
2. von der Maase H., Sengelov L., Roberts J.T. et al. Long-term survival results of a randomized trial comparing gemcitabine plus cisplatin, with methotrexate, vinblastine, doxorubicin, plus cisplatin in patients with bladder cancer. J Clin Oncol. 2005; 23: 4602-8.
3. Herr H.W., Dotan Z., Donat S.M., Bajorin D.F. Defining optimal therapy for muscle invasive bladder cancer. J.Urol. 2007; 177: 437-43.
4. Lorne I. Budman, Wassim Kassouf, Jordan R. Steinberg. Biomarkers for detection and surveillance of bladder cancer. CUAJ. 2008; 2 (3): 212-21.
5. Lotan Y., Roehrborn C.G. Sensitivity and specificity of commonly available bladder tumor markers versus cytology: results of a comprehensive literature review and metaanalyses. Urology. 2003; 61: 109-18.
6. van Rhijn B.W., van der Poel H.G., van der Kwast T.H. Urine markers for bladder cancer surveillance: a systematic review. Eur Urol. 2005; 47: 736-48.
7. Dey P. Urinary markers of bladder carcinoma. Clin Chim Acta. - 2004; 340: 57-65.
8. Lu J., GetzG., Miska E.A., Alvarez-Saave-dra E., Lamb J., Peck D., Sweet-Cordero A., Ebert B.L., Mak R.H., Ferrando A.A.,
Downing J.R., Jacks T., Horvitz H.R., Golub T.R. MicroRNA expression profiles classify human cancers. Nature. 2005; 435: 834-838.
9. Calin G.A., Dumitru C.D., Shimizu M. et al. Frequent deletion and down-regulation of micro-RNA genes miR-15 and miR-16 at13q14 in chronic lymphocytic leukemia. Proc. Natl. Acad. Sci USA. 2002; 99: 15524-9.
10. Volinia S., Calin G.A., Liu C.G. et al. A micro-RNA expression signature of human solid tumors defines cancer gene targets. Pros. Natl. Acad.Sci. USA. 2006; 103: 2257-61.
11. Calin G.A., Croce C.M., Micro-RNA signatures in human cancers. Nat.Rev. Cancer 2006; 6: 857-66.
12. lorio M.V., Ferracin M., Liu C.G. et al. Micro-RNA gene expression deregulation in human breast cancer. Cancer Res. 2005, 65; 7065-70.
13. Murakami Y., Yasuda T., Saigo K. at al. Comprehensive analysis of micro-RNA expression patterns in hepatocellular carcinoma and non-tumorous tissue. Oncogene. 2006; 25: 2537-45.
14. Yu S.L., Chen H.Y., Chang G.C. et al. Micro-RNA signature predict survival and relapse in lung cancer. Cancer Cell. 2008; 13: 48-57.
15. Schaefer A., Jung M., Kristiansen G., Lein M., Schrader M., Miller K., Stephan C., Jung K. Micro-RNAs and Cancer: Current state and future perspectives in urologic oncology. Urologic Oncology. 2009; 28 (1): 4-13.
16. He L. et al. A microRNA polycistron as a potential human oncogene. Nature. 2005; 435: 828-33.
17. O'Donnell K.A. et al. c-Myc-regulated microRNAs modulate E2F1 expression. Nature. 2005; 435: 839-43.
18. Costinean S. et al. Pre-B cell proliferation and lymphoblastic leukemia/highgrade lymphoma in E(mu)-miR155 transgenic mice. Proc Natl. Acad Sci USA. 2006; 103: 7024-9.
19. Voorhoeve P.M. et al. A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell. 2006; 124: 1169-81.
20. Chan J.A., Krichevsky A.M., Kosik K.S. MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res. 2005; 65: 6029-33.
21. Johnson S.M. et al. RAS is regulated by the let-7 microRNA family. Cell. 2005; 120: 635-47.
22. Mayr C., Hemann M.T., Bartel D.P. Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation. Science. 2007; 315: 1576-9.
23. He L. et al. A microRNA component of the p53 tumor suppressor network. Nature. 2007; 447: 1130-4.
24. Garber M.E., Troyanskaya O.G., Schluens K., Petersen S., Thaesler Z., Pacyna-Gengel-bach M., van de Rijn M., Rosen G.D., Perou C.M., Whyte R.I., Altman R.B., Brown P.O., Botstein D., Petersen I. Diversity of gene expression in adenocarcinoma of the lung.
Proc Natl Acad Sci USA. 2001; 98: 13784-9.
25. Shedden K., Taylor J.M., Enkemann S.A., Tsao M.S., Yeatman T.J., Gerald W.L., Eschrich S., Jurisica I., Giordano T.J., Misek D.E., Chang A.C., Zhu C.Q., Strumpf D., Hanash S., Shepherd F.A., Ding K., Seymour L., Naoki K., Pennell N., Weir B., Verhaak
R., Ladd-Acosta C., Golub T., Gruidl M., Sharma A., Szoke J., Zakowski M., Rusch V., Kris M., Viale A., Motoi N., Travis W., Conley B., Seshan V.E., Meyerson M., Kuick R., Dobbin K.K., Lively T., Jacobson J.W., Beer D.G. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008; 14: 822-7.
26. Yanaihara N., Caplen N., Bowman E., Seike M., Kumamoto K., Yi M., Stephens R.M., Okamoto A., Yokota J., Tanaka T., Calin G.A., Liu C.G., Croce C.M., Harris C.C. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006; 9: 189-98.
27. Raponi M., Dossey L., Jatkoe T., Wu X., Chen G., Fan H., Beer D.G. MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res. 2009; 69: 5776-6783.
28. Beer D.G., Kardia S.L., Huang C.C., Giordano T.J., Levin A.M., Misek D.E., Lin L., Chen G., Gharib T.G., Thomas D.G., Lizyness M.L., Kuick R., Hayasaka S., Taylor J.M., lannettoni M.D., Orringer M.B., Hanash S. Gene- expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002; 8: 816-24.
29. Patnaik S.K., Kannisto E., Knudsen S., Yen-damuri S. Evaluation of microRNA expression profiles that may predict recurrence of localized stage I non-small cell lung cancer after surgical resection. Cancer Res. 2010; 70: 36-45.
30. Hu Z., Chen X., Zhao Y., Tian T., Jin G., Shu Y., Chen Y., Xu L., Zen K., Zhang C., Shen H. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol. 2010; 28: 1721-6.
31. Tavazoie S.F., Alarcon C., Oskarsson T., Padua D., Wang Q., Bos P.D., Gerald W.L., Massague J. Endogenous human microRNAs that suppress breast cancer metastasis. Nature. 2008; 451: 147-52.
32. Benetti R., Gonzalo S., Jaco I., Munoz P., Gonzalez S., Schoeftner S., Murchison E., Andl T., Chen T., Klatt P., Li E., Serrano M., Millar S., Hannon G., Blasco M.A. A mammalian microRNA cluster controls DNA methylation and telomere recombination via Rbl2-dependent regulation of DNA methyltransferases. Nat Struct Mol Biol. 2008; 15: 268-79.
33. Sinkkonen L., Hugenschmidt T., Berninger P., Gaidatzis D., Mohn F., Artus-Revel C.G., Zavolan M., Svoboda P., Filipowicz W. MicroRNAs control de novo DNA methyla-tion through regulation of transcriptional repressors in mouse embryonic stem cells.
Nat Struct Mol Biol. 2008; 15: 259-67.
34. Lim L.P., Lau N.C., Garrett-Engele P. et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005; 433: 769-73.
35. Calin G.A., Sevignani C., Dumitru C.D. et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA. 2004; 101: 2999-3004.
36. Deng S., Calin G.A., Croce C.M. et al. Mechanism of microRNA deregulation in human cancer. Cell Cycle. 2008; 7: 2643-6.
37. Rosenfeld N., Aharonov R., Meiri E. et al. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol. 2008; 26: 462-9.
38. Baffa R., Fassan M., Volinia S. et al. MicroRNA expression profiling of human metastatic cancers identifies cancer gene targets. J Pathol. 2009; 219: 214-21.
39. Nass D., Rosenwald S., Meiri E. et al. MiR-92b and miR-9/9* are specifically expressed in brain primary tumors and can be used to differentiate primary from metastatic brain tumors. Brain Pathol. 2009; 19: 375-83.
40. Gottardo F., Lie C.G., Ferracin M., Calin G., Fassan M., Bassi P., Sevignani C., Byrne D., Negrini M., Pagano F., Gomella L., Croce C., Baffa R. Micro RNA profiling in kidney and bladder cancers. Urologic Oncology. 2007; 387-92.
41. Veerla S., Linddren D., Kvist A., Frigyesi A., Staat J., Persson H., Liedberg F., Chebil G., Gudjonsson S., Borg A., Mansson W., Rovira C., Hoglund M. MiRNA expression in urothelial carcinomas: Important roles of miR-10a, miR-222, miR-125b, miR-7 and miR-452 for tumor stage and metastasis, and frequent homozygous losses of miR-31. Int.L.Cancer. 2009; 124: 2236-42.
42. Saito Y. et al. Specific activation of mi-croRNA-127 with down-regulation of the proto-oncogene BCL6 by chromatin-mod-ifying drugs inhumancancer cells. Cancer Cell. 2006; 9: 435-43.
43. Lin T., Dong W., Huang J., Pan Q., Fan X., Zhang C., Huang L. MicroRNA-143 as a tumor suppressor for bladder cancer. The Journal of Urology. 2009; 181: 1372-80.
44. Ichimi T., Enokida H., Okuno Y., Kunimoto R., Chiyomaru T., Kawamoto K., Kawahara K., Toki K., Kawakami K., Nishiyama K., Tsujimoto G., Nakagawa M. and Seki N. Identification of novel microRNA targets based on microRNA signatures in bladder cancer. Int. J. Cancer. 2009; 125: 345-52.
45. Dyrskjot L., Ostenfeld M.S., Bramsen J.B., Silahtaroglu A.N., Lamy P., Ramanathan R., Fristrup N., Jensen J., Andersen C.L., Zieger K., Kauppinen S., Ulhoi B.P., KjemsJ., Borre M., and Orntoft T.F. Genomic Profiling of microRNAs in Bladder Cancer: miR-129 is Associated with Poor Outcome and Promotes Cell Death In vitro. Cancer Res. 2009; 69 (11): 4851-60.
46. Hanke M., Hoefig K., Merz H., Feller A., Kausch I., Jocham J., Warnecke M., Sczak-iel G. A robust methodology to study urine
microRNA as tumor marker: microRNA-126 and microRNA-182 are related to urinary bladder cancer. Urologic Oncology 2010; 28 (6): 655-61.
47. Miah et al. «The utilisation of urinary microRNA as a potential biomarker for bladder cancer», Abstract 816, Poster Session on Urothelial tumours: Basic research 1; 25th Anniversary EAU Congress, Barcelona, Spain, April 16-20, 2010.
48. Wszolek M.F., Rieger-Christ K.M., Kennedy P.A., Gould J.J., Neto B.S., LaVoie A.K., Logvinenko T., Libertino J.A., and Summer-hayes I.C. (2009). A MicroRNA expression profile defining the invasive bladder tumor phenotype. Urologic Oncology (in press).
49. Catto J.W., Miah S., Owen H.C. et al. Distinct microRNA alterations characterize high- and low- grade bladder cancer. Cancer Research. 2009; 69: 8472-81.
50. Yamada Y., Enokida H., Kojima S., Kawakami K., Chiyomaru T., Tatarano S. et al. MiR-96 and miR-183 detection in urine serve as potential tumor markers of urothelial carcinoma: correlation with stage and grade, and comparison with urinary cytology. Cancer Science. 2011; 102: 522-9.
51. Huang U., Luo J., Cai Q. et al. MicroRNA-125b suppresses the development of bladder cancer by targeting E2F3. Int. J. Cancer. 2011; 128: 1758-69.
52. Lu Q., Lu C. et al. MicroRNA-221 silencing predisposed human bladder cancer cells to undergo apoptosis induced by TRAIL. Urologic oncology. 2010; 26: 635-41.
53. Groskopf J., Aubin S.M., Deras I.L. et al. AP-TIMA PCA3 molecular cancer. Clin. Chem. 2006; 52: 1089-95.
54. Bai V.U., Kaseb A., Tejwani S. et al. Identification of prostate cancer mRNA markers by averaged differential expression and their deyection in biopsies,blood and urine. Proc.Natl.Acad. Sci. 2007; 104: 2343-8.
55. Guttilla I.K., White B.A. Coordinate regulation ofFOXO1 by miR-27a, miR-96, and miR-182 in breast cancer cells. J Biol Chem. 2009; 284: 23204-16.
56. Yang H. et al. Evaluation of genetic variation in microRNA related genes and risk of bladder. Cancer Research. 2008; 68 (7): 2530-7.
57. Seron D., Moreso F. Protocol biopsies in renal transplantation: prognostic value of structural monitoring. Kidney Int . 2007; 72: 690-7.
58. Wilkinson A. Protocol transplant biopsies: are they really needed? Clin J Am Soc Nephrol. 2006; 1: 130-7.
59. Arikawa E., Quellhorst G., Han Y., Pan H., Yang J. RT Profiler PCR Aray: Pathway-Focused gene expression profiling with qRT-PCR. Technical Article, SABioscieces, Inc. 2006.
60. Livak K.J. and Schmittgen T.D. Analysis of relative gene expression data using realtime quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001; 25: 402-8.
