Abstract

Background

   The current study aimed to explore the association between two
   epigenomic components, miRNA and DNA methylation, in bladder cancer
   (BC).

Methods

   Eight paired samples of tumor tissue and matched adjacent normal
   tissues from BC patients were subjected to methylated DNA
   immunoprecipitation sequencing and sRNA-Seq for differentially
   methylated miRNA genes and differential miRNA analysis. The miRNAs
   regulated by DNA methylation were screened and their functions involved
   in BC were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia
   of Genes and Genomes (KEGG) as well as a miRNA–mRNA interaction
   network.

Results

   The methylation levels of 212 genes were different between tumors and
   normal tissues with specific enrichment at transcription initiation and
   termination sites. Among these genes, 154 were hypermethylated and 58
   were hypomethylated. GO and KEGG pathway enrichment analysis indicated
   that differentially methylated miRNA genes were mainly enriched in
   tumor-associated GO terms and signaling pathways. Pairwise statistical
   analysis of MeDIP-Seq and sRNA-Seq data showed that there are 154 and
   165 candidate methylation-regulated genes in tumors and normal tissues,
   respectively. Notably, an interaction network indicated that the miRNAs
   regulated by methylation regulated a broad range of mRNAs associated
   with cancer development and progression. In particular, the most
   differentially expressed miRNAs were validated by qRT-PCR, such that
   miR-145-5p was downregulated and miR-182-5p was upregulated in patients
   with bladder cancer.

Conclusion

   A large number of miRNA genes were modified by methylation in BC.
   Identification of changes in the expression of these miRNAs provides a
   great deal of important information for BC diagnosis.

   Keywords: DNA methylation, 5-methylcytidine, miRNA, bladder cancer

Introduction

   Bladder cancer (BC) is a highly prevalent disease of the urinary system
   in populations worldwide and is ranked as the fifth most common cancer
   in Western countries.[44]^1 In China, the incidence of BC is moderate
   though it increased year by year between 1998 and 2008 with an average
   annual growth rate of 4.6%.[45]^2 There are multiple risk factors that
   contribute to the etiology of BC, but smoking and urogenital
   schistosomiasis are believed to be two of the major risk
   factors.[46]^3^–[47]^5 Recently studies have indicated that epigenetic
   changes including DNA methylation, histone modification, and microRNA
   (miRNA) regulation are associated with an increased risk of BC.[48]^6

   Increasing evidence has demonstrated that aberrations in methylation
   affect tumor development and progression in BC.[49]^7^–[50]^9
   Methylation of cytosine is a type of genetic annotation which regulates
   how the genetic code is read and when gene information is
   transcribed.[51]^10 Methylation occurring at CpG sites generally leads
   to gene suppression or even silencing. Specifically, CpG
   hypermethylation in promoter regions is associated with a reduction in
   mRNA levels while CpG methylation in gene body regions often activates
   transcription.[52]^9 Changes in DNA methylation resulting in the
   silencing of tumor suppressor genes can promote cancer onset and
   development.[53]^11

   miRNA is a type of endogenous non-coding RNA that regulates gene
   expression by inducing direct mRNA degradation or suppressing
   translation, resulting in abnormal cell behaviors such as cell cycle
   arrest, apoptosis, proliferation, metastasis, and drug resistance in
   several cancers inluding BC.[54]^12^–[55]^17 Evidence has been proven
   that epigenetically regulated miRNAs are another important component of
   the epigenome and are widely distributed in multiple types of cancers
   exerting effects on tumor development and progression.[56]^18^,[57]^19
   Treatment with methylation inhibitors such as aza-2ʹ-deoxycytidine and
   4-phenylbutyric acid can activate the expression of miRNAs and enhance
   their function in tumorigenesis.[58]^20^,[59]^21

   The expression of miRNA can be altered if the methylation occurs in a
   miRNA-encoding gene.[60]^22 Several miRNAs regulated by methylation
   have been identified in BC and suggested to be associated with the
   onset and progression of the cancer.[61]^14^,[62]^23^,[63]^24 However,
   there are still few comprehensively analyses of the relationship
   between miRNA expression and DNA methylation in BC. In the present
   study, we screened interactions between these two epigenomic components
   by high-throughput deep sequencing and methylated DNA
   immunoprecipitation sequencing (MeDIP-Seq) with eight paired samples of
   BC tissue and matched normal adjacent tissues. Further, we suggest a
   potential regulatory network of methylated miRNA.

Materials and methods

MeDIP-Seq

   Following the manufacturer’s instructions, genomic DNA from eight
   tumors and matched normal adjacent tissues were isolated using a
   MagMedIP Kit (Diagenode, Denville, NJ, USA). Next, the methylated DNA
   was separated from the unmethylated fragments by immunoprecipitation
   using a monoclonal antibody against 5-methylcytidine (Diagenode,
   Denville, NJ, USA) as previously described.[64]^25 Illumina sequencing
   libraries of the enriched methylated DNA were prepared using NEBNext
   reagents according to the manufacturer’s recommendations (New England
   Biolabs, MA, USA). After quantitative evaluation of size distribution
   and concentration using an Agilent Bioanalyzer 2100 (Agilent
   Technologies, CA, USA), reads of 100 nucleotides (nt) were sequenced on
   an Illumina HiSeq 2000 platform (Illumina, CA, USA). All of the samples
   were obtained with written informed consent and analyzed anonymously.
   This study was approved by the Medical Ethical Committee of Affiliated
   Haikou Hospital of Xiangya Medical College, Central South University.

Methylation profiling and differentially methylated gene analysis

   The MeDIP-Seq raw data from the Illumina HiSeq 2000 platform were
   processed according to the standard procedure of the BGI Group
   (Shenzhen, China). Reads were aligned to a human reference genome
   (hg19) using the Bowtie alignment algorithm with default parameters.
   Using a MACS software (MACS 1.4.0), a whole-genome methylation peak
   scan was performed and sequencing results for tumors and normal tissues
   were converted into peak values. Distribution trends for peak values
   across gene bodies and regions 2 kb directly upstream and downstream of
   the gene bodies were deduced. Genes with a fold change ≥1.5 and P<0.05
   were defined as differentially methylated. Differentially methylated
   genes were further subjected to unsupervised hierarchical cluster
   analysis to obtain an overview of the differentially methylated genes
   in BC tumors. Gene ontology (GO) annotation and the Kyoto Encyclopedia
   of Genes and Genomes (KEGG) pathway analysis function prediction were
   performed using the open-source R package. P-values after correction,
   also termed Q-values were taken as the false discovery rate. GO terms
   or pathways with a value of Q <0.001 were considered significantly
   enriched.

sRNA-Seq and differentially expressed miRNA screening

   Total RNA was extracted from eight tumors and matched adjacent normal
   tissues using TRiZol Reagent (Thermo Fisher Scientific, MA, USA)
   according to the manufacturer’s instructions. RNA quality was evaluated
   using an Agilent Bioanalyzer 2100 (Agilent). A sRNA library was
   constructed using TruSeq Small RNA Sample Preparation Kits (Illumina)
   according to the manufacturer’s protocols. The quality of the library
   was confirmed with an Agilent Bioanalyzer 2100 and sequencing was
   performed on an Illumina HiSeq2000 platform (Illumina).

   For miRNA analysis, low quality, adapter-only, and unclipped reads were
   discarded. Remaining clean reads 15–30 nt in length were mapped to hg19
   using SOAP V2.0. miRNA was identified and annotated using miRBase21
   data ([65]http://www.mirbase.org/ftp.shtml). To compare the expression
   of miRNA between tumors and normal tissues, miRNA reads were normalized
   to miRNA reads per million reads (RPM) and the average RPM levels of
   each miRNA were obtained. miRNA with a fold change ≥2 and a P-value
   <0.01 were considered to be significantly expressed.

miRNA–mRNA interaction network construction

   To construct a miRNA–mRNA interaction network differentially expressed
   miRNAs detected indirectly from the total RNA of the eight paired
   tissue samples were mapped to the differentially methylated genes
   obtained by MeDIP-Seq. Overlapping miRNAs were used to generate a
   miRNA–mRNA interaction network. Candidate miRNA-target mRNA was
   predicted using miRanda and Target-Scan. An interaction network
   illustrating the regulatory relationship between the miRNAs and their
   corresponding mRNAs was drawn using Cytoscape software.

Quantitative real-time polymerase chain reaction (qRT-PCR)

   Differentially expressed miRNAs identified by MeDIP-Seq and sRNA-Seq
   were confirmed by qRT-PCR. Briefly, total RNAs were extracted from
   tissues and reverse transcribed by an miRNA-specific stem-loop RT
   primer according to the manufacturer’s protocol. Each cDNA generated
   was amplified by quantitative PCR using TaqMan^® miRNA assays (Thermo
   Fisher Scientific, MA,USA) using a mature miRNA-specific forward primer
   and the universal reverse primer. The reactions were incubated at 95 °C
   for 120 s, followed by 40 cycles of 95 °C for 15 s, and 60 °C for 30 s.

Results

Differentially methylated miRNA genes in BC patients

   MeDIP-Seq was carried out to identify differences in the methylation of
   miRNA genes between BC tumors and normal tissues. As shown in
   [66]Figure 1A, more than half of the methylated miRNA genes identified
   were found exclusively in one patient, while only a very small number
   were found in five of the eight patients studied ([67]Figure 1A). Among
   these methylated miRNA genes, the number of hypermethylated genes was
   greater than the number of hypomethylated genes in five out of eight
   patients, and only three patients had a higher number of hypomethylated
   miRNA genes ([68]Figure 1B). Analysis of the distribution of DNA
   methylation across gene bodies and regions 2 kb directly upstream and
   downstream of the gene bodies reveled two methylation peaks close to
   the transcription start and termination sites in both tumors and normal
   tissues ([69]Figure 1C). Most of the changes in methylation between
   tumors and normal tissues also occurred in these two regions. To gain
   an overview of the differentially methylated miRNA genes identified, a
   heatmap was generated and distinct patterns of methylation of miRNA
   genes were observed in tumors and normal tissues. As shown in
   [70]Figure 1D, a total of 212 genes were differentially methylated
   between tumors and normal tissues in BC patients. Of these, 154 genes
   were hypermethylated and 58 were hypomethylated in the tumor tissues.
   Higher levels of methylation were observed in tumors tissues than in
   normal controls. In a volcano plot, hyper- and hypomethylated genes
   were distributed in quadrants of fold change >1 and P-value <0.05, with
   larger genes concentrated in the hypermethylated quadrant ([71]Figure
   1E).

Figure 1.

   [72]Figure 1
   [73]Open in a new tab

   Determination of DNA methylation by MeDIP-Seq analysis. (A)
   Distribution of deferentially methylated genes encoding miRNAs in eight
   patients. (B) Number of hypo- and hypermethylated genes in eight
   patients. (C) Distribution of reads around gene bodies ±2 kb flanking
   regions. Upstream 2 kb, region 2 kb upstream of the transcription start
   site 2K; intragenic, the entire gene from the transcription start site
   to the end of the transcript; downstream 2 kb, 2 kb downstream of the
   end of the transcript. (D) Heatmap showing deferentially methylated
   miRNA-encoding genes. Each gene corresponds to each row. (E) Volcano
   plot showing the differentially expressed reads in tumors and normal
   tissues. Red points represents upregulated reads, green points
   represent downregulated reads, black points are not statistically
   significant.

Functional prediction for differential methylated genes

   Hyper- and hypomethylated genes encoding miRNAs were further subjected
   to GO and KEGG pathway enrichment analysis for function prediction.
   KEGG analysis showed that differentially methylated genes were mainly
   enriched in tumor-associated pathways such as the PI3K-Akt signaling
   pathway, the MAPK signaling pathway, the Ras signaling pathway, focal
   adhesion, and miRNAs and proteoglycans associated with cancer
   ([74]Figure 2A). GO term analysis also determined that most genes were
   enriched in biological processes, followed by cellular components, with
   the most enrichment in single-organism cellular processes such as the
   regulation of nitrogen compound metabolism, cellular and cellular
   metabolic processes, and intracellular organelles ([75]Figure 2B).

Figure 2.

   [76]Figure 2
   [77]Open in a new tab

   KEGG pathway enrichment and GO analysis of the differentially
   methylated miRNA genes. (A) Top 20 most enriched signaling pathways.
   Dot size reflects the number of genes enriched in each signaling
   pathway. Dot color indicates P-value. (B) GO enrichment analysis of all
   differentially methylated genes.

Differential expression of miRNAs in BC tumors are associated with
methylation of miRNA-encoding genes

   To investigate the association between miRNA expression and the
   methylation of miRNA-encoding genes in tumor tissues, miRNA expression
   profiles were detected by high throughput sequencing for eight paired
   tumors and normal tissues. As shown in [78]Figure 3A, cluster analysis
   revealed distinct profiles of miRNA expression between tumors and
   matched normal tissues. There were 83 upregulated and 59 downregulated
   miRNAs in the tumor tissues relative to the normal tissues ([79]Figure
   3A). A volcano plot also showed a large number of miRNAs that were up-
   or downregulated by more than 2-fold in tumor tissues ([80]Figure 3B).
   Differentially expressed miRNAs were classified into four categories
   according to the region of methylation in their corresponding genes:
   both gene body and promoter modified; neither modified; only gene body
   modified; or only promoter modified. The average expression of miRNAs
   in each category was calculated. As shown in [81]Figure 3C, there was
   no significant difference in these four groups of miRNAs between tumors
   and normal tissues. However, the average expression of unmodified
   miRNAs was higher, and that of miRNAs with methylation in the promoter
   region, gene body region, or both was lower ([82]Figure 3C). To
   determine whether DNA methylation in miRNA-encoding genes was
   associated with the expression of miRNAs, methylated miRNA genes
   detected by MeDIP-Seq and miRNAs obtained by sRNA-Seq were merged. A
   total of 154 and 165 overlapping genes were identified in tumors and
   normal tissues, respectively ([83]Figure 3D and [84]E).

Figure 3.

   [85]Figure 3
   [86]Open in a new tab

   Differentially expressed miRNAs in BC tumor tissues are associated with
   methylation of miRNA-encoding genes. (A) Heatmap showing differential
   miRNA expression profiles in bladder cancer relative to normal tissues.
   (B) Volcano plot showing the differentially expressed miRNAs in tumor
   tissues compared with normal tissues. Red points represents
   up-regulated miRNA, green points represent downregulated miRNAs, black
   points are not statistically significant. (C) Expression levels of
   miRNAs corresponding to four genomic regions with different methylation
   modification levels: both gene body and promoter modified, neither
   modified, only gene body modified, only promoter modified. (D) Overlap
   of miRNA-encoding genes identified by MeDIP-Seq and sRNA-Seq in tumor
   tissues. (E) Overlap of miRNA-encoding genes identified by MeDIP-Seq
   and sRNA-Seq in normal tissues.

Differentially expressed miRNAs regulated by methylation regulated a large
number of mRNAs

   Generally, miRNAs regulate gene expression by targeting the 3ʹUTR of
   mRNA. Changes in the expression of miRNA in tumor tissues may therefore
   affect a number of mRNAs. To generate a miRNA–mRNA interaction network,
   candidate mRNA targets of differentially expressed miRNAs that
   overlapped according to MeDIP-Seq and sRNA-Seq data were predicted.
   Target prediction revealed that numerous mRNAs were potentially
   regulated by these miRNAs. A miRNA–mRNA interaction network which
   contained four subsets and included 12 miRNAs and 278 mRNAs was
   constructed. The largest regulatory subnetwork was that of miR-145,
   which included 143 mRNAs, accounting for more than 50% of mRNAs in the
   entire network ([87]Figure 4).

Figure 4.

   [88]Figure 4
   [89]Open in a new tab

   miRNA–mRNA regulatory network generated using miRNAs of differential
   methylated genes and mRNAs which containing predicted miRNA targets.
   Green triangle indicates miRNAs, pink oval represents mRNAs.

   To further validate the MeDIP-Seq and sRNA-Seq results, RT-PCR was used
   to evaluate two up-regulated (miR-1266-5p and miR-182-5p) and three
   down-regulated (miR-145-5p, miR-126-3p, and miR-152-3p) miRNAs in eight
   paired tumors and normal tissues. qRT-PCR results showed that
   miR-1266-5p and miR-182-5p were up-regulated in the BC tissues compared
   with the normal tissues, whereas miR-145-5p was down-regulated in the
   tumor tissues ([90]Figure 5), which was consistent with the results of
   sRNA-Seq. However, miR-126-3p and miR-152-3p were up-regulated in the
   BC tissues, and qRT-PCR results were inconsistent with the sRNA-Seq
   results.

Figure 5.

   [91]Figure 5
   [92]Open in a new tab

   Validation of up-regulated [miR-1266-5p (A) and miR-182-5p (B)] and
   down-regulated [miR-145-5p (C), miR-126-3p (D), and miR-152-3p (E)]
   miRNAs by qRT-PCR.

Discussion

   DNA methylation and miRNAs are two important regulatory factors in
   human cancers. In the present study, we identified miRNA genes
   regulated by methylation by combining the profiles of methylated
   miRNA-encoding genes with the expression profiles of miRNAs in BC
   patients. Several differentially methylated miRNA-encoding genes were
   identified and predicted to regulate many miRNAs in BC. Further, we
   outlined a regulatory network of these differentially methylated miRNAs
   and highlighted the central role of miR-145 in the methylated
   miRNA–mRNA interaction network.

   Previously, studies have discussed some aspects of the relationship
   between DNA methylation and miRNA in BC. However, previous studies such
   as that of Ma et al have mainly focused on the differences in
   methylation between sensitive and resistant HCC cell lines in response
   to 5-fluorouracil treatment[93]^;19 another study by Shimizu et al
   identified a set of epigenetically silenced miRNA genes in BC patients,
   but this was mainly based on a PCR-based method, which may not cover
   all miRNAs in addition to their counterpart genes information.
   Therefore, previous works may not have covered all possible methylation
   changes in miRNA genes[94]^.24 Unlike these studies, our current study
   provides a genome-wide profile of both DNA methylation state and miRNAs
   expression based on a high-throughput deep sequencing method, which
   means that the data reflects the global changes in methylation state
   and miRNA levels in BC tissues.

   Previous studies have suggested that methylation levels are higher in
   gene bodies than in regions flanking the 5ʹ and 3ʹ ends of genes in
   human cells, bovine placentas, and arabidopsis.[95]^26^–[96]^28 In the
   current study, partially consistent with previous finding, methylation
   levels were slightly increased in the gene bodies compared with the
   flanking regions. However, sharply increased methylation peaks were
   noted in upstream and downstream regions close to the transcription
   start and termination sites. These peaks were particular high in the BC
   tissues relative to normal tissues, suggesting that methylation in
   these regions may contribute to BC etiology.

   Our research shows that methylation does have an effect on miRNA
   expression levels. Both promoter regions and the gene body regions
   exhibited the lowest expression of methylated miRNA genes, while
   unmethylated miRNA had the highest average expression level.
   Furthermore, integrated analysis using MeDIP-Seq and sRNA-Seq data
   revealed that DNA methylation in miRNA gene can partially explain the
   aberrant expression of a proportion of miRNAs in normal and cancer
   tissues. Previously, several miRNAs have been proven to be regulated by
   DNA methylation. For example, miR-193, a miRNA which has been shown to
   be hypermethylated, is downregulated in chemoresistant BC cells.[97]^29
   Treatment with demethylase resulted in changes in the expression of
   miR-137, miR-124–2, miR-124–3, and miR-9–3, which are associated with
   growth and invasion in primary cancers.[98]^24 Methylation of the
   promoters of miR-34a and miR-126 is found in a variety of cancers,
   including BC.[99]^30^,[100]^31 miR-200b, a miRNA associated with
   cisplatin treatment sensitivity, is upregulated after treatment with
   demethylase.[101]^32 Meanwhile, in the muscle-invasive bladder tumors,
   the miR-200b gene is hypermethylated and its expression is specifically
   silenced.[102]^33 Our findings provide more information regarding BC
   associated methylated miRNAs.

   Of these miRNAs, we highlight the tumor suppressor, miR-145, which
   targets the greatest number of mRNAs in the methylated miRNA–mRNA
   interaction network produced in the present study. In particular, we
   confirmed the down-regulation of miR-145 in the bladder tumors with
   RT-PCR. It has been reported that miR-145 regulates tumor progression
   and metastasis in a wide range of cancer.[103]^34^–[104]^37 Lower
   levels of miR-145 induced by DNA methylation have been evidenced to
   enhance cell behaviors such as proliferation, migration and invasion
   and could be used as potential prognostic biomarker for cancer
   therapy.[105]^38^,[106]^39 Removal of the methylation suppresses cancer
   growth and metastasis.[107]^40 In terms of BC, miR-145 has been proven
   to inhibit BC cell growth, cell invasion and migration, and induce
   tumor apopotosis.[108]^41^–[109]^44 These findings suggest that miR-145
   plays an inhibitory role in tumor development and progress and that DNA
   methylation occurs with high frequency in cancer. In support of these
   findings, we observed the downregulation of miR-145 in the present
   study, and our methylated miRNA–mRNA interaction network shows that
   miR-145 regulates the biggest subnetwork of cancer-associated mRNAs,
   including c-Myc, SOX9, SP1, and
   FSCN1.[110]^34^,[111]^36^,[112]^45^,[113]^46 Thus, it will be important
   for us to identify the role of methylated miR-145 in BC in future work.

   Collectively, our data suggest that methylated miRNA genes are involved
   in the pathogenesis of BC and that drugs that regulate the expression
   of methylated miRNA genes, especially those related to cancer may be
   useful in BC therapy. In addition, the differentially methylated miRNAs
   genes identified in the current study may function as biomarkers for
   the early detection of BC.

Acknowledgments