Abstract
Several studies have found that DNA methylation is associated with
transcriptional regulation and affect sponge regulation of non-coding
RNAs in cancer. The integration of circRNA, miRNA, DNA methylation and
gene expression data to identify sponge circRNAs is important for
revealing the role of DNA methylation-mediated regulation of sponge
circRNAs in cancer progression. We established a DNA
methylation-mediated circRNA crosstalk network by integrating gene
expression, DNA methylation and non-coding RNA data of breast cancer in
TCGA. Four modules (26 candidate circRNAs) were mined. Next, 10 DNA
methylation-mediated sponge circRNAs (sp_circRNAs) and five sponge
driver genes (sp_driver genes) in breast cancer were identified in the
CMD network using a computational process. Among the identified genes,
ERBB2 was associated with six sponge circRNAs, which illustrates its
better sponge regulatory function. Survival analysis showed that DNA
methylations of 10 sponge circRNA host genes are potential prognostic
biomarkers in the TCGA dataset (p = 0.0239) and [41]GSE78754 dataset (p
= 0.0377). In addition, the DNA methylation of two sponge circRNA host
genes showed a significant negative correlation with their driver gene
expressions. We developed a strategy to predict sponge circRNAs by DNA
methylation mediated with playing the role of regulating breast cancer
sponge driver genes.
Keywords: circular RNA, DNA methylation, miRNA sponge, breast cancer,
network
Introduction
Breast cancer is one of the most common malignant tumors in women
(Koboldt et al., [42]2012). The HER2 (encoded by ERBB2) amplification
group has achieved great clinical success because HER2 is an effective
therapeutic target for breast cancer (Slamon et al., [43]1987).
However, the current targeted therapy for breast cancer has not been
fully and effectively implemented (Ayca and Traina, [44]2011). Studies
have shown that dysregulated gene expression in breast cancer is
affected by various genetic and epigenetic factors, including frequent
somatic mutations (Nik-Zainal et al., [45]2016), copy number variations
(Nik-Zainal et al., [46]2016), single nucleotide polymorphisms (SNPs)
(Michailidou et al., [47]2017), non-coding RNA (Iorio et al.,
[48]2015), and DNA methylation (Yang et al., [49]2001). Abnormal
regulation of epigenetic modifications leads to aberrant gene
expression and function (Berger et al., [50]2009; Pan et al.,
[51]2017). Epigenetic modifications thus play an important role in the
development and progression of cancer (Esteller, [52]2008).
DNA methylation is a common epigenetic modification that regulates gene
expression at the transcriptional level (Lindahl, [53]1981; Bird,
[54]1984; Baylin and Jones, [55]2011; Wise and Charchar, [56]2016).
Abnormal DNA methylation, including global hypomethylation and aberrant
methylation status on key regulatory elements, has been observed in
multiple human cancers (Trimarchi et al., [57]2011; Aran et al.,
[58]2013). Many studies have found significant differences in DNA
methylation between breast cancer samples and normal samples. In
general, cancer cells show hypomethylation of the entire genome, while
promoter sequences of tumor suppressor genes show hypermethylation.
Hypomethylation of the whole genome is caused by genomic instability
and loss of gene imprinting, which causes overexpression of oncogenes
(Trimarchi et al., [59]2011). In contrast, hypermethylation of tumor
suppressor gene promoters causes genetic silencing of tumor suppressor
genes (Bird, [60]1996; Aran et al., [61]2013). The hypomethylation of
the gene promoter CpG island promotes its downstream gene
transcription, whereas its hypermethylation inhibits the transcription
of the downstream genes (Deaton and Bird, [62]2011).
Circular RNAs (circRNAs) form a covalently linked continuous loop and
lack 5′ and 3′ ends and polyadenylation tail structures (Diener,
[63]1989; Chen and Yang, [64]2015; Song et al., [65]2016). CircRNAs
exhibit important effects on RNA-related protein binding, gene splicing
regulation and transcription, as well as the modification of parental
gene expression (Li et al., [66]2015; Peng et al., [67]2017). Recent
studies have shown that circRNAs are more stable than their linear
counterparts and some function as biological markers for disease
treatment (Weil et al., [68]2015; Zhang et al., [69]2015; Abu and
Jamal, [70]2016; Kulcheski et al., [71]2016). Many studies have shown
that circRNAs can also function as miRNA sponges in the regulation of
gene expression (Hansen et al., [72]2013; Jens, [73]2014; Qiang et al.,
[74]2016). It can be involved in the development of a variety of
diseases, such as atherosclerotic disease, prion disease, and myotonic
dystrophy (Qu et al., [75]2015). For example, the circular transcript
CDR1as (or ciRS-7), which is encoded by the reverse genome of the human
CDR1 gene in human and mouse brain tissues, functions as an miRNA
sponge to miR-7(Hansen et al., [76]2011). ciRS-7 inhibits miR-7
activity and competes with miR-7 in binding to other RNAs to modulate
target gene expression (Hansen et al., [77]2013; Peng et al.,
[78]2015). Several studies have demonstrated that circRNAs also exhibit
functions in cancer. For instance, circ-Amotl1 is highly expressed in
breast cancer samples and many cancer cell lines (Yang et al.,
[79]2017). Interactions between circ-Amotl1 and c-myc lead to increased
tumorigenicity (Yang et al., [80]2017). The potential clinical value of
circRNAs in cancer diagnosis, therapy and prognosis has been the
subject of recent investigation (Abu and Jamal, [81]2016; Kulcheski et
al., [82]2016; Yang et al., [83]2017).
circRNAs are weakly expressed and have a long half-life (Enuka et al.,
[84]2016). The detection process of circRNA expression is technically
challenging (Enuka et al., [85]2016). Here we integrated genome,
epigenome and non-coding RNA data of breast cancer samples and used a
bioinformatics approach to identify potential important sponge circRNAs
in breast cancer. This study demonstrates the important role of circRNA
sponge regulation associated with DNA methylation in breast cancer,
which suggests a therapeutic strategy for manipulating the driver gene
function in breast cancer through circRNA sponge regulation.
Materials and Methods
Data Sources
miRNASeq data (1,102 breast cancer samples, 104 normal samples),
Illumina Infinium Human Methylation 450 BeadChip level 3 data (684
cancer samples, 96 normal samples), and RNASeqV2 data (1,102 breast
cancer samples, 113 normal samples) were downloaded from The Cancer
Genome Atlas (TCGA) ([86]https://gdc.cancer.gov/). Clinical data were
downloaded from TCGA ([87]https://gdc.cancer.gov/) and [88]GSE78754.
Among them, DNA methylation and gene expression matching data were
found in 781 cancer samples and 84 normal samples. CircRNA-miRNA
interaction data were downloaded from the Starbase v2.0
([89]http://starbase.sysu.edu.cn/). MiRNA-target gene interaction data
were downloaded from miRTarBase
([90]http://mirtarbase.mbc.nctu.edu.tw/), which stores experimentally
verified miRNA-target gene interaction information. CircRNA location
information was obtained from Circbase ([91]http://www.circbase.org/).
The protein coding driver gene list was derived from Vogelstein et al.
(Vogelstein et al., [92]2013). The reference genome (hg19) was obtained
from UCSC ([93]http://genome.ucsc.edu/).
Data Preprocessing
We integrated miRNASeq data from 1,102 breast cancer samples and 104
normal samples to construct miRNA expression profiles between breast
cancer samples and normal samples. If there was a missing value for
miRNA expression, the miRNA was removed.
We used Illumina Infinium Human Methylation 450 BeadChip data from 684
breast cancer samples and 96 normal samples and circRNA position
information to construct the DNA methylation profile of circRNA host
genes. We performed data preprocessing to remove probes containing
missing values in the sample and probes for multiple genes, CpG sites
on the sex chromosomes and SNPs. Previous studies showed that multiple
adjacent CpG sites have the same DNA methylation pattern
(Lehmann-Werman et al., [94]2016); if a circRNA host gene contains
multiple CG loci, the DNA methylation level of the circRNA host gene is
quantified as the average methylation level of these CG loci.
We used the same samples to construct the DNA methylation profile of
target genes interacting with differentially expressed miRNAs. We
performed data preprocessing to remove probes containing missing values
in the sample and probes for multiple genes, CpG sites on the sex
chromosomes and SNPs. The DNA methylation of target gene was quantified
as the average methylation level of CG loci located in the target gene.
We use RNASeqV2 data of breast cancer samples and normal samples to
construct target gene expression profile interacting with
differentially expressed miRNAs. We removed target genes with missing
values.
Establishment of the DNA Methylation-Mediated circRNA Crosstalk (DMCC)
Network and Identification of Candidate circRNAs
First, differentially expressed miRNAs (fold change > 2 or fold change
< 0.5, q value < 1) were screened between breast cancer samples and
normal samples using SAMR package. CircRNAs that interacted with
differentially expressed miRNAs were matched. If two circRNAs shared
the same miRNA (two circRNAs have sequence and function similarity),
then both circRNAs form a connection. A circRNA crosstalk (CC) network
was constructed.
In addition, the DNA methylation profile of circRNA host genes in the
CC network was constructed according to the data preprocessing
description. Differential methylation circRNA host genes between breast
cancer samples and normal samples were screened by SAMR package (fold
change > 1.5 or fold change < 2/3, q value < 5), and Pearson
correlation of DNA methylation of two circRNA host genes was
calculated. If the Pearson correlation of two circRNA host genes was
greater than random (permutation > 1,000) even >0.6, these two circRNA
host genes will be two nodes of one edge in the CC network. The DMCC
network was constructed with Pearson correlation as the weight. The
circRNA host genes are co-methylated in the DMCC network.
Module-mining was performed in the DMCC network by using MCODE plugin
in the Cytoscape, and circRNAs in the modules were selected as the
candidate circRNAs.
miRNA Target Prediction and circRNA-miRNA-Driver (CMD) Gene Network
Establishment
All human miRNAs and target information were derived from the
miRTarBase database, which stores the experimental validated miRNA
targets and is prevalent in the target prediction. We used SAMR package
to screen differentially expressed genes between breast cancer samples
and normal samples (fold change > 2 or fold change < 0.5, q value < 1).
We obtained breast cancer driver genes by taking the intersection of
breast cancer differentially expressed genes and cancer driver genes.
We used candidate circRNAs, breast cancer driver genes and all shared
miRNAs to construct the CMD network. Each pair of circRNA-driver gene
in this network is a candidate circRNA-driver gene pair. All
implementations of network diagrams and module mining are realized by
Cytoscape.
Predicting the Sponge circRNAs of Protein-Coding Driver Genes
Studies have shown that circRNAs are almost co-expressed with their
linear transcripts (Enuka et al., [95]2016). Therefore, DNA methylation
of the circRNA host genes may regulate the expression of circRNAs,
which influences the competitive regulation of circRNAs as miRNA
sponge. Similarly, DNA methylation of breast cancer driver genes can
also regulate the expression of driver genes. Sponge circRNAs
positively regulate the expression of their targets, and their
regulation depends on the miRNA stoichiometry (Du et al., [96]2016).
Therefore, we used the following hypothesis to predict sponge
circRNA-driver gene pairs: sponge circRNA host genes and target driver
genes are co-methylated and they interact with the same miRNAs.
A complete computational analysis process was designed to predict
sponge circRNA-driver gene pairs. First, for each candidate
circRNA-driver gene pair, the significance (P[1]: P-value of Fisher's
exact test) of shared miRNA with same seeds and the significance (P[2]:
P-value of Pearson's correlation coefficient test) of the DNA
methylation correlation between a circRNA host gene and driver gene
promoter region (−1.5 to +0.5 kb relative to transcription start site)
in breast cancer samples were calculated. We computed P[1] using
formula (1):
[MATH: P1=Ca+baCc+dc
Cna+c
=(a+b)!(c+d)!(a+c)!(b+d)!a!b!c!d!n! :MATH]
(1)
where a is the number of shared miRNAs in a candidate circRNA-driver
gene pair; b is the number of shared miRNAs in a circRNA-other driver
genes; c is the number of shared miRNAs in a driver gene-other
circRNAs; d is the number of remaining miRNAs; and n represents the
number of total miRNAs.
We computed P[2] using formula (2):
[MATH: P2=P(r) :MATH]
(2)
with
[MATH: r=1n-1∑i=1n(X
i-X¯s<
mrow>X)(Y
i-Y¯s<
mrow>Y) :MATH]
(3)
where X is the DNA methylation profile of circRNA host genes in breast
samples, and Y is the DNA methylation profile of driver genes in breast
cancer samples.
The following conditions were fulfilled for the candidate
circRNA-driver gene pairs as predicted sponge circRNA-driver gene
pairs: (1) P[1] and P[2] are no larger than a threshold of 0.05 (P[1] ≤
0.05 and P[2] ≤ 0.05); (2) the Pearson correlation of DNA methylation
between circRNA host genes and driver gene promoter region shows a
positive correlation and greater than random (permutation 1000 times, p
< 0.05); (3) circRNA-driver gene pairs that shared at least eight
unique targeting sites for these shared miRNAs (different miRNAs
sharing the same seed sequence could target the same site) are selected
(Du et al., [97]2016); and (4) there was a significant negative
correlation between DNA methylation of driver gene promoter region and
the expression of driver genes (p < 0.05).
Functional Enrichment Analysis of circRNA Host Genes Associated With DNA
Methylation
The circRNA host genes in the DMCC network are associated with DNA
methylation and are co-methylated. We next analyzed the function of
circRNA host genes in the DMCC network. Functional and pathway
enrichment of the DNA methylation-mediated circRNA host genes was
achieved using DAVID ([98]https://david.ncifcrf.gov/). Rich factor is
the ratio of the number of circRNA host genes mapped to this GO term
and the number of annotated genes in this term; the higher rich factor
means the more significant enrichment. P-value was corrected by
Benjamini. Enrichment analysis. The graph was performed using the
ggplot2 package in R 3.2.1.
Survival Analysis
The clinical survival data for breast cancer were downloaded from TCGA
and GEO ([99]GSE78754). Survival data of breast cancer patients were
integrated (missing data were removed). Finally, we obtained 656 breast
cancer samples in TCGA and 70 breast cancer samples in [100]GSE78754.
We assessed the predictive effect of sponge circRNAs on overall
survival in breast cancer by survival analysis. Survival analysis was
performed using Cox proportional hazards model. DNA methylation of the
sponge circRNA host gene was used as a covariate to assess the
independent contribution of each circRNA host gene to prognosis. The
DNA methylation value of the circRNA host gene was multiplied by the
linear sum of the regression coefficients in the multivariate Cox
regression to assign a prognostic index (PI). PI for each patient was
calculated as shown in formula 4.
[MATH: PI=β<
mrow>1x1+β1x1+…+βnxn :MATH]
(4)
where x[i] represents DNA methylation of circRNA host gene i; β[i]
represents regression coefficient of gene i in multivariate Cox
regression; and n represents the number of genes. The median of PI was
used as a threshold to classify breast cancer patients into the high
risk group and low risk group. We used R V3.2.1 for the survival
analysis and generating survival plot.
Results
Differential Target Gene Expression and DNA Methylation of circRNA Host Genes
and Target Genes in Breast Cancer
miRNAs are abundantly present in many organisms and can selectively
interact with complementary mRNAs to reduce protein production. We
identified 192 differentially expressed miRNAs between breast cancer
samples and normal samples in TCGA datasets. To determine potential
targets of these miRNAs, we next used miRTarBase, which revealed 4,546
potential target genes of the differentially expressed miRNAs. Notably,
the target genes also showed differential expression between breast
cancer and normal samples and target gene expression can separate
breast cancer samples from normal samples ([101]Figure 1A). This
suggests that these differentially expressed miRNAs in breast cancer
may be targeting specific mRNAs to regulate target gene expression,
leading to the differential gene expression in breast cancer samples.
Figure 1.
[102]Figure 1
[103]Open in a new tab
Feature display of target genes and circRNAs that interact with
differentially expressed miRNAs. (A) The target genes expression of
differential expression miRNAs between breast cancer samples and normal
samples. Gene expression values were log2 transformed after adding a
pseudo-value of 1 to avoid infinite values. Red indicates breast cancer
samples, blue indicates normal samples. (B) DNA methylation cluster
analysis of target genes bound by differentially expressed miRNAs. Red
represents breast cancer samples, blue represents normal samples. (C)
DNA methylation cluster analysis of circRNA host genes that interact
with differentially expressed miRNAs. Red indicates breast cancer
samples, and blue indicates normal samples.
We next constructed a DNA methylation profile of the 4,546 target genes
between 684 breast cancer samples and 96 normal samples. The DNA
methylation heat map showed that the target genes showed differential
methylation between breast cancer and normal samples, with higher DNA
methylation in breast cancer compared with normal samples. Furthermore,
DNA methylation of the target genes could separate breast cancer
samples and normal samples ([104]Figure 1B). These findings suggest
that the differential levels of DNA methylation may impact target gene
expression in breast cancer.
circRNA can bind to miRNAs through miRNA response elements (MREs) and
regulate miRNA functions. We found that 747 circRNAs showed potential
interaction with the 192 differentially expressed miRNAs. DNA
methylation of circRNA host genes may affect their miRNA sponge
function and impact miRNA target gene expression. To explore the
potential role of DNA methylation of circRNA host genes, we constructed
a DNA methylation profile of the 747 circRNA host genes and generated a
DNA methylation heat map. The heat map showed that circRNA host genes
showed differential methylation between breast cancer samples and
normal samples, and the DNA methylation of circRNA host genes could
separate breast cancer samples from normal samples ([105]Figure 1C).
These results confirm differential DNA methylation of circRNA host
genes in breast cancer and suggests that methylation of circRNA host
genes may indirectly regulate target gene expression.
Construction of the DMCC Network and Identification of Candidate circRNAs
We next aimed to predict sponge circRNAs from the perspective of
epigenetics regulation. To first identify circRNAs that highly
correlated with DNA methylation, we established a DMCC network. We
screened the 192 miRNAs differentially expressed in breast cancer and
found that they formed 2,419 circRNA-miRNA interactions. A circRNA
crosstalk (CC) network ([106]Supplementary Figure 1) was established by
sharing same miRNAs ([107]Figure 2A), thus two circRNAs have sequence
similarity. The CC network contained 747 circRNAs and 48,492
circRNA-circRNA connections. The circRNA-circRNA network represents the
interactions between circRNAs, in which two circRNAs may have sequence
functional similarities. The maximum degree of the CC network is 938.
The degree distribution of the CC network is subject to the power-law
distribution ([108]Supplementary Figure 2), and therefore the CC
network is a biologically significant network.
Figure 2.
[109]Figure 2
[110]Open in a new tab
Establishment and analysis of DMCC network in the breast cancer. (A)
The construction of the circRNA crosstalk (CC) network. The orange,
green, and blue represent three different circRNAs, and the red and
yellow rectangles are two miRNAs. (B) The DNA methylation mediated
circRNA crosstalk (DMCC) network and candidate circRNAs. The blue nodes
represent circRNAs. (C) Degree distribution of DMCC network. (D) The
function and pathway enrichment analysis of circRNA host genes in the
DMCC network. The abscissa represents the rich factor, and the ordinate
represents the inverse of the logarithm of the P-value corrected by
Benjamini. The larger value represents the significant of GO terms and
pathway.
We next constructed a DNA methylation profile of the 747 circRNA host
genes according to the circRNA location information and identified 201
differentially methylated circRNA host genes (P < 0.05). DNA
methylation of these circRNA host genes could separate cancer samples
from normal samples ([111]Supplementary Figure 3). We calculated the
Pearson correlation coefficient of DNA methylation between every two
circRNA host genes. By connecting the two circRNAs with a Pearson
correlation coefficient greater than random even >0.6 (P < 0.05), a
DMCC network was constructed ([112]Figure 2B). The circRNA host genes
in the DMCC network are characterized by co-methylation. The DMCC
network contains 92 circRNAs and 138 circRNA-circRNA interactions. The
degree distribution of the DMCC network is also subject to the
power-law distribution ([113]Figure 2C), and therefore the DMCC network
is also biologically significant.
Our results above show that the 92 circRNAs in the DMCC network are
differentially methylated in breast cancer samples, with sequence
similarity and co-methylation characteristics. We next evaluated the
biological function of these circRNA host genes by DAVID and found that
the circRNA host genes are mainly enriched in the cell membrane,
cytoplasm, protein binding, and protein domain specific binding
([114]Figure 2D). These genes are mainly enriched in SUMOylation as a
mechanism to modulate CtBP-dependent gene responses, systemic lupus
erythematosus, and the Notch signaling pathway ([115]Figure 2D).
To identify novel circRNAs associated with DNA methylation, module
mining in the DMCC network was performed. Four network modules
([116]Figure 2B) were obtained in the DMCC network according to the
module score. Four circRNAs (hsa_circ_000582, hsa_circ_002025,
hsa_circ_002024, hsa_circ_001851) are seed nodes of four modules. A
total of 26 circRNAs in the four modules were used as candidate
circRNAs for subsequent studies.
Establishment and Global Properties of the CMD Network
miRNAs interact with circRNAs as well as cancer driver genes through
MREs (Bartel, [117]2009; Lü et al., [118]2017). Thus, some circRNAs may
act as sponges by binding to miRNAs and preventing their interactions
with cancer driver genes. These circRNAs therefore regulate the
expression of driver genes and play a very important role in disease
regulation.
To examine the regulatory interactions among potential circRNAs, their
miRNA targets and putative cancer driver genes, we established a CMD
gene network. We first screened 4,541 differentially expressed genes
between breast cancer and normal samples (fold change > 2 or fold
change < 0.5, p < 0.01). Differentially expressed genes and 125 cancer
driver genes were selected and 25 breast cancer driver genes were
obtained. According to rules ([119]Figure 3A), we constructed a CMD
network ([120]Figure 3B), containing 23 circRNAs, 23 cancer driver
genes and 45 miRNAs. The CMD network is divided into three layers:
driver genes act as functional layer in the outermost layer, and the
internal circRNAs indirectly regulate driver genes through miRNAs in
the middle layer.
Figure 3.
[121]Figure 3
[122]Open in a new tab
Establishment of the CMD gene network and prediction process of sponge
circRNA-driver gene pairs. (A) The construction of the CMD gene
network. (B) The CMD network. The ellipse represents circRNAs, the
diamond represents miRNAs, and the rectangle represents driver genes.
(C) Construction of sponge regulatory mechanism for circRNA and driver
gene by targeting the same miRNA and prediction of sponge circRNAs. (D)
Computational strategy of predicting sponge circRNA-protein coding
driver genes in breast cancer.
The miRNA-mediated complex CC network was established by the
circRNA-miRNA-driver gene interaction. The crosstalk type between the
circRNA-miRNA-driver genes represents a complex transcriptional
regulatory network. This provides a perspective on how to indicate the
intermolecular relationship of cell behavior. Each circRNA-driver gene
pair in the circRNA-miRNA-driver gene network served as candidate
circRNA-driver gene pair for subsequent study.
Prediction of Sponge circRNAs Regulating Cancer Driver Genes Based on the CMD
Network
We next used a bioinformatics approach to predict sponge circRNA-driver
gene pairs in breast cancer affected by DNA methylation.
Sponge circRNAs have a positive regulatory effect on their target gene
expression (Hansen et al., [123]2011; Peng et al., [124]2015; Zheng et
al., [125]2016), and the strength of their regulation depends on the
stoichiometry of the involved miRNAs (Du et al., [126]2016). If the
host gene of the sponge circRNA is hypermethylated, the circRNA host
gene will be silenced, eliminating its ability to bind miRNAs, leading
to downregulation of the target gene. This suggests that high
methylation of host genes may correlate with downregulated target gene
expression and vice versa. Therefore, the sponge circRNA may have the
same methylation pattern as the sponge driver gene ([127]Figure 3C).
Based on this assumption, we developed a computational process to
predict the sponge circRNA and gene pairs in breast cancer.
CircRNA-breast cancer driver gene pairs that share at least one miRNA
were selected as candidate circRNA-driver gene pairs. Using the
prediction algorithm in the flow ([128]Figure 3D), we identified 10
sponge circRNA-driver gene pairs comprising 10 sponge circRNAs and five
protein-coding driver genes ([129]Figures 4A,B).
Figure 4.
[130]Figure 4
[131]Open in a new tab
Location information of sponge circRNA-driver gene pairs. (A) Genome
position information of 10 sponge circRNA-driver gene pairs. Purple bar
represents the genome location of GATA3 and its sponge circRNA. Blue
bar represents the genome location of WT1 and its sponge circRNA. Red
bar represents the genome location of KIT and its sponge circRNA. Pink
bar represents the genome location of CEBPA and its sponge circRNA.
Yellow bar represents the genome location of ERBB2 and its sponge
circRNAs. (B) Sponge circRNA-driver gene pairs in breast cancer. The
nodes represent circRNAs and driver genes (blue represents circRNAs,
red represents driver genes) and the edges represent the predicted
regulation between sponge circRNA and the corresponding protein-coding
driver gene.
We found that most of the sponge circRNAs and driver genes are located
on the different chromosomes (even when located on the same chromosome,
the distance was >2 Mb). This illustrates that the regulation between
sponge circRNAs and sponge driver genes is a trans-acting relationship,
supporting the mechanism by which sponge circRNAs indirectly regulate
sponge driver gene expression by interacting with miRNAs.
Characteristics of Sponge circRNAs Based on DNA Methylation
Many studies have shown that circRNAs can act as sponge circRNAs to
competitively bind to miRNAs and regulate gene expression (Leonardo
Salmena et al., [132]2011; Hansen et al., [133]2013; Zheng et al.,
[134]2016; Peng et al., [135]2017). We next analyzed the predicted
sponge circRNA-driver gene pairs that showed the following
characteristics: (1) DNA methylation of the sponge circRNA host gene
was positively correlated with DNA methylation of the sponge driver
gene promoter region in breast cancer samples ([136]Figures 5A,B) and
(2) DNA methylation of the driver gene promoter region was negatively
correlated with driver gene expression in breast cancer samples
([137]Figures 5C,D). We found that the ERBB2 gene was associated with a
large number of predicted sponge circRNAs compared with other examined
genes ([138]Supplementary Table 1), which suggests that it may have the
better sponge regulation. We also examined DNA methylation of sponge
circRNA host genes, DNA methylation of sponge driver genes and sponge
driver gene expression patterns in breast cancer samples and normal
samples ([139]Supplementary Figure 5). The box plot showed that the DNA
methylation of the sponge circRNA host genes and the DNA methylation of
the sponge driver genes have the same tendency, while the DNA
methylation of the sponge driver gene and sponge driver gene expression
showed the opposite trend.
Figure 5.
[140]Figure 5
[141]Open in a new tab
Correlation of sponge circRNAs and target driver genes. (A) The
correlation coefficient diagram of DNA methylation of sponge circRNA
host genes and target driver genes in breast cancer samples. (B) The
smoothscatter plot of DNA methylation of sponge circRNA host genes and
DNA methylation of target driver genes in breast cancer samples. (C)
The correlation coefficient diagram of DNA methylation of target driver
genes and sponge driver genes expression in breast cancer samples. (D)
The smoothscatter plot of DNA methylation of sponge driver genes and
sponge driver genes expression in breast cancer samples. Gene
expression values were log2 transformed after adding a pseudo-value of
1 to avoid infinite values. The scalable size of circles in correlation
coefficient diagram represent the absolute value of the correlation
coefficient.
We further analyzed pathways of the predicted five cancer driver genes
and found that KIT, ERBB2 and CEBPA are related to PI3K and RAS
pathways (Vogelstein et al., [142]2013). GATA3 is associated with
transcriptional regulatory core pathways (Vogelstein et al.,
[143]2013). WT1 is associated with the chromosome modification core
pathway (Vogelstein et al., [144]2013). These genes are enriched in the
process of cell survival and cell fate ([145]Supplementary Table 2)
(Vogelstein et al., [146]2013). Together these results suggest that the
driver genes regulated by sponge circRNAs may be associated with
cancer. These results are consistent with previous studies that showed
that sponge RNAs may have the same carcinogenic or tumor suppressor
function as their regulatory genes (Du et al., [147]2016).
Sponge circRNAs Are Potential Prognostic Biomarkers for Breast Cancer
To determine whether the sponge circRNAs are associated with the
prognosis of breast cancer, we performed survival analysis using
Infinium Human Methylation 450 BeadChip data and clinical survival data
of breast cancer in TCGA and [148]GSE78754.
Survival analysis shows that patients in the high risk group have worse
overall survival than those in the low risk group in the TCGA dataset
([149]Figure 6A, p = 0.0239). DNA methylation of the circRNA host genes
clearly separated the high and low risk groups in breast cancer
patients in the first 10 years. Similarly, patients in the high risk
group showed worse survival compared with those in the low risk group
in the [150]GSE78764 dataset ([151]Figure 6B, p = 0.0377), and DNA
methylation of circRNA host genes separated the high and low risk
groups of breast cancer patients. These results indicate that the 10
sponge circRNAs can not only be used as potential diagnostic markers
for breast cancer, but also may reflect the prognosis of patients.
Figure 6.
[152]Figure 6
[153]Open in a new tab
Survival analysis of 10 sponge circRNAs. (A) Survival analysis curve of
10 sponge circRNAs using TCGA clinical data (breast cancer). Red curve
represents high risk patients, and blue curve represents low risk
patients. (B) Survival analysis curve of 10 sponge circRNAs using
[154]GSE78754. Red curve represents high risk patients, and blue curve
represents low risk patients.
DNA Methylation of circRNA Host Gene and Driver Gene Synergistically Affect
Driver Gene Expression
We also calculated the correlation between DNA methylation of circRNA
host genes and driver genes with driver gene expression. The results
indicated that DNA methylation of sponge circRNA host gene and DNA
methylation of driver gene synergistically affect driver gene
expression in breast cancer ([155]Supplementary Figure 4). Moreover,
DNA methylation of the predicted two sponge circRNAs (hsa_circ_000582,
hsa_circ_001109) showed a significant negative correlation with the
expression of their sponge driver genes (KIT, GATA3) ([156]Figures
7A,B). The relationship between the expression of ERBB2, CEBPA, and WT1
and DNA methylation of their sponge circRNAs is shown in
[157]Supplementary Figure 6.
Figure 7.
[158]Figure 7
[159]Open in a new tab
Relationship between the DNA methylation of the predicted two sponge
circRNAs (hsa_circ_000582, hsa_circ_001109) and expression of their
sponge driver genes (KIT, GATA3). (A) Competition interaction diagram
of the predicted two sponge circRNAs (hsa_circ_000582, hsa_circ_001109)
and their sponge driver genes (KIT, GATA3). (B) The correlation
coefficient diagram of DNA methylation of the predicted two sponge
circRNAs (hsa_circ_000582, hsa_circ_001109) and expression of their
sponge driver genes (KIT, GATA3). The scalable size of circles in
correlation coefficient diagram represent the absolute value of the
correlation coefficient.
Discussion
CircRNAs are a class of non-coding RNAs that regulate gene expression
at both transcriptional and post-transcriptional levels. Recent studies
have shown that circRNAs can also function as miRNA sponges (Ebert and
Sharp, [160]2010; Leonardo Salmena et al., [161]2011). CircRNAs play an
important role in cancer and can be used as biological markers for
prognosis.
In our study, we consider the effect of transcriptional and
post-transcriptional levels on breast cancer. First, genomic,
epigenetic genomics, and non-coding RNA data were integrated. Next, we
constructed a DMCC network and a CMD gene network. We designed a
computational process to predict sponge circRNA-driver gene pairs in
breast cancer to evaluate the effect of DNA methylation of sponge
circRNA host genes on circRNA sponge function. The regulation of sponge
circRNA on the protein coding driver genes is not one to one, but there
exists a mixed regulation. ERBB2 showed a large number of predicted
sponge circRNAs compared with other examined genes, indicating that
ERBB2 may have better circRNA sponge regulation function. ERBB2 is a
185 kDa cell membrane receptor encoded by the oncogene erbB-2, a member
of the epidermal growth factor receptor family. Higher Ras-MAPK and
PI3K-Akt signaling activity is detected in ERBB2 overexpressing tumor
cells, which show stronger cell proliferation ability. Survival
analysis showed that DNA methylation of sponge circRNA host genes could
significantly differentiate breast cancer samples in two data sets.
These results suggest that the predicted sponge circRNAs can be used as
a prognostic biomarker for breast cancer.
The experimental methods that are currently used to verify sponge
circRNAs require extensive time and are costly. Therefore, using
bioinformatics calculation methods to identify sponge circRNAs may be
more advantageous. While our study focused on predicting sponge
circRNAs in breast cancer, these methods can be extended to other
cancer types. We provide a new approach to study the regulatory effects
of sponge circRNAs associated with DNA methylation in cancer, which may
be helpful for understanding of the mechanism of competitive endogenous
RNAs (ceRNA).
Our research reveals a complex DNA methylation-mediated circRNA sponge
regulatory mechanism ([162]Supplementary Figure 7). Interestingly, four
of the 10 Sponge circRNAs (hsa_circ_000582, hsa_circ_000691,
hsa_circ_000877, hsa_circ_000690) were identical to the breast
cancer-specific circRNAs from previous studies (Coscujuela Tarrero et
al., [163]2018). Sponge circRNAs may lead to abnormal expression of
important protein coding driver genes in breast cancer. Furthermore,
the DNA methylation-mediated disruption of circRNA sponge regulation
may be a useful target for cancer treatment.
The quantification of the DNA methylation of circRNA host genes in this
study is based on the Illumina Infinium HumanMethylation 450 BeadChip
of breast cancer in the TCGA database, which covered only 482,421 CpG
sites, whereas WGBS covers more CpG sites. Therefore, using high
throughput sequencing data such as WGBS to quantify the DNA methylation
of circRNA host genes may be more accurate, but still shows the problem
of insufficient sample size.
Our research reveals a complex DNA methylation-mediated regulation of
circRNA sponges. Sponge circRNAs bind to miRNAs to prevent their
interactions with target genes, and sponge circRNAs showed the same DNA
methylation pattern and expression pattern as the sponge-driven genes.
Sponge circRNAs may significantly contribute to the abnormal expression
of important protein-coding genes in breast cancer. The DNA
methylation-mediated circRNA sponge regulation may be a potential
target for cancer therapy. Our findings also show a new perspective of
circRNA as a miRNA sponge in the pathogenesis.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can
be found here: [164]https://gdc.cancer.gov/GSE78754.
Ethics Statement
The studies involving human participants were reviewed and approved by
the Ethics Committee of the working institution and in accordance with
the Helsinki Declaration (revised in Fortaleza, Brazil, October 2013).
The patients/participants provided their written informed consent to
participate in this study. Written informed consent was obtained from
the individual(s) for the publication of any potentially identifiable
images or data included in this article.
Author Contributions
YG, CCi, and SZ contributed to the study design, identification of the
level of DNA methylation of circular RNA, analysis of data and
manuscript draft. YZ contributed to the study design and supervision.
XZ provided help about the mechanism of circRNA during the manuscript
revision. MS, WL, CCh, and HL were responsible for data search and
figure beautification. DZ contributed to the study supervision.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments