Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 10  |  Issue : 7  |  Page : 167-177

Identification of potential biomarkers for clear cell renal cell carcinoma based on microRNA-mRNA pathway relationships


Department of Blood Purification, General Hospital of Shenyang Military Area Command, Shenyang 110000, Liaoning, China

Date of Web Publication29-Nov-2014

Correspondence Address:
Ning Cao
Department of Blood Purification, General Hospital of Shenyang Military Area Command, 83 Wenhua Road, Shenyang 110000, Liaoning
China
Xu Li
Department of Blood Purification, General Hospital of Shenyang Military Area Command, 83 Wenhua Road, Shenyang 110000, Liaoning
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.145856

Rights and Permissions
 > Abstract 

Background: MicroRNAs (miRNAs) play important roles in tumor genesis. miRNA dysregulation has been widely studied and demonstrated in clear cell renal cell carcinoma (ccRCC).
Materials and Methods: We applied a newly proposed method for selecting miRNAs that discriminate between healthy controls and cancers. We initially extracted different miRNAs and mRNAs and then selected miRNA-mRNA dysregulation pairs. The pathways that involved mRNAs were acquired according to the functional enrichment. We integrated the miRNAs, mRNAs, and pathways and constructed the miRNA-mRNA pathway relationships based on the derived significant miRNAs.
Results: We acquired 566 antiregulated miRNA-mRNA pairs including 56 miRNAs and 485 mRNAs. Three significant pathways related to ccRCC, namely, arginine and proline metabolism, aldosterone-regulated sodium reabsorption, and oxidative phosphorylation, were observed. Based on the miRNA-mRNA pathway relationships, five significant miRNAs were identified as potential biomarkers: hsa-miR-425, hsa-miR-136, hsa-miR-335, hsa-miR-340, and hsa-miR-320d.
Conclusion: This integrative network approach revealed important miRNAs in the ccRCC that can identify specific disease biomarkers, which can be used as targets for cancer treatment.

Keywords: Clear cell renal cell carcinoma, microRNS, miRNA-mRNA dysregulation, pathway


How to cite this article:
Hao JF, Ren KM, Bai JX, Wang SN, Shao B, Cao N, Li X. Identification of potential biomarkers for clear cell renal cell carcinoma based on microRNA-mRNA pathway relationships. J Can Res Ther 2014;10, Suppl S3:167-77

How to cite this URL:
Hao JF, Ren KM, Bai JX, Wang SN, Shao B, Cao N, Li X. Identification of potential biomarkers for clear cell renal cell carcinoma based on microRNA-mRNA pathway relationships. J Can Res Ther [serial online] 2014 [cited 2019 Aug 20];10:167-77. Available from: http://www.cancerjournal.net/text.asp?2014/10/7/167/145856

Jun-Feng Hao and Kai-Ming Ren contribute equally to this work



 > Introduction Top


Renal cell carcinoma (RCC) accounts for approximately 2% of all cancers, with an annual increase of 1.5-5.9% worldwide. [1],[2],[3] The most common subtype of RCC is clear cell renal cell carcinoma (ccRCC). [4] Researchers have recently suggested that the development of ccRCC is associated with multiple environmental and genetic factors, [5] such as the regulation of microRNAs (miRNAs). miRNAs are an important class of noncoding RNAs that influence posttranscriptional protein levels through targeted mRNA degradation and translational inhibition [6] and affect differentiation, growth, and apoptosis. [7]

Numerous miRNA genes are dysregulated in cancers and influence tumor formation/progression because they are located in commonly overexpressed or deleted regions of the genome. [8],[9],[10] Dysregulated miRNAs contribute to oncogenesis through the loss of tumor-suppressing miRNAs or increased expression of oncomiRs. [11] miRNAs are generated in a cell through canonical or noncanonical pathways. [12],[13],[14] The canonical pathway is dependent on Drosha (RNase III-like protein)/DiGeorge syndrome critical region gene 8 (DGCR8) and Dicer, whereas the noncanonical pathway is independent of Drosha/DGCR8 or Dicer. miRNAs are produced through processing and editing in both pathways and result in the formation of mature, functional miRNAs with 21-25 nucleotides. The canonical pathway of miRNA biogenesis produces most of the known miRNAs. However, several alternative pathways of miRNA biogenesis have recently been identified in both invertebrates and vertebrates. [13],[14] Advanced pathway analyses of putative targets of significantly upregulated miRNAs present the involvement of antioxidative systems, energy metabolism, cell mitosis and proliferation, and extracellular matrix degradation. [15] Multiple analyses of aberrant miRNA expressions in renal tumor tissues have been conducted using microarray or RNA deep sequencing; these analyses provide systematic views of miRNA abnormality in RCC. [5],[16],[17],[18],[19],[20] The von Hippel-Lindau (VHL) tumor suppressor is frequently inactivated in RCC. Neal et al. [21] investigated VHL-dependent miRNA expression and identified the upregulation of mir-210, mir-155, and mir-21 in RCC tumor tissue through VHL inactivation. Numerous miRNAs function as oncogenes or tumor suppressors. [22],[23],[24],[25],[26] miRNAs also possess critical roles in the progression of other renal cancer subtypes. For example, mir-562 targets EYA1 and is inactivated by the mutation at 2q37.1, which result in the expression of EYA1 and induction of Wilms' tumor. [27] miRNAs are more stable than mRNAs and are used as diagnostic and prognostic biomarkers for human diseases. Multiple miRNAs have recently been identified by miRNA profiling; multi-step methods have been developed to distinguish different RCC subtypes [28],[29],[30] and become potential biomarkers for major kidney diseases, such as diabetic nephropathy and ischemic acute kidney injury.

The research of miRNAs in kidneys has increased in the last few years. Most studies focus on selected miRNAs based on miRNA expression profiles or certain miRNA groups under renal disease conditions for identifying one or a few functionally important protein targets. However, every miRNA has numerous potential targets according to a bioinformatics analysis, and multiple miRNAs may target the same protein. Thus, we studied the function of miRNA in ccRCC by integrating the expression levels of miRNAs and mRNAs on a whole-genome scale. miRNAs usually target proteins from one or several related pathways. Consequently, we proposed a frame [Figure 1] for selecting biomarkers by combining the pathways and functional annotations of the targets involved.
Figure 1: The frame of biomarker identification. First, the miRNA and mRNA expression profiles were available from the GEO. And then the different expression miRNAs and different expression mRNAs were extracted. The DE in the figure indicted the different expression. Subsequently, we acquired the anti-regulated miRNA-mRNA pairs and annotated the mRNAs into pathways. Lastly, the miRNA-mRNA-pathway relationships were built based on which the miRNA biomarkers were acquired. Red lines showed the significant miRNA-mRNA-pathway relationships

Click here to view



 > Materials and methods Top


0 Data

miRNA and mRNA microarray expression data including 68 samples were deposited to the Gene Expression Omnibus Database (http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE16441. [31] The total mRNA from 17 ccRCC tumors and 17 corresponding nontumor samples in GSM413237 to GSM413270 was hybridized against a common reference RNA [32] for gene expression analysis, which was based on the GPL6480 Agilent-014850 Whole Human Genome Microarray 4×44K G4112F (Probe Name version). The total miRNA from 17 RCC tumors and 17 corresponding nontumor samples in GSM413271 to GSM413304 was hybridized on a single channel platform for miRNA expression analysis, which was based on the GPL8659 Agilent Human miRNA Microarray Rel12.0. Based on the packages "limma" of the R-project, 119 miRNAs and 3144 mRNAs were significantly differentially expressed between the ccRCC tissues and their corresponding normal tissues.

MicroRNA-mRNA antiregulated pairs

MicroRNAs implicated in a specific tumor phenotype show the aberrant regulation of their target genes. [33] Thus, we screened the anticorrelations between upregulated miRNAs and downregulated mRNAs and vice versa. CoExpress is a tool for an effective Co-Expression Analysis of large microarray data sets, and a user-friendly and allows on-the-fly study of Co-expression (CE) , including: Expression data preprocessing, possibility for customized preprocessiong usin R; filtering; advanced interactive analysis of the expression profiles; building and visualization of CE matrix using correlation or mutual information metrics; comparing the detected co-expressions with predicted targets (for miRNA:mRNA interaction). We used the CoExpress software to study the regulation relationships between 119 significantly differentially expressed miRNAs and 3144 significantly differentially expressed mRNAs. CoExpress is a tool for effective co-expression analysis of large microarray data sets with Pearson correlation. We randomly assigned the samples into two groups 10,000 times to determine if the deviation in correlation between ccRCC and the normal samples is significant and re-extracted the significant miRNA-mRNA antiregulated pairs (R < −0.5).

MicroRNA-mRNA dysregulation network

We downloaded the human protein-protein interactions from STRING to examine the dysregulation between miRNAs and mRNAs in the human protein interaction network. [34] We then constructed a miRNA-mRNA dysregulation network using significant miRNA-mRNA anticorrelation pairs and PPIs with Cytoscape and acquired the topological characteristic of the entire node in the network.

MicroRNA-mRNA pathway relationships

A pathway enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.7 to examine the functional relationships between miRNAs and mRNAs. [35] We enriched the mRNAs of miRNA-mRNA antiregulated pairs into pathways and abstracted the significant pathways (P < 0.05). Based on the bridge of mRNAs, the pathways of the miRNA-mRNA pathway relationships were set up.

Identification of biomarkers

We calculated the correlations among miRNAs, mRNAs, and pathways for every path of the miRNA-mRNA pathway relationship following Equations 1 and 2 starting with the significantly expressed miRNAs.

(1)

(2)

N t (M) is the number of M in object t. Nall (M) is the number of M in all objects.

Taking miRNAi-mRNAj-pathwayk for example, we calculate the weights of miRNAi, mRNAj, and pathwayk. The mRNAs referred to in the equation are from the miRNA-mRNA antiregulated pairs. Weight miRNA is the weight of miRNA and indicates the mRNA proportion regulated by miRNAi in all mRNAs. Weight miRNA is the weight of mRNA and indicates the percentage of pathways that mRNAj enriched in all pathways. Weight pathway is the weight of pathway and presents the level of differentially expressed mRNAs of pathwayk in all mRNAs. Large scores indicate a pivotal relationship pathway. We randomly disturbed all pathways to extract the significant miRNA-mRNA pathway relationships. We maintained the number of mRNAs related to miRNA and randomly selected the same number of mRNAs from all mRNAs of the miRNA-mRNA antiregulated pairs to replace the original mRNAs. Subsequently, we enriched the new mRNAs into pathways and calculated the scores of random miRNA-mRNA pathway relationships. We repeated the process 10,000 times and established a significant threshold by randomly ranking the original scores of the miRNA-mRNA pathway relationships. Finally, we selected the significant miRNA-mRNA pathway relationships (P < 0.05).


 > Results Top


0 Functional annotation for mRNAs dysregulated by microRNAs

A total of 119 differentially expressed miRNAs and 3144 differentially expressed mRNAs were identified using the packages "limma" of R-project. We also identified negatively correlated miRNA-mRNA pairs using CoExpress and acquired 566 antiregulated miRNA-mRNA pairs including 56 miRNAs and 485 mRNAs [Supplement Table 1]. [Additional file 1] The five miRNAs with the most negatively correlated mRNAs were hsa-miR-340, hsa-miR-320d, hsa-miR-93, hsa-miR-425, and hsa-miR-361-3p. We performed functional annotations of the pathway using DAVID to deeply understand the biological functions affected by different dysregulated miRNAs and mRNAs. We selected significant functions (P < 0.05) and then determined that the differentially expressed mRNAs, antiregulated by miRNAs, regulated the establishment of localization and acted on catalytic, transporter, and electron carrier activities. These mRNAs were involved in oxidative phosphorylation, Vibrio cholerae infection, and PPAR signaling pathway [Table 1].
Table 1: Enriched pathways of mRNAs dysregulated by miRNAs


Click here to view


Specific microRNA-mRNA pathway relationships

We mapped the differentially expressed mRNAs into pathways, and these mRNAs were dysregulated by differentially expressed miRNAs. The mRNAs regulating the same biological functions were always regulated by the same miRNAs. Thus, we analyzed the significant miRNAs, mRNAs, and pathways based on the miRNA[TAG:2][/TAG:2]

mRNA pathway relationships. We used the miRNA[TAG:2][/TAG:2]

mRNA pathway as a regulated path in the complex regulation network. Consequently, we identified an important regulation path by calculating the relationships among miRNAs, mRNAs, and pathways and abstracted the miRNAs, which were potential biomarkers for kidney cancer. We acquired five pivotal miRNA[TAG:2][/TAG:2]

mRNA pathway relationships including 5 miRNAs, 10 mRNAs, and 3 important pathways [Table 2].
Table 2: Significant miRNA-mRNA pathway relationships


Click here to view


Analysis of the potential biomarker

Basing on PPI, we constructed the miRNA[TAG:2][/TAG:2]

mRNA dysregulated network consisting of 668 nodes and 1193 edges including 57 miRNAs and 485 differentially expressed mRNAs. We identified five miRNAs (hsa-miR-425, hsa-miR-136, hsa-miR-340, hsa-miR-335, and hsa-miR-320d) that were potential biomarkers for kidney cancer based on the five significant miRNA[TAG:2][/TAG:2]

mRNA pathway relationships. We then extracted four normal topological properties (average shortest path, betweenness centrality, clustering coefficient, and degree) and two other characteristics (Dout and Nshared ). Dout is the number of mRNAs dysregulated by miRNAs, and Nshared is the number of mRNAs shared by two miRNAs. We analyzed the topological characteristics for all miRNAs from the whole dysregulation network [Supplement Table 2] [Additional file 2] and highlighted the five miRNAs in the special miRNA-mRNA pathway relationships. miR-320 exhibited the largest degree, and the betweenness centrality of miR-335 was the most intense. Moreover, the average shortest path of miR-136 was the shortest, and miR-340 shared the most mRNAs compared with other miRNAs [Table 3].
Table 3: Topological properties for five potential biomarkers


Click here to view


We performed logistic regression using the five miRNAs and observed that the significance for all miRNAs was less than 0.05. We also utilized the five miRNAs to classify the ccRCC samples and normal tissues using the support vector machine and 10-fold cross validation and obtained an AUC score of 100%. Thus, the five miRNAs were ideal biomarkers for theoretically diagnosing ccRCC. However, further actual experiments must be conducted.


 > Discussion Top


We identified five significant relationships and acquired five potential biomarkers (hsa-miR-425, hsa-miR-136, hsa-miR-340, hsa-miR-335, and hsa-miR-320d) from the miRNA-mRNA pathway relationships. In the most significant pathway (hsa00330: Arginine and proline metabolism), six differentially expressed mRNAs (NOS1, ALDH1B1, CKM, ALDH4A1, GLS, and SAT1) were regulated by hsa-miR-425, hsa-miR-320d, and hsa-miR-335. However, the relationship of miRNAs to ccRCC occurrence has rarely been studied. Thus, we inferred the function of hsa-miR-425 in ccRCC through other cancers.

Hsa-miR-425 is expressed in human atria and ventricles and is predicted to bind the sequence spanning rs5068 for the A allele, but not the G allele. Arora et al. [36] indicated that miR-425 regulates ANP production and that miR-425 antagonists can be used to treat salt overload disorders, including hypertension and heart failure. Di Leva et al. [37] demonstrated that the miR-425 cluster fundamentally affects the initiation and progression of breast cancer cells by reducing the expression of an extensive gene network. The inhibition of miR-425 in gastric carcinogenesis cell line HGC-27 not only reduces cell proliferation and cycle progression, but also impairs cell migration and invasion. [38] Rio-Machin et al. [39] showed that the downregulation of miR-425 and upregulation of their targets occur simultaneously in primary cases of nonhyperdiploid multiple myeloma. Wojcicka et al. [40] indicated that the increased expression of miR-425 is responsible for the downregulation of thyroid hormone receptor beta (THRB) in ccRCC tumors. The THRB gene is commonly deregulated in cancers and is postulated to possess a tumor-suppressive role. We determined that miR-425 was downregulated in ccRCC compared with the matched controls. Thus, miR-425 is a potential biomarker for ccRCC diagnosis in the clinic.

Wu et al. reported that the low expression of miR-320d is associated with the poor prognosis of diffuse large B-cell lymphoma (DLBCL) patients treated with the standard cyclophosphamide, doxorubicin, vincristine, and prednisone regimen and decreased both progression-free and overall survival. Functional studies demonstrated that the overexpression of miR-320d inhibited DLBCL cell proliferation, whereas the knockdown of miR-320d promoted the proliferation of DLBCL cells. [41] miR-360d was downregulated compared with normal kidney tissues. Thus, miR-320d can induce the occurrence of ccRCC. In the miRNA-mRNA dysregulation network, the degree and anticorrelated mRNAs of miR-320d were the largest, which indicates that miR-320d has a key function in the occurrence of ccRCC. We mapped the mRNAs anticorrelated to miR-320d into DAVID, and the results showed that the mRNAs deregulated by miR-320d involved the establishment of localization and functioned in electron carrier as well as catalytic and transporter activities. They also acted in the pathways of epithelial cell signaling in Helicobacter pylori infection, oxidative phosphorylation, arginine and proline metabolism, and valine, leucine, and isoleucine degradation. They also enriched the tissues of kidneys, lungs, livers, and brain.

miR-136 has a tumor-suppressive role in downregulated human glioma [42] and promotes apoptosis of glioma cells induced by chemotherapy. The low-level expression of miR-136 is significantly associated with aggressive and/or poor prognostic phenotype of patients with gliomas. Gain-of-function and loss-of-function experiments showed that the miR-136 expression reverses cisplatin resistance and enhances the response to cisplatin treatment. [43] However, miR-136 was upregulated in ccRCC compared with normal tissues and acted as tumor-activators in promoting cell proliferation in ccRCC.

Zhang et al. [44] proved that telmisartan improves kidney function by inhibiting the oxidative phosphorylation pathway. Small et al. [45] observed that oxidative stress promotes mitochondrial destabilization in kidney proximal tubular epithelium. Thus, we conclude that the oxidative phosphorylation pathway is related to the occurrence of ccRCC. miR-340 is involved in oxidative phosphorylation pathways. Takeyama et al. [46] showed that decreased miR-340 expression in the bone marrow is associated with liver metastasis of colorectal cancer. Zhou et al. [47] indicated that miR-340 acted as a tumor suppressor. They also found that miR-340 overexpression in osteosarcoma cell lines significantly inhibited cell proliferation, migration, and invasion in vitro as well as tumor growth and metastasis in a xenograft mouse model. We observed that miR-340 was downregulated in ccRCC and can be a potential biomarker for ccRCC diagnosis. This analysis showed that dysregulation between miRNAs and mRNAs result in ccRCC occurrence.


 > Conclusion Top


Clear cell renal cell carcinoma exhibited the highest mortality rate and accounted for nearly 80-85% of kidney tumors in humans. miRNAs are a class of small, noncoding, single-stranded RNAs that downregulate gene expression. The dysregulation of miRNAs disrupts early kidney development, renal progenitor cell differentiation, and maintenance of mature nephrons. Differentially regulated miRNAs represent innovative biomarkers for diagnosis and prognosis of ccRCC. Compared with protein or enzyme-based tests, miRNA biomarkers are highly stable and are reliably analyzed and quantified by real-time polymerase chain reaction. We can identify the important miRNAs that contribute to the special pathways by combining knowledge of pathways with miRNA-mRNA dysregulation relationships. This analysis can be used to identify new and relevant indicators of ccRCC and can offer insights toward the development of targeted molecular therapies for ccRCC. Finally, five significant miRNAs, namely, hsa-miR-425, hsa-miR-136, hsa-miR-335, hsa-miR-340, and hsa-miR-320d, were identified as potential biomarkers.

 
 > References Top

1.
Boorjian S. Commentary on "reproductive factors and kidney cancer risk in 2 US cohort studies, 1993-2010." Karami S, Daugherty SE, Schonfeld SJ, Park Y, Hollenbeck AR, Grubb RL 3 rd , Hofmann JN, Chow WH, Purdue MP, Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD. Am J Epidemiol 2013;177:1368-77.  Back to cited text no. 1
    
2.
Chow WH, Dong LM, Devesa SS. Epidemiology and risk factors for kidney cancer. Nat Rev Urol 2010;7:245-57.  Back to cited text no. 2
    
3.
Janout V, Janoutová G. Epidemiology and risk factors of kidney cancer. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2004;148:95-101.  Back to cited text no. 3
    
4.
Rini BI, Campbell SC, Escudier B. Renal cell carcinoma. Lancet 2009;373:1119-32.  Back to cited text no. 4
    
5.
Huang Y, Dai Y, Yang J, Chen T, Yin Y, Tang M, et al. Microarray analysis of microRNA expression in renal clear cell carcinoma. Eur J Surg Oncol 2009;35:1119-23.  Back to cited text no. 5
    
6.
DeVere White RW, Vinall RL, Tepper CG, Shi XB. MicroRNAs and their potential for translation in prostate cancer. Urol Oncol 2009;27:307-11.  Back to cited text no. 6
    
7.
Calin GA, Croce CM. MicroRNA-cancer connection: The beginning of a new tale. Cancer Res 2006;66:7390-4.  Back to cited text no. 7
    
8.
Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, Yendamuri S, et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 2004;101:2999-3004.  Back to cited text no. 8
    
9.
Liu J, Zhong X, Li J, Liu B, Guo S, Chen J, et al. Screening and identification of lung cancer metastasis-related genes by suppression subtractive hybridization. Thorac Cancer 2012;3:207-16.  Back to cited text no. 9
    
10.
Nurwidya F, Takahashi F, Takahashi K. Meeting Report: Current Cancer Perspectives from the 9 th Annual Meeting of the Japanese Society of Medical Oncology. Thorac Cancer 2012;3:94-7.  Back to cited text no. 10
    
11.
Esquela-Kerscher A, Slack FJ. Oncomirs-microRNAs with a role in cancer. Nat Rev Cancer 2006;6:259-69.  Back to cited text no. 11
    
12.
Bhatt K, Mi QS, Dong Z. microRNAs in kidneys: Biogenesis, regulation, and pathophysiological roles. Am J Physiol Renal Physiol 2011;300:F602-10.  Back to cited text no. 12
    
13.
Miyoshi K, Miyoshi T, Siomi H. Many ways to generate microRNA-like small RNAs: Non-canonical pathways for microRNA production. Mol Genet Genomics 2010;284:95-103.  Back to cited text no. 13
    
14.
Yang JS, Lai EC. Alternative miRNA biogenesis pathways and the interpretation of core miRNA pathway mutants. Mol Cell 2011;43:892-903.  Back to cited text no. 14
    
15.
Bai XY, Ma Y, Ding R, Fu B, Shi S, Chen XM. MiR-335 and miR-34a Promote renal senescence by suppressing mitochondrial antioxidative enzymes. J Am Soc Nephrol 2011;22:1252-61.  Back to cited text no. 15
    
16.
Gottardo F, Liu CG, Ferracin M, Calin GA, Fassan M, Bassi P, et al. Micro-RNA profiling in kidney and bladder cancers. Urol Oncol 2007;25:387-92.  Back to cited text no. 16
    
17.
Yi Z, Fu Y, Zhao S, Zhang X, Ma C. Differential expression of miRNA patterns in renal cell carcinoma and nontumorous tissues. J Cancer Res Clin Oncol 2010;136:855-62.  Back to cited text no. 17
    
18.
White NM, Khella HW, Grigull J, Adzovic S, Youssef YM, Honey RJ, et al. miRNA profiling in metastatic renal cell carcinoma reveals a tumour-suppressor effect for miR-215. Br J Cancer 2011;105:1741-9.  Back to cited text no. 18
    
19.
Osanto S, Qin Y, Buermans HP, Berkers J, Lerut E, Goeman JJ, et al. Genome-wide microRNA expression analysis of clear cell renal cell carcinoma by next generation deep sequencing. PLoS One 2012;7:e38298.  Back to cited text no. 19
    
20.
Zhou L, Chen J, Li Z, Li X, Hu X, Huang Y, et al. Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27.3 associate with clear cell renal cell carcinoma. PLoS One 2010;5:e15224.  Back to cited text no. 20
    
21.
Neal CS, Michael MZ, Rawlings LH, Van der Hoek MB, Gleadle JM. The VHL-dependent regulation of microRNAs in renal cancer. BMC Med 2010;8:64.  Back to cited text no. 21
    
22.
Zhai Q, Zhou L, Zhao C, Wan J, Yu Z, Guo X, et al. Identification of miR-508-3p and miR-509-3p that are associated with cell invasion and migration and involved in the apoptosis of renal cell carcinoma. Biochem Biophys Res Commun 2012;419:621-6.  Back to cited text no. 22
    
23.
Yamamura S, Saini S, Majid S, Hirata H, Ueno K, Chang I, et al. MicroRNA-34a suppresses malignant transformation by targeting c-Myc transcriptional complexes in human renal cell carcinoma. Carcinogenesis 2012;33:294-300.  Back to cited text no. 23
    
24.
Sakurai T, Bilim VN, Ugolkov AV, Yuuki K, Tsukigi M, Motoyama T, et al. The enhancer of zeste homolog 2 (EZH2), a potential therapeutic target, is regulated by miR-101 in renal cancer cells. Biochem Biophys Res Commun 2012;422:607-14.  Back to cited text no. 24
    
25.
Hidaka H, Seki N, Yoshino H, Yamasaki T, Yamada Y, Nohata N, et al. Tumor suppressive microRNA-1285 regulates novel molecular targets: Aberrant expression and functional significance in renal cell carcinoma. Oncotarget 2012;3:44-57.  Back to cited text no. 25
    
26.
Saini S, Yamamura S, Majid S, Shahryari V, Hirata H, Tanaka Y, et al. MicroRNA-708 induces apoptosis and suppresses tumorigenicity in renal cancer cells. Cancer Res 2011;71:6208-19.  Back to cited text no. 26
    
27.
Drake KM, Ruteshouser EC, Natrajan R, Harbor P, Wegert J, Gessler M, et al. Loss of heterozygosity at 2q37 in sporadic Wilms' tumor: Putative role for miR-562. Clin Cancer Res 2009;15:5985-92.  Back to cited text no. 27
    
28.
Youssef YM, White NM, Grigull J, Krizova A, Samy C, Mejia-Guerrero S, et al. Accurate molecular classification of kidney cancer subtypes using microRNA signature. Eur Urol 2011;59:721-30.  Back to cited text no. 28
    
29.
Powers MP, Alvarez K, Kim HJ, Monzon FA. Molecular classification of adult renal epithelial neoplasms using microRNA expression and virtual karyotyping. Diagn Mol Pathol 2011;20:63-70.  Back to cited text no. 29
    
30.
Fridman E, Dotan Z, Barshack I, David MB, Dov A, Tabak S, et al. Accurate molecular classification of renal tumors using microRNA expression. J Mol Diagn 2010;12:687-96.  Back to cited text no. 30
    
31.
Liu H, Brannon AR, Reddy AR, Alexe G, Seiler MW, Arreola A, et al. Identifying mRNA targets of microRNA dysregulated in cancer: With application to clear cell Renal Cell Carcinoma. BMC Syst Biol 2010;4:51.  Back to cited text no. 31
    
32.
Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-52.  Back to cited text no. 32
    
33.
Xu J, Li CX, Lv JY, Li YS, Xiao Y, Shao TT, et al. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: Case study of prostate cancer. Mol Cancer Ther 2011;10:1857-66.  Back to cited text no. 33
    
34.
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011;39:D561-8.  Back to cited text no. 34
    
35.
Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.  Back to cited text no. 35
    
36.
Arora P, Wu C, Khan AM, Bloch DB, Davis-Dusenbery BN, Ghorbani A, et al. Atrial natriuretic peptide is negatively regulated by microRNA-425. J Clin Invest 2013;123:3378-82.  Back to cited text no. 36
    
37.
Di Leva G, Piovan C, Gasparini P, Ngankeu A, Taccioli C, Briskin D, et al. Estrogen mediated-activation of miR-191/425 cluster modulates tumorigenicity of breast cancer cells depending on estrogen receptor status. PLoS Genet 2013;9:e1003311.  Back to cited text no. 37
    
38.
Peng WZ, Ma R, Wang F, Yu J, Liu ZB. Role of miR-191/425 cluster in tumorigenesis and diagnosis of gastric cancer. Int J Mol Sci 2014;15:4031-48.  Back to cited text no. 38
    
39.
Rio-Machin A, Ferreira BI, Henry T, Gómez-López G, Agirre X, Alvarez S, et al. Downregulation of specific miRNAs in hyperdiploid multiple myeloma mimics the oncogenic effect of IgH translocations occurring in the non-hyperdiploid subtype. Leukemia 2013;27:925-31.  Back to cited text no. 39
    
40.
Wojcicka A, Piekielko-Witkowska A, Kedzierska H, Rybicka B, Poplawski P, Boguslawska J, et al. Epigenetic regulation of thyroid hormone receptor beta in renal cancer. PLoS One 2014;9:e97624.  Back to cited text no. 40
    
41.
Wu PY, Zhang XD, Zhu J, Guo XY, Wang JF. Low expression of microRNA-146b-5p and microRNA-320d predicts poor outcome of large B-cell lymphoma treated with cyclophosphamide, doxorubicin, vincristine, and prednisone. Hum Pathol 2014;45:1664-73.  Back to cited text no. 41
    
42.
Yang Y, Wu J, Guan H, Cai J, Fang L, Li J, et al. MiR-136 promotes apoptosis of glioma cells by targeting AEG-1 and Bcl-2. FEBS Lett 2012;586:3608-12.  Back to cited text no. 42
    
43.
Chen W, Yang Y, Chen B, Lu P, Zhan L, Yu Q, et al. MiR-136 targets E2F1 to reverse cisplatin chemosensitivity in glioma cells. J Neurooncol 2014;120:43-53.  Back to cited text no. 43
    
44.
Zhang Q, Xiao X, Li M, Li W, Yu M, Zhang H, et al. Telmisartan improves kidney function through inhibition of the oxidative phosphorylation pathway in diabetic rats. J Mol Endocrinol 2012;49:35-46.  Back to cited text no. 44
    
45.
Small DM, Morais C, Coombes JS, Bennett NC, Johnson DW, Gobe GC. Oxidative stress-induced alterations in PPAR-? and associated mitochondrial destabilization contribute to kidney cell apoptosis. Am J Physiol Renal Physiol 2014;307:F814-22.  Back to cited text no. 45
    
46.
Takeyama H, Yamamoto H, Yamashita S, Wu X, Takahashi H, Nishimura J, et al. Decreased miR-340 expression in bone marrow is associated with liver metastasis of colorectal cancer. Mol Cancer Ther 2014;13:976-85.  Back to cited text no. 46
    
47.
Zhou X, Wei M, Wang W. MicroRNA-340 suppresses osteosarcoma tumor growth and metastasis by directly targeting ROCK1. Biochem Biophys Res Commun 2013;437:653-8.  Back to cited text no. 47
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  >Abstract>Introduction>Materials and me...>Results>Discussion>Conclusion>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed2101    
    Printed90    
    Emailed1    
    PDF Downloaded99    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]