MethHC: a database of DNA methylation and gene expression in human cancer

D856–D861 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 14 November 2014 doi: 10.1093/nar/gku1151

MethHC: a database of DNA methylation and gene

expression in human cancer

Wei-Yun Huang

_{, Sheng-Da Hsu}

_{, Hsi-Yuan Huang}

_{, Yi-Ming Sun}

_{, Chih-Hung Chou}

_,

Shun-Long Weng

_{and Hsien-Da Huang}

1_{Department of Biological Science and Technology, National Chiao Tung University, Hsin-Chu 300, Taiwan,}2_Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan,3_{Department of} Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu 300, Taiwan,4_{Mackay Medicine, Nursing} and Management College, Taipei 112, Taiwan,5_{Department of Medicine, Mackay Medical College, New Taipei City} 252, Taiwan,6_{Center for Bioinformatics Research, National Chiao Tung University, Hsin-Chu 300, Taiwan and} 7_{Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 807,} Taiwan

Received August 15, 2014; Revised October 11, 2014; Accepted October 28, 2014

ABSTRACT

We present MethHC (http://MethHC.mbc.nctu.edu. tw), a database comprising a systematic integra-tion of a large collecintegra-tion of DNA methylaintegra-tion data

and mRNA/microRNA expression profiles in

hu-man cancer. DNA methylation is an important epi-genetic regulator of gene transcription, and genes with high levels of DNA methylation in their pro-moter regions are transcriptionally silent. Increasing

numbers of DNA methylation and mRNA/microRNA

expression profiles are being published in different public repositories. These data can help researchers to identify epigenetic patterns that are important for carcinogenesis. MethHC integrates data such as DNA methylation, mRNA expression, DNA methyla-tion of microRNA gene and microRNA expression to identify correlations between DNA methylation and mRNA/microRNA expression from TCGA (The Can-cer Genome Atlas), which includes 18 human can-cers in more than 6000 samples, 6548 microarrays and 12 567 RNA sequencing data.

INTRODUCTION

DNA methylation is one of the epigenetic mechanisms that can control gene expression without incurring any change to genomic sequence during development and cell prolif-eration (1). Many studies have shown that DNA methyla-tion plays an important role in cancer biology and provides promising biomarkers for cancer diagnosis and prognosis evaluation (2,3). Recent studies have shown that epigenet-ics plays an important role in cancer biology, viral infec-tions, somatic gene therapy, transgenic technologies,

devel-opmental abnormalities and genomic imprinting (4,5). In recent decades, attention has focused on DNA methylation in cancer. For example, TP53 (6) and RB1 (7) are hyperme-thylated in glioma, WNT5A is hypomehyperme-thylated in prostate cancer (8), SMN2 is hypermethylated in spinal muscular at-rophy and HSPB1 methylation is a biomarker in prostate cancer as well as in human breast cancer, colorectal cancer and malignant melanoma (9). Furthermore, DNA methyla-tion may serve as a tumor marker or diagnosis and therapy index in colorectal cancer (10,11) and ovarian cancer (12).

A wide range of techniques is used to detect DNA methy-lation, including mass spectrometry, methylation specific PCR, CHIP-on-chip assays, methylated DNA immuno-precipitation, microarray assay, pyrosequencing of bisul-fite treated DNA, whole genome bisulbisul-fite sequencing, etc. These methods can be divided into two categories, gene-specific approaches and genome-wide approaches. Studies in recent years have produced copious amounts of DNA methylation data, especially epigenome-wide array data and next-generation sequencing (NGS) data. Collection of DNA methylation and mRNA/microRNA expression data related to diseases produced by high-throughput ap-proaches based on array and NGS will facilitate the dis-covery of potential methylation events in human diseases (13). Hence, several DNA methylation databases had been constructed. MethDB is a database that stores manually cu-rated experimental data on DNA methylation and environ-mental epigenetic effects (14). PubMeth is an annotated and reviewed database of methylation in cancer based on text mining of the published literature (15). MethyCancer (16) contains DNA methylation, cancer-related gene and can-cer information from public data sources and large-scale ex-perimental data sets produced from the Cancer Epigenome Project in China. NGSMethDB (17) provides DNA methy-lation data for humans, chimpanzees, mice and

Arabidop-*_{To whom correspondence should be addressed. Tel: +886 3 5712121 (Ext. 56952); Fax: +886 3 5739320; Email: [email protected]}

C

The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/

Figure 1. Data generation flow of MethHC.

sis thaliana from publicly available data sets using the NGS bisulfite sequencing technique. DiseaseMeth (18) provides both locus-specific and high-throughput data sets for many human diseases. MENT (19) is the first database to provide both DNA methylation and gene expression information for diverse tumor tissues.

MicroRNA, a small non-coding RNA, functions in RNA silencing and post-transcriptional regulation of gene ex-pression, and is frequently associated with cancers and may be an important causal factor. Aberrant DNA methyla-tion has been found to silence microRNA genes in breast cancer, such as mir-31, mir-130a, mir-155, mir-137 and mir-34b/mir-34c genes (20), indicating the important role of DNA methylation in microRNA deregulation in can-cer. Recently, various high-throughput approaches based on array and NGS have been developed and applied in combination with bisulfite conversion of the DNA for genome-wide DNA methylation analysis in mammals and plants (21), and a large number of DNA methylation data has been rapidly accumulated. However, there are fewer databases that provide both DNA methylation and gene expression information. Thus, there is a great need for an integrated database that provides both DNA methylation and mRNA/microRNA expression information in normal and tumor tissues. This paper describes a DNA methylation and gene expression database in human cancer, MethHC

(database of DNA methylation and gene expression in hu-man cancer). MethHC is a web-based resource focused on the aberrant methylomes of human diseases. MethHC integrates data including DNA methylation, gene expres-sion, microRNA methylation, microRNA expression and the correlation between DNA methylation and gene expres-sion from TCGA (The Cancer Genome Atlas).

DATABASE CONTENT

Figure1 shows the data generation flow of the MethHC database, which comprises two parts: (i) integration of ex-perimental data source and (ii) integration of annotated databases. A detailed description is provided below.

The DNA methylation data sets used in MethHC are mainly taken from TCGA which was launched in 2006 with an investment of US$50 million from the National Cancer Institute and the National Human Genome Re-search Institute. The goal of TCGA is to improve the ability to diagnose, treat and prevent cancer. Therefore, TCGA collects and analyzes high-quality tumor samples such as clinical information about participants, metadata about the samples, histopathology slide images of the sam-ple and molecular information derived from the samsam-ples (e.g. copy number variation, DNA methylation, single-nucleotide polymorphism, protein expression, DNA

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/

D858 Nucleic Acids Research, 2015, Vol. 43, Database issue

Figure 2. Illustrative screenshot of the MethHC interface.

quencing and mRNA/microRNA expression etc.). A pilot project developed and tested the research framework to sys-tematically explore the entire spectrum of genomic changes involved in more than 20 types of human cancer. The result-ing data are freely available through the TCGA Data Portal and the Cancer Genomics Hub (CGHub). TCGA collects three types of array-based DNA methylation platform data: Illumina DNA Methylation Cancer Panel I, Illumina Hu-manMethylation27 BeadChip and Illumina HumanMethy-lation450K BeadChip. To obtain human DNA methylation and gene expressions, we integrated the heterogeneous data from TCGA. First, DNA methylation data are specific to the Illumina HumanMethylation450K BeadChip platform, because of the more probes in HumanMethylation450K BeadChip. Second, Illumina HiSeq 2000 RNA Sequencing platform data are for gene expression. Third, microRNA expression profiles were done using Illumina HiSeq 2000 miRNA Sequencing platform.

MethHC provides not only DNA methylation and gene expression data for protein-coding genes, but also miRNAs. The information for gene transcription start sites (TSS) and microRNA TSS was taken from the UCSC Genome

Browser (22) and the miRStart database (23). miRStart in-tegrates data from many TSS-relevant experiments, offering reliable human microRNA TSSs for the further decipher-ment of microRNA transcription regulation.

To study DNA methylation patterns, different regions and CpG islands are considered. Hypermethylation in the promoter region would induce the down-regulation of gene expression. However, hypermethylation in the gene body may not block and might even stimulate transcription elongation and exciting new evidence suggests that gene body methylation may have an impact on splicing. CpG island (CGI) hypermethylation of the TSS is associated with long-term silencing (24). The differential methylation level at the CpG island shore is tissue-specific (25). Most DNA methylation-cancer studies have focused on promot-ers, CpG islands and sometimes a bit further out (e.g. CpG shores), but mostly overlap with putative enhancer regions, and they are enriched in consensus binding sequences for important renal transcription factors (26). Therefore, we provide several different gene regions (promoter, TSS, un-translated region, exon, gene body and enhancer region) and CGI regions (CpG Island, shore and shelf).

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/

Table 1. MethHC statistics for samples in each cancer

Cancer DNA methylation microRNA expression Gene expression

Bladder urothelial carcinoma 276 260 260

Breast invasive carcinoma 839 798 1159

Cervical squamous cell carcinoma and endocervical adenocarcinoma

217 204 188

Colon adenocarcinoma 315 246 305

Head and neck squamous cell carcinoma

569 520 542

Kidney renal clear cell carcinoma 458 314 593

Kidney renal papillary cell carcinoma 229 230 203

Liver hepatocellular carcinoma 256 250 244

Lung adenocarcinoma 471 476 540

Lung squamous cell carcinoma 406 377 451

Pancreatic adenocarcinoma 102 88 76

Prostate adenocarcinoma 387 376 342

Rectum adenocarcinoma 105 94 106

Sarcoma 178 138 109

Skin cutaneous melanoma 380 356 353

Stomach adenocarcinoma 330 316 318

Thyroid carcinoma 565 566 541

Uterine corpus endometrial carcinoma 465 419 209

Total 6548 6,028 6539

Table 2. Comparing MethHC with other resources

MethDB PubMeth MethyCancer NGSMethDB DiseaseMeth MENT MethHC

Publication NAR. 2001 NAR Database

Issue (2008) NAR Database Issue (2008) NAR Database Issue (2010) NAR Database Issue (2012) GENE (2013) Last update 2009 – 2008 2014 – – 2014

Support species Four species Homo sapiens Homo sapiens Six species Homo sapiens

72 diseases Homo sapiens 30 tissues Homo sapiens 18 cancers Number of samples 20 236 fragments 6312 profiles – 11 561 (genes) Human: 19 Monkey: 6 Tomato: 8 Mouse: 45 Arabidopsis: 27 175 data sets (3610 microarray data) Methylation: 9243 microarray data Gene expression: 34 000 microarray data Methylation: 6548 microarray data, Gene expression: 12 567 RNA sequencing data Number of methylation sites – – 10 486 – – 27 578 482 481

Number of gene – – 11 561 – – 14 476 20 500 genes

1040 microRNAs

Data sources Experiments Medline/

PubMed

BIG, MethDB, HEP

GEO GEO GEO, TCGA TCGA

Correlation analysis

No No Yes Yes Yes Yes Yes

microRNA expression

No No No No No No Yes

Gene region No No CpG island,

promoter region CpG

Promoter region

Promoter region (−1.5K to 0.5K)

Probe level only Eight gene regions*

five CpG island regions** Other characteristic Experimental data only Automated text mining Repeat sequence Search CpG, CHG Offline analysis, CpG Differential methylation, Correlation analysis, Sample subtype MicroRNA expression, Differential methylation, Correlation analysis

Finally, to complete the gene information, we also inte-grate the UCSC Genome Browser, miRStart and KEGG PATHWAY databases. The KEGG (Kyoto Encyclopedia of Genes and Genomes) project was initiated in 1995 (27) and the KEGG PATHWAY database is a collection of manu-ally drawn KEGG pathway maps representing experimental knowledge for metabolism, genetics information process-ing, cellular processes, organism systems and human dis-eases. MethHC allows users to select a particular KEGG pathway and identify differentially methylated genes in a set of tumors.

DATABASE STATISTICS

MethHC is a web-based resource focused on the aber-rant methylomes of human diseases. MethHC integrates DNA methylation data, gene expression data and mi-croRNA expression data from TCGA. MethHC currently comprises 6548 DNA methylation data generated by Il-lumina HumanMethylation450K BeadChip and 12 567 mRNA/microRNA expression data generated by RNA-seq/ miRNA-seq in 18 human cancers. Table1provides a detailed list of the cancer and sample numbers, and the data

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/

D860 Nucleic Acids Research, 2015, Vol. 43, Database issue

include a greater number of samples in the breast invasive carcinoma cohort, thyroid carcinoma and head and neck squamous cell carcinoma. MethHC contains 20 500 genes and 1040 miRNAs, and each gene includes eight gene re-gions and five CGI rere-gions.

COMPARISON

Table 2 compares MethHC with other methylation databases including MethDB, PubMeth, MethyCancer, NGSMethDB, DiseaseMeth and MENT. MethDB only stores DNA methylation data and makes these data readily available to the public. PubMeth is based on text mining of Medline/PubMed abstracts and combined with manual reading and annotation of preselected abstracts. MethyCancer provides large-scale methylation data, cancer-related genes, mutation, cancer information and methylation status in CpG islands and promoter regions from multiple public sources. NGSMethDB provides sequence-based methylation data of six species samples in CpG islands and promoter regions obtained by NGS. DiseaseMeth is a human methylation database that pro-vides both locus-specific and high-throughput data sets in 72 human diseases. MENT was developed by collect-ing over 6000 samples from Gene Expression Omnibus (GEO) and TCGA, and provides regression and corre-lation analysis. MethHC collects DNA methycorre-lation and mRNA/microRNA expression profiles in 18 human nor-mal and tumor tissues. Illumina HumanMethylation450K BeadChip data includes more than 480 000 CpG sites. RNA-Seq and small RNA-Seq are applied to calculate the abundance of mRNA/microRNA expressions. To help investigators reveal new potential epigenetic biomarkers for cancer, MethHC provides additional functions and multiplicity information, including several different gene regions (promoter, TSS, untranslated region, exon, gene body and enhancer region), CGI regions (CpG island, shore and shelf) and estimate of the DNA methylation level methods (average, maximum and median).

INTERFACE

To facilitate access to data and further analyses for the iden-tification of differentially methylated genes, MethHC pro-vides a variety of interfaces and graphical visualizations. Users can utilize top 250 analysis to identify the 250 hyper-methylated and hypohyper-methylated genes, yielding genes which are respectively hypermethylated and hypomethylated in tu-mors. Figure2A provides a detailed view of the interface. Hierarchical clustering is probably the most widely used clustering method for the detection of co-regulated methy-lation genes and the identification of cancer subtypes. In the hierarchical clustering graph, genes and samples with sim-ilar DNA methylation patterns are grouped together (Fig-ure2B). In addition, MethHC can submit a group of inter-ested genes/miRNAs or the name of a KEGG pathway to identify differentially methylated genes in a set of tumors. A detailed view of each gene is demonstrated in the correla-tion of DNA methylacorrela-tion and gene expression. GSTM2 and PENK are hypermethylated in prostate cancer (3). Aber-rant DNA methylation of mir-31, mir-130a, let-7a-3/let-7b and mir-155 gene promoters in breast cancer is linked

to silencing of miRNA expression (20). Mir-9 family and mir-34 family were also reported to be silenced by DNA methylation in various cancer (28). We use PENK as a case study. Our system shows the differential methylation sta-tus of each transcript in selected tumors by boxplot (Fig-ure2C). The asterisk means significant differential methy-lation in selected tumors. The corremethy-lation between methyla-tion levels and expression status was shown by scatter plot (Figure2D).

CONCLUSIONS AND PERSPECTIVES

MethHC is an integrated and useful database comprising DNA methylation and mRNA/microRNA expression pro-files in 18 human normal and tumor tissues. We use var-ious textual and graphical interfaces to demonstrate the methylation patterns in normal and tumor tissues and to analyze the correlation between methylation and expres-sion. Genome-wide analysis reveals differentially methy-lated genes and gene clusters. MethHC also provides more multiplicity information, such as several different gene re-gions, CGI regions and estimate of the DNA methylation level methods, to facilitate further investigation of genomic methylation status.

To help investigators reveal new potential epigenetic biomarkers for cancer diagnosis and prognosis, prospective works of the proposed database are listed as follows: (i) to support more data resources from the GEO and the In-ternational Cancer Genome Consortium, (ii) to integrate clinical–pathological indicators from TCGA and (iii) to col-lect bisulfate sequencing data from recent TCGA releases. AVAILABILITY

The MethHC database will be continuously maintained and updated. The database is now publicly accessible athttp: //MethHC.mbc.nctu.edu.tw/.

ACKNOWLEDGEMENTS

The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially sup-porting this research. We also thank the UST-UCSD Inter-national Center of Excellence in Advanced Bio-engineering sponsored by the Taiwan Ministry of Science and Technol-ogy I-RiCE Program, Veterans General Hospitals and Uni-versity System of Taiwan (VGHUST) Joint Research Pro-gram, and MOE ATU. We would like to thank The Cancer Genome Atlas for use of data.

FUNDING

Ministry of Science and Technology of the Republic of China [NSC 101–2311-B-009–005-MY3 and NSC 102– 2627-B-009–001]. UST-UCSD International Center of Ex-cellence in Advanced Bio-engineering sponsored by the Taiwan Ministry of Science and Technology I-RiCE Pro-gram [NSC 102–2911-I-009–101]; Veterans General Hos-pitals and University System of Taiwan (VGHUST) Joint Research Program [VGHUST103-G5–1–2]. MOE ATU. Funding for open access charge: Ministry of Science and Technology of the Republic of China, Taiwan [NSC101-2311-B-009-005-MY3].

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/

Conflict of interest statement. None declared. REFERENCES

1. Zerbini,L.F. and Libermann,T.A. (2005) GADD45 deregulation in cancer: frequently methylated tumor suppressors and potential therapeutic targets. Clin. Cancer Res., 11, 6409–6413.

2. Das,P.M. and Singal,R. (2004) DNA methylation and cancer. J. Clin. Oncol., 22, 4632–4642.

3. Ashour,N., Angulo,J.C., Andres,G., Alelu,R., Gonzalez-Corpas,A., Toledo,M.V., Rodriguez-Barbero,J.M., Lopez,J.I.,

Sanchez-Chapado,M. and Ropero,S. (2014) A DNA hypermethylation profile reveals new potential biomarkers for prostate cancer diagnosis and prognosis. Prostate, 74, 1171–1182. 4. Laird,P.W. (2003) The power and the promise of DNA methylation

markers. Nat. Rev. Cancer, 3, 253–266.

5. Ballestar,E. and Esteller,M. (2002) The impact of chromatin in human cancer: linking DNA methylation to gene silencing. Carcinogenesis, 23, 1103–1109.

6. Amatya,V.J., Naumann,U., Weller,M. and Ohgaki,H. (2005) TP53 promoter methylation in human gliomas. Acta Neuropathol., 110, 178–184.

7. Nakamura,M., Yonekawa,Y., Kleihues,P. and Ohgaki,H. (2001) Promoter hypermethylation of the RB1 gene in glioblastomas. Lab. Invest., 81, 77–82.

8. Wang,Q., Williamson,M., Bott,S., Brookman-Amissah,N., Freeman,A., Nariculam,J., Hubank,M.J., Ahmed,A. and

Masters,J.R. (2007) Hypomethylation of WNT5A, CRIP1 and S100P in prostate cancer. Oncogene, 26, 6560–6565.

9. Vasiljevic,N., Ahmad,A.S., Beesley,C., Thorat,M.A., Fisher,G., Berney,D.M., Moller,H., Yu,Y., Lu,Y.J., Cuzick,J. et al. (2013) Association between DNA methylation of HSPB1 and death in low Gleason score prostate cancer. Prostate Cancer Prostatic Dis., 16, 35–40.

10. Kim,M.S., Lee,J. and Sidransky,D. (2010) DNA methylation markers in colorectal cancer. Cancer Metastasis Rev., 29, 181–206.

11. Carmona,F.J., Azuara,D., Berenguer-Llergo,A., Fernandez,A.F., Biondo,S., de Oca,J., Rodriguez-Moranta,F., Salazar,R.,

Villanueva,A., Fraga,M.F. et al. (2013) DNA methylation biomarkers for noninvasive diagnosis of colorectal cancer. Cancer Prev. Res., 6, 656–665.

12. Barton,C.A., Hacker,N.F., Clark,S.J. and O’Brien,P.M. (2008) DNA methylation changes in ovarian cancer: implications for early diagnosis, prognosis and treatment. Gynecol. Oncol., 109, 129–139. 13. Feinberg,A.P. (2010) Genome-scale approaches to the epigenetics of

common human disease. Virchows Arch., 456, 13–21. 14. Grunau,C., Renault,E., Rosenthal,A. and Roizes,G. (2001)

MethDB––a public database for DNA methylation data. Nucleic Acids Res., 29, 270–274.

15. Ongenaert,M., Van Neste,L., De Meyer,T., Menschaert,G., Bekaert,S. and Van Criekinge,W. (2008) PubMeth: a cancer methylation database combining text-mining and expert annotation. Nucleic Acids Res., 36, D842–D846.

16. He,X., Chang,S., Zhang,J., Zhao,Q., Xiang,H., Kusonmano,K., Yang,L., Sun,Z.S., Yang,H. and Wang,J. (2008) MethyCancer: the database of human DNA methylation and cancer. Nucleic Acids Res., 36, D836–D841.

17. Hackenberg,M., Barturen,G. and Oliver,J.L. (2011) NGSmethDB: a database for next-generation sequencing single-cytosine-resolution DNA methylation data. Nucleic Acids Res., 39, D75–D79. 18. Lv,J., Liu,H., Su,J., Wu,X., Liu,H., Li,B., Xiao,X., Wang,F., Wu,Q.

and Zhang,Y. (2012) DiseaseMeth: a human disease methylation database. Nucleic Acids Res., 40, D1030–D1035.

19. Baek,S.J., Yang,S., Kang,T.W., Park,S.M., Kim,Y.S. and Kim,S.Y. (2013) MENT: methylation and expression database of normal and tumor tissues. Gene, 518, 194–200.

20. Vrba,L., Munoz-Rodriguez,J.L., Stampfer,M.R. and Futscher,B.W. (2013) miRNA gene promoters are frequent targets of aberrant DNA methylation in human breast cancer. PLoS One, 8, e54398.

21. Laird,P.W. (2010) Principles and challenges of genomewide DNA methylation analysis. Nat. Rev. Genet., 11, 191–203.

22. Karolchik,D., Barber,G.P., Casper,J., Clawson,H., Cline,M.S., Diekhans,M., Dreszer,T.R., Fujita,P.A., Guruvadoo,L., Haeussler,M. et al. (2014) The UCSC Genome Browser database: 2014 update. Nucleic Acids Res., 42, D764–D770.

23. Chien,C.H., Sun,Y.M., Chang,W.C., Chiang-Hsieh,P.Y., Lee,T.Y., Tsai,W.C., Horng,J.T., Tsou,A.P. and Huang,H.D. (2011) Identifying transcriptional start sites of human microRNAs based on

high-throughput sequencing data. Nucleic Acids Res., 39, 9345–9356. 24. Jones,P.A. (2012) Functions of DNA methylation: islands, start sites,

gene bodies and beyond. Nat. Rev. Genet., 13, 484–492. 25. Irizarry,R.A., Ladd-Acosta,C., Wen,B., Wu,Z., Montano,C.,

Onyango,P., Cui,H., Gabo,K., Rongione,M., Webster,M. et al. (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet., 41, 178–186.

26. Ko,Y.A., Mohtat,D., Suzuki,M., Park,A.S., Izquierdo,M.C., Han,S.Y., Kang,H.M., Si,H., Hostetter,T., Pullman,J.M. et al. (2013) Cytosine methylation changes in enhancer regions of core pro-fibrotic genes characterize kidney fibrosis development. Genome Biol., 14, R108.

27. Kanehisa,M. and Goto,S. (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 28, 27–30.

28. Suzuki,H., Maruyama,R., Yamamoto,E. and Kai,M. (2012) DNA methylation and microRNA dysregulation in cancer. Mol. Oncol., 6, 567–578.

at Osaka Daigaku Ningen on February 10, 2015

http://nar.oxfordjournals.org/