- Open Access
Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer
- Manu Shivakumar†1,
- Younghee Lee†2,
- Lisa Bang1,
- Tullika Garg3,
- Kyung-Ah Sohn4Email author and
- Dokyoon Kim1, 5Email author
© The Author(s). 2017
Published: 24 May 2017
One of the fundamental challenges in cancer is to detect the regulators of gene expression changes during cancer progression. Through transcriptional silencing of critical cancer-related genes, epigenetic change such as DNA methylation plays a crucial role in cancer. In addition, miRNA, another major component of epigenome, is also a regulator at the post-transcriptional levels that modulate transcriptome changes. However, a mechanistic role of synergistic interactions between DNA methylation and miRNA as epigenetic regulators on transcriptomic changes and its association with clinical outcomes such as survival have remained largely unexplored in cancer.
In this study, we propose an integrative framework to identify epigenetic interactions between methylation and miRNA associated with transcriptomic changes. To test the utility of the proposed framework, the bladder cancer data set, including DNA methylation, miRNA expression, and gene expression data, from The Cancer Genome Atlas (TCGA) was analyzed for this study.
First, we found 120 genes associated with interactions between the two epigenomic components. Then, 11 significant epigenetic interactions between miRNA and methylation, which target E2F3, CCND1, UTP6, CDADC1, SLC35E3, METRNL, TPCN2, NACC2, VGLL4, and PTEN, were found to be associated with survival. To this end, exploration of TCGA bladder cancer data identified epigenetic interactions that are associated with survival as potential prognostic markers in bladder cancer.
Given the importance and prevalence of these interactions of epigenetic events in bladder cancer it is timely to understand further how different epigenetic components interact and influence each other.
Precision medicine, an emerging approach for disease prevention and treatment strategies based on patients’ environmental and genomic variabilities, is moving toward a new era of future medicine . Since cancer is a disease of the genome, cancer genomics aims to improve personalized medicine through the advanced sequencing technology and analysis of patient tumors to discover new genetic alterations associated with specific cancers. To support advances in developing more effective ways to diagnosis, treat, and prevent cancer, a comprehensive understanding of the underlying genetic architectures that drive different cancers is needed.
One of the fundamental challenges in cancer is to detect the regulators of gene expression changes during cancer progression. Through transcriptional silencing of critical cancer-related genes, epigenetic change such as DNA methylation plays a crucial role in cancer . Cytosine methylation of CpG islands are likely to occur in promoter regions located close to the start of transcription, and hypermethylation in the promoter regions is negatively associated with the mRNA level . For example, the hypermethylation of tumor suppressor genes, which is associated with their inhibition of transcription, is recognized as one of the key features of cancer pathogenesis . On the contrary, CpG methylations in gene body regions are likely to be positively associated with transcript level . In addition to DNA methylation, miRNA, another major component of epigenome, is also a regulator at the post-transcriptional levels that modulate transcriptome changes . miRNAs regulate many cancer-related genes associated with different biological processes such as proliferation, apoptosis, development, and tumorigenesis [6–8]. However, a mechanistic role of synergistic interactions between DNA methylation and miRNA as epigenetic regulators on transcriptomic changes and its association with clinical outcomes such as survival have remained largely unexplored in cancer.
Patients’ variability in multi-omics data, including somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene, and protein expression, should be captured simultaneously since cancer is an extremely heterogeneous disease. Large-scale collaborative initiatives such as The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) have been generating multi-omics data, mostly using the advanced sequencing technologies, as well as patients’ clinical data. These collaborative initiatives have provided unprecedented opportunities to deepen our understanding of complex mechanisms of cancer for advancing precision medicine [9, 10]. Since different types of genomic data sets are regarded as partially independent from and partially complementary to others, there has been an ever-increasing demand for the development of data integration methodologies [11–13]. Therefore, many data integration methods have been developed to improve prediction of cancer clinical outcomes [14–23].
Previously, we developed a novel graph-based framework that integrates multi-omics data and inter-relationship between omic features to better predict cancer clinical outcomes . Notably, a prediction model showed the great improvement when combining inter-relationship between miRNA and DNA methylation data . The previous study suggested that there might be possible synergistic regulatory mechanisms between miRNA and methylation within the epigenome of cancer-related genes, and further these epigenetic interactions could be associated with clinical outcomes such as survival. Thus, integrating miRNA, DNA methylation and gene expression profiles can aid in extracting new biological knowledge by drawing associations between epigenetic interactions and clinical outcomes in cancer. In this study, we propose an integrative framework to identify epigenetic interactions between methylation and miRNA associated with transcriptomic changes and further detect epigenetic interactions significantly associated with prognosis. To test the utility of the proposed framework, urothelial carcinoma of the bladder data set from TCGA was analyzed for this study. Thus far, no molecularly targeted agents have been approved for treatment of the disease .
Clinical values (N = 403)
Histological subtype (Non-papillary/Papillary/NA)
Stage (I, II/III, IV/NA)
Smoking status (Smoker/Non-smoker/NA)
TCGA bladder cancer data types used for the analysis
Number of featuresa
Infinium HM450 BeadChip
Illumina HiSeq miRNA-Seq
Illumina HiSeq RNA-Seq
Extracting relationship between methylations, miRNAs and genes
The methylation probes in the TCGA data were mapped to the nearest gene using the open source Illumina methylation platform annotation file. To reduce many false positives of miRNA-target gene interactions, the relationships between miRNAs and their target genes were obtained using miRTarBase, which is a manually collected database of miRNA-target interactions experimentally validated by reporter assay, western blot, microarray and next generation sequencing experiments . Relationships between miRNAs and target genes are many-to-many; thus miRTarBase with 410,621 entries mapped 1,046 miRNAs to 12,657 genes, resulting in 722,812 combinations consisting of 7,922 genes, 146,653 methylation probes and 416 miRNAs.
Identifying interactions between miRNA and methylation associated with gene expression
Linear regression models were used to determine the significance of the interaction between miRNA and methylation above and beyond the additive effects of each variable alone on gene expression variability. To achieve this goal, we performed a likelihood ratio test (LRT) between the full and reduced model for 722,812 combinations. The full model tested the effects of miRNA + methylation + miRNA*methylation (interaction term) on gene expression. The reduced model tested only the effects of miRNA + methylation. For both models, we adjusted for sex and age. LRT is a well-established statistical test to examine whether the observed difference in model fit is statistically significant. Bonferroni correction was applied to correcting for multiple LRT p-values.
Overall survival analysis
To identify interactions between miRNA and methylation that are significantly associated with prognosis, Kaplan-Meier overall survival analysis was performed. We graphed the methylation with miRNA expression profiles and classified patients’ profiles into nine subgroups based on three quantiles of miRNA and three quantiles of methylation profiles. We then ran survival analysis for the two extreme subgroups – the first subgroup from the lowest quantiles of miRNA and methylation profiles, and the second subgroup from the top quantiles of both profiles.
Analysis of differential gene expression
We tested the difference in gene expression levels between subgroups defined based on quantiles of miRNA and methylation. Student’s T-test was performed to check significant differential gene expression levels between the subgroup from the lowest quantiles of miRNA and methylation and the subgroup from the top quantiles of the data set. ANOVA was used to test the significance of differential expression levels among four different subgroups, including low methylation & low miRNA (LL), low methylation & high miRNA (LH), high methylation & low miRNA (HL), and high methylation & high miRNA (HH) levels.
Identification of interactions between miRNA and methylation associated mRNA levels
Pathway over-representation analysis of 120 genes associated with interactions between miRNA and methylation
Reactome pathway name
Oncogene induced senescence
Deactivation of the beta-catenin transactivating complex
Pre-NOTCH transcription and translation
Oxidative stress induced senescence
PIP3 activates AKT signaling
PI-3 K cascade:FGFR1
PI-3 K cascade:FGFR2
PI-3 K cascade:FGFR3
PI-3 K cascade:FGFR4
PI3K events in ERBB4 signaling
PI3K events in ERBB2 signaling
Pre-NOTCH expression and processing
Role of LAT2/NTAL/LAB on calcium mobilization
repression of WNT target genes
Downstream signaling events of B Cell Receptor (BCR)
AKT phosphorylates targets in the cytosol
Negative regulation of the PI3K/AKT network
Cyclin D associated events in G1
Epigenetic interactions associated with survival outcome
Summary of overall survival analysis results
Bonferroni-corrected LRT p-value
p-value from survival analysis
Differential gene expression between subgroups defined by epigenetic interactions
Differential miRNA expression patterns in cancer – tumor-suppressing effect
Decreased expression of miR-217 was found to be significantly associated with large tumor size and advanced clinical stage , and miR-217 directly suppressed E2F3 and thereby inhibited invasion of hepatocellular carcinoma . MiR-217 was also found to regulate and be regulated by miR-30a-3p, which suppresses p53 . Previously, introduction of synthetic miR-107 suppressed growth of human non-small cell lung cancer cell lines  and high levels of miR-107 were associated with a better survival outcome in gastric cancer . MiR-107 was found to target DICER1 and thereby regulate tumor invasion and metastasis (Fig. 3) . Mutations in DICER1 lead to an abnormally short Dicer protein that is unable to aid in the production of miRNA; Dicer acts as an oncogene or tumor suppressor in varying contexts, including varied roles in bladder cancer (Fig. 3) . MIR-940 levels were found to be the highest in invasive and advanced bladder cancer  and has previously been found to inhibit the migratory and invasive potential of cells and increase E-cadherin expression by regulating MIEN1. MiR-940 is highly expressed in immortalized normal cells compared to cancer cells and plays a role in mesenchymal-to-epithelial transition (MET) . MiR-543 is known to target SIRT1 in gastric cancer , and miR-543-mediated targeting of SIRT1 is known to alleviate insulin resistance . MMP7 (an oncogene) is also targeted by miR-543 in ovarian cancer; downregulation of miR-543 promotes cancer invasion . The function of miR-1976 is poorly characterized although it was identified as aberrantly expressed in lymphoblastic leukemia .
Differential miRNA expression patterns in cancer – high levels of expression in cancer
MiR-944 is overexpressed in human cervical cancer cells . MiR-944 is located in the intron of the TP63 gene but has its own promoter; however, miR-944 biogenesis is markedly increased by the binding of a TP63 gene product, ΔNp63 protein. Moreover, miR-944 upregulates p53 expression . In making the case for distinct subtypes of bladder cancer, basal and luminal, Choi et al. found that TP63 knockdown (and inferred from that, lessened miR-944) deceased basal pathway gene expression and also increased PPAR pathway gene expression, associated with luminal-type carcinomas. The evidence suggests a complex pathway for the interaction of miR-944 and p63 in encouraging the development of primary basal MIBC and perhaps discouraging the luminal type of MIBC. Plasma-borne levels of miR-944 and miR-3662 has been suggested as possible biomarkers for lung cancer . In hepatocellular carcinoma, miR-3662 was also found to be upregulated by p53 (Fig. 3) . MiR-1254 was suggested as a serum-based miRNA biomarker for early-stage lung cancer  and in contrast to canonical miRNAs, its biogenesis is independent of DROSHA ; miR-1254 expression enhancement may re-sensitize tamoxifen-resistant breast cancer cells to tamoxifen .
Downregulation of expression of target with high epigenetic control
The pattern of epigenetic control leading to lower expression held for E2F3, CCND1, CDADC1, SLC35E3, NACC2, and VGLL4, and for some of these (CCND1, CDADC1, NACC2, VGLL4), the epigenetic control was found to be associated with a worse survival outcome. CDADC1 is a domain of cytidine and dCMP deaminase, also called NYD-SP15. CDADC1 was found to dynamically shuttle between nucleus and cytoplasm and overexpression of CDADC1 was found to reduce cell growth and block G1 to S phase transition in the cell cycle . It was also dysregulated in liver cancer  and involved in regulating testicular development . High epigenetic control of CDADC1 was associated with lower expression and with a worse survival outcome; we posit that lower expression of CDADC1 would encourage cell cycle deregulation (including less breakdown of cytidine, a component of RNA) and thereby worsen survival outcome. NACC2, also known as RBB, is a transcription repressor that is an important regulator of the p53 pathway: NACC2 inhibits the expression of MDM2, which stabilizes p53 expression . VGLL4 is a Hippo pathway member and acts as a YAP agonist ; it is said to function as a tumor suppressor in gastric cancer , lung cancer  and was also included in a smoking cessation quit-success genotype score calculation . As esophageal squamous cell carcinoma progresses, VGLL4 expression is downregulated .
CCND1 gene amplification was previously found to be correlated to histopathological tumor characteristics, cancer-specific survival and response to chemotherapy in bladder cancer [57, 58]. CCND1 protein regulates the cell cycle during the G(1)/S transition, is a substrate for SMAD1, and also phosphorylates and inhibits members of the RB protein family, including RB1. When RB1 is phosphorylated, E2F, a transcription factor, disassociates from the RB/E2F complex and allows the E2F target genes to be transcribed. Our model also implicated epigenetic control of a component of E2F, E2F3, as associated with a better survival outcome. MiRNA targeting (miR-577) of E2F3 was found to inhibit gastric cancer cell progression , and miRNA targeting of E2F3 and CCND1 (miR-449b) was found to inhibit the proliferation of SW1116 colon cancer stem cells .
In a study of metastatic urothelial carcinoma, significant copy number amplifications were found in E2F3 (30% vs. 7% amplification) and CCND1 . For E2F3 and CCND1, we found that the interaction of methylation of the target gene (E2F3/CCND1) and presence of a targeting miRNA was associated with lower expression of the target gene; high epigenetic control was associated with a worse survival outcome for CCND1, but a better survival outcome for E2F3.
For SLC35E3, the lowered expression resulting from the interaction between miRNA targeting and methylation of target, as for E2F3, led to a better survival outcome. SLC35E3, also called Bladder Cancer-Overexpressed gene 1 (BLOV1), acts in transmembrane transport and belongs to the drug/metabolite transporter protein superfamily (Luscombe, MJ, A novel gene which is overexpressed in advanced bladder cancer May 1999, EMBL/GenBank/DDBJ databases). SLC35E3 was also bioinformatically identified as a potential secreted or transmembrane protein .
Maintenance or increase of expression of target with high epigenetic control
For UTP6, METRNL, TPCN2, and PTEN, high epigenetic control was associated with higher expression of the target gene. For UTP6 and PTEN, high epigenetic control led to a worse survival outcome. UTP6 (HCA66) is required for both centriole duplication and ribosome synthesis . Haploinsufficiency-derived UTP6 under-expression in neurofibromatosis type 1 (NF1) cells resulted in those cells being less susceptible to apoptosis . UTP6 was also implicated as element of the interactome of the human histone deacetylase family . PTEN has been shown to be hypermethylated in ovarian cancer cell lines and also highly regulated at the translational level , and aberrantly expressed in many forms of cancer . In MIBC squamous epithelia, the expression of PTEN was reduced or lost, and mTOR expression was negatively correlated with PTEN expression only in urothelial squamous cell carcinoma, not schistosomal bladder squamous cell carcinoma .
METRNL expression was reduced in the high methylation/high miR-107 (H/H) cohort as compared to the low methylation/low miR-107 (L/L) group for one methylation probe (cg01502876) and unchanged for the other methylation probe (cg03155999), suggesting that methylation of METRNL in cancer will reduce its expression in some cases (Fig. 3). We found that the cohort of patients with high levels of miR-107 and high methylation of its target METRNL at both probes had a better survival outcome. METRNL promotes glucose tolerance and energy expenditure, and blocking METRNL in vivo causes reduces thermogenic gene response and may play a role in tissue inflammation . TPCN2 encodes an NAADP (nicotinic acid adenine dinucleotide phosphate)-induced two-pore Ca(2+) ion channel which is ubiquitously expressed but has elevated expression in liver and kidney and operates as a sensor of both luminal pH and Ca(2+) . TPCN2 is highly sensitive to pH, and is localized intracellularly to endolysosomal organelles . The fact that TPCN2 is induced by nicotinic acid raises questions about potentially novel mechanisms that may underlie well-established link between bladder cancer and smoking.
In this study, we proposed a novel approach to identify epigenetic interactions between methylation and miRNA associated with prognosis in bladder cancer. We identified 11 significant epigenetic interactions associated with survival (Table 4). In particular, a higher epigenetic control group (HH) on NACC2 was only associated with good prognosis compared to other subgroups (HL, LH, and LL) (Fig. 2). This suggests that these epigenetic interactions might be new prognostic markers that can be detected by only integrating both methylation and miRNA data, not by miRNA or methylation alone. In addition, a global view of inter-plays of miRNA, methylation, and gene expression could aid in extracting new biological knowledge (Fig. 3). The interactions with miRNAs as targeter and targetee belie potential feedback loops in dictating survival outcome. Intriguingly, double epigenetic control in some cases leads to no change or a slight increase in expression. For UTP6 and PTEN, the presence of epigenetic control does not lead to a change in expression of the target but does lead to a worse survival outcome. For current study, we captured the interactions between miRNA and methylation. However, genomic changes, such as somatic mutations or CNA, could substantially affect the transcriptomic changes and could induce a certain level of bias in estimating the interactions between other epigenetic factors. To better understand the role of epigenetic interactions on gene expression levels, we will improve the current approach to incorporate other omics data as well as a future work.
To this end, exploration of TCGA bladder cancer data identified epigenetic interactions that are associated with survival as a potential prognostic marker. Given the importance and prevalence of these interactions of epigenetics events in bladder cancer it is timely to understand further how different epigenetic components interact and influence each other. Thus, cancer patient’s variability in molecular signatures based on these epigenetic interactions in bladder cancer may lead to better prognostic/treatment strategies for improved precision medicine. Our results warrant further investigation in a larger independent cohort.
We gratefully acknowledge the TCGA Consortium and all its members for the TCGA Project initiative, for providing sample, tissues, data processing and making data and results available. The results published here are in whole or part based upon data generated by The Cancer Genome Atlas pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions that constitute the TCGA research network can be found at http://cancergenome.nih.gov.
This project is funded, in part, under a grant with the Pennsylvania Department of Health (#SAP 4100070267). The Department specifically disclaims responsibility for any analyses, interpretations or conclusions. This work was also funded by NIGMS grant P50GM115318. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (MSIP) [NRF-2014R1A1A3051169]. In addition, the publication charge for this article was funded by DK’s startup funding at Geisinger Health System.
Availability of data and materials
The TCGA datasets used for analysis are publicly available at https://gdc.cancer.gov/.
MS, YL, KS, and DK designed and developed the research, and also performed the experiments. KS and DK provided experienced guidance. LB and TG collected medical literature and made up supporting materials to infer the results. MS, YL, KS, and DK wrote the manuscript and all authors read the manuscript and approved it.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Consent for publication
About this supplement
This article has been published as part of BMC Medical Genomics Volume 10 Supplement 1, 2017: Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-1.
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