Open Access

Identification and functional analysis of risk‑related microRNAs for the prognosis of patients with bladder urothelial carcinoma

  • Authors:
    • Ji Gao
    • Hongyan Li
    • Lei Liu
    • Lide Song
    • Yanting Lv
    • Yuping Han
  • View Affiliations

  • Published online on: October 3, 2017     https://doi.org/10.3892/ol.2017.7124
  • Pages: 7297-7303
  • Copyright: © Gao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The aim of the present study was to investigate risk‑related microRNAs (miRs) for bladder urothelial carcinoma (BUC) prognosis. Clinical and microRNA expression data downloaded from the Cancer Genome Atlas were utilized for survival analysis. Risk factor estimation was performed using Cox's proportional regression analysis. A microRNA‑regulated target gene network was constructed and presented using Cytoscape. In addition, the Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, followed by protein‑protein interaction (PPI) network analysis. Finally, the K‑clique method was applied to analyze sub‑pathways. A total of 16 significant microRNAs, including hsa‑miR‑3622a and hsa‑miR‑29a, were identified (P<0.05). Following Cox's proportional regression analysis, hsa‑miR‑29a was screened as a prognostic marker of BUC risk (P=0.0449). A regulation network of hsa‑miR‑29a comprising 417 target genes was constructed. These target genes were primarily enriched in GO terms, including collagen fibril organization, extracellular matrix (ECM) organization and pathways, such as focal adhesion (P<0.05). A PPI network including 197 genes and 510 interactions, was constructed. The top 21 genes in the network module were enriched in GO terms, including collagen fibril organization and pathways, such as ECM receptor interaction (P<0.05). Finally, 4 sub‑pathways of cysteine and methionine metabolism, including paths 00270_4, 00270_1, 00270_2 and 00270_5, were obtained (P<0.01) and identified to be enriched through DNA (cytosine‑5)‑methyltransferase (DNMT)3A, DNMT3B, methionine adenosyltransferase 2α (MAT2A) and spermine synthase (SMS). The identified microRNAs, particularly hsa‑miR‑29a and its 4 associated target genes DNMT3A, DNMT3B, MAT2A and SMS, may participate in the prognostic risk mechanism of BUC.

Introduction

Bladder urothelial carcinoma (BUC), a malignancy of the genitourinary system, is one of the most common types of bladder cancer (1). At present, the risk factors of BUC primarily comprise smoking and contact with aromatic amine chemicals (1). BUC may be divided into two categories: Non-muscle- and muscle-invasive BUC (2). Transurethral resection and radical cystectomy are the current treatment strategies for non-muscle- and muscle-invasive BUC, respectively (3). Although numerous methods have been suggested, an effective treatment remains elusive due to high recurrence rates. A more thorough understanding of the underlying molecular mechanism of prognostic risk may be beneficial for the development of therapeutic interventions, and therefore the prognosis of patients with BUC.

MicroRNAs are a group of non-coding small RNAs, comprising ~21 nucleotides, which regulate the expression of target genes through binding to 3′-untranslated regions (UTRs) (4). Previous studies have demonstrated the association between microRNAs and risk factors in the prognosis of BUC (5), including miR-141 expression, which was revealed to be significantly downregulated in invasive bladder cancer (6). miR-141 regulates kelch-like ECH-associated protein 1 and controls the oxidative stress response that is associated with the prognosis of BUC (7). In addition, miR-205 targets PH domain leucine-rich repeat-containing protein phosphatase 2 and phosphatase and tensin homolog (PTEN), further influencing protein kinase B signaling (8). Cathomas et al (9) demonstrated that the expression of PTEN was associated with the development of chemotherapy- and castration-resistant cancer, as well as patient prognosis. Additionally, members of the epidermal growth factor (EGF) family have been suggested as potential prognostic markers in BUC (10); at the same time, resistance of EGF receptor is reversed by miR-200 in BUC (11). Therefore, miR-200 serves an important role in the prognostic risk of BUC and is an independent marker associated with an increased risk of non-muscle-invasive bladder cancer recurrence (12).

An improved understanding of microRNA-associated risk factors may clarify the prognostic molecular mechanism of BUC. In the present study, microRNA expression profile data and clinical data were downloaded, survival curves were created to estimate risk factors and target genes regulated by microRNA were analyzed. In addition, regulation networks were constructed and functional analysis of target genes was performed. Finally, a protein-protein interaction (PPI) network of target genes regulated by microRNA was analyzed and a sub-pathway analysis was performed.

Materials and methods

Data sources

Clinical case data and expression profile data of microRNAs were downloaded from the Cancer Genome Atlas (TCGA; cancergenome.nih.gov) database on the BCGSC_IlluminaHiSeq_miRNASeq platform (Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada). The TCGA microRNA expression data were obtained from 529 patients with BUC (download cut-off date, August 11, 2014). Reads per kilobase of exon per million mapped reads (RPKM) was used to quantify the expression value of patient microRNA (13) using the following formula: RPKM = total microRNA reads/[total mapped reads (million) × microRNA sequence length (kb)]. Additionally, clinical case data comprised 411 patients with urothelial bladder carcinoma (download cut-off date, August 11, 2014). A total of 408 cases that exhibited microRNA expression profile data were selected for analysis.

Survival analysis

The mean expression value of each microRNA in the 408 cases was calculated as the critical value. All cases were divided into two groups: MicroRNA expression greater than the critical value, and microRNA expression equal to or less than the critical value of microRNA expression. A Kaplan-Meier estimator survival curve was created for microRNA in the two groups and a log-rank test was applied to analyze the significance. MicroRNAs exhibiting a significantly different survival curve were screened as candidates for prognostic factors. P<0.05 was considered to indicate a statistically significant difference.

Identification of risk-related miRNAs

Cox's proportional hazards regression model was used to estimate the risk factors for collected clinical data and microRNA that demonstrated a significant effect on the survival curves. KMsurv (14) and survival (15) packages in R language were applied for the plotting of survival curves and Cox's proportional hazards regression model. Cox's proportional hazards regression model was created according to the backward selection method; variables were first introduced and subsequently the free variables with no significant differences were eliminated [hazard ratio (HR), 0.99997; P=0.0449].

Analysis of key target genes regulated by microRNA

MicroRNA target genes were predicted from relevant databases, including two validation databases, miRNecords (16) and miRWalk (17). To be applicable for the present study, the predicted regulatory association must have existed in at least three of the following databases: miRanda (18), mirTarget2 (19), PicTar (20), PITA (21) and TargetScan (22). Genes that complied with the two aforementioned requirements were screened. A regulatory network was created and visualized using Cytoscape (23), based on the predicted target genes. Cytoscape is an open source software platform for visualizing complex networks and integrating these with any data type.

Functional analysis of target genes

The Database for Annotation, Visualization and Integrated Discovery, which provides analytical tools for extracting biological relevance from collections of genes (24), was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of target genes in the microRNA-regulated network. P<0.05 was used as the threshold criterion.

PPI network analysis of microRNA target genes

The PPI network of target genes was constructed using the Search Tool for Retrieval of Interacting Genes database, which provided integrated knowledge of the known and predicted associations for protein networks (25). PPI pairs with a combined score >0.4 were screened and visualized using Cytoscape.

Sub-pathway analysis of target genes

The K-clique method was used to divide metabolic pathways into sub-pathways, based on structural information, and to identify risk pathways using hypergeometric test (26). ISubpathway Miner limma (27) in R was applied for investigation of the processes of K-clique recognized risk sub-pathways. Sub-pathways with P<0.05 were considered to be risk sub-pathways. The associations between pathways and disease with target gene involvement were investigated.

Results

Survival analysis

A total of 16 survival curves that significantly affected microRNA were obtained. Among them, the survival curves, including those for hsa-miR-3622a, hsa-miR-1292 and hsa-miR-3138 with significantly longer survival times and has-miR-29a with shorter survival time, were obtained on the condition that expression of microRNA was higher than the mean critical value. Another 12 survival curves exhibited significant longer survival time on the condition that the expression of microRNA was lower than mean value.

Cox's proportional regression analysis

Prognostic hazard ratios of microRNA were obtained using Cox's proportional regression analysis of the aforementioned 16 microRNA expression values. hsa-miR-29a was identified as a risk microRNA associated with the prognosis of UBC.

Risk-related microRNA regulation network

A regulation network of hsa-miR-29a was constructed by collecting and arranging database data of microRNA regulated target genes; a total 417 target genes were contained in the network (Fig. 1).

Functional enrichment analysis of target genes

Based on the results of enrichment analysis, the target genes of hsa-miR-29a were primarily enriched in GO terms, including collagen fibril organization (P=2.64×10−6), extracellular matrix (ECM) organization (P=2.02×10−5), homophilic cell adhesion (P=3.66×10−5) and extracellular structure organization (P=7.45×10−5). These target genes were also enriched in pathways that included focal adhesion (P=5.06×10−10), ECM-receptor interaction (P=1.16×10−9) and small cell lung cancer (P=7.71×10−6), and pathways in cancer (P=1.11×10−3) (Table I).

Table I.

Top 5 GO terms and pathways enrichment of hsa-miR-29a target genes.

Table I.

Top 5 GO terms and pathways enrichment of hsa-miR-29a target genes.

CategoryTermCountP-value
GOTERM_BP_FATGO:0030199~collagen fibril organization  8 2.64×10−6
GOTERM_BP_FATGO:0030198~ECM organization12 2.02×10−5
GOTERM_BP_FAT GO:0007156~homophilic cell adhesion13 3.66×10−5
GOTERM_BP_FAT GO:0043062~extracellular structure organization14 7.45×10−5
GOTERM_BP_FAT GO:0022610~biological adhesion33 9.53×10−5
GOTERM_CC_FAT GO:0005581~collagen18 4.91×10−20
GOTERM_CC_FATGO:0044420~ECM part24 1.84×10−16
GOTERM_CC_FAT GO:0005578~proteinaceous ECM34 2.30×10−14
GOTERM_CC_FATGO:0031012~ECM35 3.42×10−14
GOTERM_CC_FATGO:0005604~basement membrane15 6.33×10−10
GOTERM_MF_FATGO:0005201~ECM structural constituent19 9.83×10−13
GOTERM_MF_FATGO:0048407~PDGF binding  7 6.57×10−8
GOTERM_MF_FAT GO:0005198~structural molecule activity30 4.42×10−4
GOTERM_MF_FATGO:0003677~DNA binding76 1.83×10−3
GOTERM_MF_FATGO:0019838~growth factor binding  9 3.22×10−3
KEGG_PATHWAYhsa04510: Focal adhesion22 5.06×10−10
KEGG_PATHWAYhsa04512: ECM-receptor interaction15 1.16×10−9
KEGG_PATHWAYhsa05222: Small cell lung cancer11 7.71×10−6
KEGG_PATHWAYhsa05200: Pathways in cancer17 1.11×10−3
KEGG_PATHWAYhsa05214: Glioma  7 1.85×10−3
REACTOME_PATHWAYREACT_16888: Signaling by PDGF14 2.20×10−10
REACTOME_PATHWAYREACT_18266: Axon guidance12 1.99×10−9
REACTOME_PATHWAYREACT_13552: Integrin cell surface interactions11 4.20×10−6
REACTOME_PATHWAYREACT_604: Hemostasis10 4.97×10−2

[i] ECM, extracellular matrix; PDGF, platelet-derived growth factor.

PPI network analysis of target genes

A PPI network with 197 genes and 510 edges was constructed (Fig. 2). In this network, collagen type 1 α chain 2 (COL1A2)2, COL1A1 and COL3A1 were the top three nodes, the degrees of which were 25, 24 and 24, respectively. In addition, the top 5 pairs with the greatest combined score were phosphatidylinositol 3-kinase regulatory subunit 1-platelet-derived growth factor receptor β (0.999), COL5A2-COL5A1 (0.992), L5A2-COL11A1 (0.999), L5A1-COL5A3 (0.999) and COL4A6-COL4A5 (0.999). Values in brackets are the combined score value.

Furthermore, a network module with 21 genes was screened from the PPI network (Fig. 3). The enrichment results of this module are presented in Table II. The genes in this module were primarily enriched in functions that included collagen fibril organization (P=2.97×10−15), ECM organization (P=3.01×10−15), cell adhesion (P=1.15×10−13) and biological adhesion (P=1.17×10−13).

Table II.

Top 5 GO terms and pathway enrichment of hsa-miR-29a target genes in network module 1.

Table II.

Top 5 GO terms and pathway enrichment of hsa-miR-29a target genes in network module 1.

CategoryTermCountP-value
GOTERM_BP_FATGO:0030199~collagen fibril organization  8 2.97×10−15
GOTERM_BP_FATGO:0030198~ECM organization10 3.01×10−15
GOTERM_BP_FATGO:0007155~cell adhesion14 1.15×10−13
GOTERM_BP_FAT GO:0022610~biological adhesion14 1.17×10−13
GOTERM_BP_FAT GO:0043062~extracellular structure organization10 1.90×10−13
GOTERM_CC_FAT GO:0005581~collagen18 2.85×10−43
GOTERM_CC_FATGO:0044420~ECM part18 7.38×10−33
GOTERM_CC_FAT GO:0005578~proteinaceous ECM20 4.29×10−30
GOTERM_CC_FATGO:0031012~ECM20 1.86×10−29
GOTERM_CC_FAT GO:0044421~extracellular region part20 6.82×10−21
GOTERM_MF_FATGO:0005201~ECM structural constituent16 8.63×10−30
GOTERM_MF_FAT GO:0005198~structural molecule activity18 4.09×10−20
GOTERM_MF_FATGO:0048407~PDGF binding  6 2.32×10−12
GOTERM_MF_FATGO:0019838~growth factor binding  6 4.43×10−7
GOTERM_MF_FATGO:0005178~integrin binding  4 9.62×10−5
KEGG_PATHWAYhsa04512: ECM-receptor interaction14 2.60×10−24
KEGG_PATHWAYhsa04510: Focal adhesion14 3.93×10−19
KEGG_PATHWAYhsa05222: Small cell lung cancer  5 4.42×10−5
KEGG_PATHWAYhsa05200: Pathways in cancer  5 7.63×10−3
REACTOME_PATHWAYREACT_18266: Axon guidance12 2.01×10−20
REACTOME_PATHWAYREACT_16888: Signaling by PDGF12 5.11×10−19
REACTOME_PATHWAYREACT_13552: Integrin cell surface interactions  9 3.37×10−11

[i] ECM, extracellular matrix; PDGF, platelet-derived growth factor.

Risk sub-pathway analysis

A total of 4 sub-pathways of cysteine and methionine metabolism were obtained, including paths 00270_4 (P=4.11×10−4), 00270_1 (P=6.16×10−4), 00270_2 (P=5.40×10−3) and 00270_5 (P=6.26×10−3). Paths 00270_4 and 00270_1 were enriched by DNA (cytosine-5)-methyltransferase 3α (DNMT3A), DNMT3β (DNMT3B), methionine adenosyltransferase 2α (MAT2Α) and spermine synthase (SMS), whereas paths 00270_2 and 00270_5 were enriched by DNMT3A, DNMT3B and MAT2A (Table III).

Table III.

Analyzed results of risk pathways.

Table III.

Analyzed results of risk pathways.

Pathway IDPathway nameP-valueGene
path:00270_4Cysteine and methionine metabolism 4.11×10−4DNMT3A, DNMT3B, MAT2A, SMS
path:00270_1Cysteine and methionine metabolism 6.16×10−4DNMT3A, DNMT3B, MAT2A, SMS
path:00270_2Cysteine and methionine metabolism 5.40×10−3DNMT3A, DNMT3B, MAT2A
path:00270_5Cysteine and methionine metabolism 6.26×10−3DNMT3A, DNMT3B, MAT2A

[i] DNMT, DNA(cytosine-5)-methyltransferase; MAT, methionine adenosyltransferase; SMS, spermine synthase.

Discussion

BUC is a malignancy of the genitourinary system that is difficult to effectively treat due to high recurrence rates (28). In the present study, hsa-miR-29a was screened as a prognostic risk-related microRNA of BUC. In addition, 21 genes in the network module were enriched in GO terms, including collagen fibril organization and ECM organization, and were enriched in pathways, including ECM-receptor interaction and focal adhesion. Finally, 4 pathways, including path00270_4, path00270_1, path00270_2 and path00270_5, were obtained and enriched by 4 target genes, DNMT3A DNMT3B, MAT2A and SMS.

hsa-miR-29a was the only microRNA that significantly affected the prognosis of BUC. hsa-miR-29a is a microRNA member of the miR-29 family, the dysregulation of which has been demonstrated to affect DNMT3A expression in the HL1 cell line (29). Notably, in the DNMT3A mutation samples, DNA methylation patterns were altered (30). In other types of cancer, including lung cancer, the miR-29 family reversed biological processes of aberrant DNA methylation and was associated with a poor prognosis in cancer (31). In addition, downregulated miR-29a may promote transforming growth factor β induction and further promote the fibrotic response by interacting with genes, including fibrillin, elastin and collagens (32). Similar to DNMT3A, DNMT3B also exhibits complementarities with the miR-29 family at 3′-UTRs (31). The synthesis of S-adenosyl-(L)-methionine (adoMet), the primary methyl group donor in humans, is the primary step in the process of methionine metabolism (33). Through Adohcy, the transfer of activated methyl groups is naturally catalyzed from AdoMet to C5 atom by DNMT3A and DNMT3B (34). Consistent with previous studies, results in the present study revealed that DNMT3A and DNMT3B were regulated by miR-29a, and enriched in the cysteine and methionine metabolism pathway, affecting the prognosis of BUC.

Furthermore, miR-29a has also been demonstrated to regulate MAT2A and SMS. MAT2A is a mammalian gene that encodes MAT (35). AdoMet is an intermediate metabolite that also functions as an intracellular control switch, which regulates essential functions (36). Furthermore, MAT2A serves a role in the methionine cycle pathway, which is an important metabolic pathway (37). Although the molecular mechanisms of SMS associated with BUC prognostic risk have not been reported, the results of the present study suggest that they may serve important roles in BUC prognostic risk through their involvement in the cysteine and methionine metabolism pathway.

In addition to the aforementioned pathways, miR-29a was also enriched in ECM organization and biological adhesion. Ioachim et al (38) demonstrated that thrombospondin type 1 serves an important role in the prognosis of cancer, being enriched in ECM organization pathways. β1-integrin has been demonstrated to downregulate expression of miR-29a, whilst increased expression of β1-integrin in BUC cells induces tissue invasion (39). Cell invasion is the primary factor associated with poor prognosis (40). Through these pathways, miR-29a may exhibit an important prognostic risk.

Although several key genes and pathways associated with BUC were identified using comprehensive bioinformatic methods, no experiment was conducted to verify the results and this therefore presents a clear limitation to the present study. Further experimental studies of diverse samples are thus required to validate the results of the present study.

In conclusion, the identified microRNAs, particularly hsa-miR-29a, may serve important roles in the prognostic risk mechanism of BUC through the regulation of 4 target genes, including DNMT3A, DNMT3B, MAT2A and SMS, and through involvement in cysteine and methionine metabolism pathways. However, further study is required to support the potential association between microRNAs, target genes and prognostic risk factors.

Glossary

Abbreviations

Abbreviations:

BUC

bladder urothelial carcinoma

EGF

epidermal growth factor

GO

Gene Ontology

PPI

protein-protein interaction

References

1 

Ploeg M, Aben KK and Kiemeney LA: The present and future burden of urinary bladder cancer in the world. World J Urol. 27:289–293. 2009. View Article : Google Scholar : PubMed/NCBI

2 

Kauffman EC, Ng CK, Lee MM, Otto BJ, Wang GJ and Scherr DS: Early oncological outcomes for bladder urothelial carcinoma patients treated with robotic-assisted radical cystectomy. BJU Int. 107:628–635. 2011. View Article : Google Scholar : PubMed/NCBI

3 

Wright JL, Black PC, Brown GA, Porter MP, Kamat AM, Dinney CP and Lin DW: Differences in survival among patients with sarcomatoid carcinoma, carcinosarcoma and urothelial carcinoma of the bladder. J Urol. 178:2302–2307. 2007. View Article : Google Scholar : PubMed/NCBI

4 

Bartel DP: MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell. 116:281–297. 2004. View Article : Google Scholar : PubMed/NCBI

5 

Shi Z, Wei Q, Zhang M and She J: MicroRNAs in bladder cancer: Expression profiles, biological functions, regulation, and clinical implications. Crit Rev Eukaryot Gene Expr. 24:55–75. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Wszolek MF, Rieger-Christ KM, Kenney PA, Gould JJ, Neto Silva B, Lavoie AK, Logvinenko T, Libertino JA and Summerhayes IC: A MicroRNA expression profile defining the invasive bladder tumor phenotype. Urol Oncol. 29:794–801.e1. 2011. View Article : Google Scholar : PubMed/NCBI

7 

Soini Y, Haapasaari KM, Vaarala MH, Turpeenniemi-Hujanen T, Kärjä V and Karihtala P: 8-hydroxydeguanosine and nitrotyrosine are prognostic factors in urinary bladder carcinoma. Int J Clin Exp Pathol. 4:267–275. 2011.PubMed/NCBI

8 

Cai J, Fang L, Huang Y, Li R, Yuan J, Yang Y, Zhu X, Chen B, Wu J and Li M: miR-205 targets PTEN and PHLPP2 to augment AKT signaling and drive malignant phenotypes in non-small cell lung cancer. Cancer Res. 73:5402–5415. 2013. View Article : Google Scholar : PubMed/NCBI

9 

Cathomas R, Rothermundt C, Klingbiel D, Bubendorf L, Jaggi R, Betticher DC, Brauchli P, Cotting D, Droege C, Winterhalder R, et al: Efficacy of cetuximab in metastatic castration-resistant prostate cancer might depend on EGFR and PTEN expression: Results from a phase II trial (SAKK 08/07). Clin Cancer Res. 18:6049–6057. 2012. View Article : Google Scholar : PubMed/NCBI

10 

Thogersen VB, Sørensen BS, Poulsen SS, Orntoft TF, Wolf H and Nexo E: A subclass of HER1 ligands are prognostic markers for survival in bladder cancer patients. Cancer Res. 61:6227–6233. 2001.PubMed/NCBI

11 

Adam L, Zhong M, Choi W, Qi W, Nicoloso M, Arora A, Calin G, Wang H, Siefker-Radtke A, McConkey D, et al: miR-200 expression regulates epithelial-to-mesenchymal transition in bladder cancer cells and reverses resistance to epidermal growth factor receptor therapy. Clin Cancer Res. 15:5060–5072. 2009. View Article : Google Scholar : PubMed/NCBI

12 

Yun SJ, Jeong P, Kim WT, Kim TH, Lee YS, Song PH, Choi YH, Kim IY, Moon SK and Kim WJ: Cell-free microRNAs in urine as diagnostic and prognostic biomarkers of bladder cancer. Int J Oncol. 41:1871–1878. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Trapnell C, Pachter L and Salzberg SL: TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics. 25:1105–1111. 2009. View Article : Google Scholar : PubMed/NCBI

14 

Diez D: Survival analysis in R. 2012.https://folk.ntnu.no/bo/TMA4275/Download/R.tutorialDiez.pdfAugust 11–2014

15 

Therneau TM and Grambsch PM: Modeling survival data: Extending the Cox model. Springer-Verlag; New York, NY: 2000

16 

Xiao F, Zuo Z, Cai G, Kang S, Gao X and Li T: miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res. 37:D105–D110. 2009. View Article : Google Scholar : PubMed/NCBI

17 

Dweep H, Sticht C, Pandey P and Gretz N: miRWalk-database: Prediction of possible miRNA binding sites by ‘walking’ the genes of three genomes. J Biomed Inform. 44:839–847. 2011. View Article : Google Scholar : PubMed/NCBI

18 

Enright AJ, John B, Gaul U, Tuschl T, Sander C and Marks DS: MicroRNA targets in Drosophila. Genome Biol. 5:R12003. View Article : Google Scholar : PubMed/NCBI

19 

Wang X and El Naqa IM: Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 24:325–332. 2008. View Article : Google Scholar : PubMed/NCBI

20 

Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M and Rajewsky N: Combinatorial microRNA target predictions. Nat Genet. 37:495–500. 2005. View Article : Google Scholar : PubMed/NCBI

21 

Kertesz M, Iovino N, Unnerstall U, Gaul U and Segal E: The role of site accessibility in microRNA target recognition. Nat Genet. 39:1278–1284. 2007. View Article : Google Scholar : PubMed/NCBI

22 

Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP and Burge CB: Prediction of mammalian microRNA targets. Cell. 115:787–798. 2003. View Article : Google Scholar : PubMed/NCBI

23 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

24 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

25 

Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:D808–D815. 2013. View Article : Google Scholar : PubMed/NCBI

26 

Toivonen R, Onnela JP, Saramäki J, Hyvönen J and Kaski K: A model for social networks. Physica A: Statistical Mechanics and its. Applications. 371:851–860. 2006.

27 

Li C and Li MC: Package ‘iSubpathwayMiner’. 2013.http://ftp.cs.pu.edu.tw/network/CRAN/web/packages/iSubpathwayMiner/iSubpathwayMiner.pdfAugust 11–2014

28 

Schmidbauer J, Witjes F, Schmeller N, Donat R, Susani M and Marberger M: Hexvix PCB301/01 Study Group: Improved detection of urothelial carcinoma in situ with hexaminolevulinate fluorescence cystoscopy. J Urol. 171:135–138. 2004. View Article : Google Scholar : PubMed/NCBI

29 

Iorio MV and Croce CM: MicroRNA dysregulation in cancer: Diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med. 4:143–159. 2012. View Article : Google Scholar : PubMed/NCBI

30 

Yan XJ, Xu J, Gu ZH, Pan CM, Lu G, Shen Y, Shi JY, Zhu YM, Tang L, Zhang XW, et al: Exome sequencing identifies somatic mutations of DNA methyltransferase gene DNMT3A in acute monocytic leukemia. Nat Genet. 43:309–315. 2011. View Article : Google Scholar : PubMed/NCBI

31 

Fabbri M, Garzon R, Cimmino A, Liu Z, Zanesi N, Callegari E, Liu S, Alder H, Costinean S, Fernandez-Cymering C, et al: MicroRNA-29 family reverts aberrant methylation in lung cancer by targeting DNA methyltransferases 3A and 3B. Proc Natl Acad Sci USA. 104:pp. 15805–15810. 2007, View Article : Google Scholar : PubMed/NCBI

32 

van Rooij E, Sutherland LB, Thatcher JE, DiMaio JM, Naseem RH, Marshall WS, Hill JA and Olson EN: Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis. Proc Natl Acad Sci USA. 105:pp. 13027–13032. 2008, View Article : Google Scholar : PubMed/NCBI

33 

Pradhan S, Bacolla A, Wells RD and Roberts RJ: Recombinant human DNA (cytosine-5) methyltransferase. I. Expression, purification, and comparison of de novo and maintenance methylation. J Biol Chem. 274:33002–33010. 1999. View Article : Google Scholar : PubMed/NCBI

34 

Gowher H and Jeltsch A: Enzymatic properties of recombinant Dnmt3a DNA methyltransferase from mouse: The enzyme modifies DNA in a non-processive manner and also methylates non-CpG [correction of non-CpA] sites. J Mol Biol. 309:1201–1208. 2001. View Article : Google Scholar : PubMed/NCBI

35 

Mato JM, Corrales FJ, Lu SC and Avila MA: S-Adenosylmethionine: A control switch that regulates liver function. FASEB J. 16:15–26. 2002. View Article : Google Scholar : PubMed/NCBI

36 

Roje S: S-Adenosyl-L-methionine: Beyond the universal methyl group donor. Phytochemistry. 67:1686–1698. 2006. View Article : Google Scholar : PubMed/NCBI

37 

Main PA, Angley MT, Thomas P, O'Doherty CE and Fenech M: Folate and methionine metabolism in autism: A systematic review. Am J Clin Nutr. 91:1598–1620. 2010. View Article : Google Scholar : PubMed/NCBI

38 

Ioachim E, Michael MC, Salmas M, Damala K, Tsanou E, Michael MM, Malamou-Mitsi V and Stavropoulos NE: Thrombospondin-1 expression in urothelial carcinoma: Prognostic significance and association with p53 alterations, tumour angiogenesis and extracellular matrix components. BMC Cancer. 6:1402006. View Article : Google Scholar : PubMed/NCBI

39 

Chakraborty A, White SM and Lerner SP: Granulocyte colony-stimulating factor receptor signals for beta1-integrin expression and adhesion in bladder cancer. Urology. 63:177–183. 2004. View Article : Google Scholar : PubMed/NCBI

40 

Lopez-Beltran A and Cheng L: Histologic variants of urothelial carcinoma: Differential diagnosis and clinical implications. Hum Pathol. 37:1371–1388. 2006. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

December-2017
Volume 14 Issue 6

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Gao J, Li H, Liu L, Song L, Lv Y and Han Y: Identification and functional analysis of risk‑related microRNAs for the prognosis of patients with bladder urothelial carcinoma. Oncol Lett 14: 7297-7303, 2017.
APA
Gao, J., Li, H., Liu, L., Song, L., Lv, Y., & Han, Y. (2017). Identification and functional analysis of risk‑related microRNAs for the prognosis of patients with bladder urothelial carcinoma. Oncology Letters, 14, 7297-7303. https://doi.org/10.3892/ol.2017.7124
MLA
Gao, J., Li, H., Liu, L., Song, L., Lv, Y., Han, Y."Identification and functional analysis of risk‑related microRNAs for the prognosis of patients with bladder urothelial carcinoma". Oncology Letters 14.6 (2017): 7297-7303.
Chicago
Gao, J., Li, H., Liu, L., Song, L., Lv, Y., Han, Y."Identification and functional analysis of risk‑related microRNAs for the prognosis of patients with bladder urothelial carcinoma". Oncology Letters 14, no. 6 (2017): 7297-7303. https://doi.org/10.3892/ol.2017.7124