Genome-wide DNA methylation profiling of non-small cell lung carcinomas
- Rejane Hughes Carvalho1, 2,
- Vanja Haberle3,
- Jun Hou1, 4,
- Teus van Gent1, 4,
- Supat Thongjuea3,
- Wilfred van IJcken5,
- Christel Kockx5,
- Rutger Brouwer5,
- Erikjan Rijkers5,
- Anieta Sieuwerts6,
- John Foekens6,
- Mirjam van Vroonhoven5, 7,
- Joachim Aerts8,
- Frank Grosveld1, 2, 4, 9,
- Boris Lenhard3Email author and
- Sjaak Philipsen1, 2, 4Email author
© Hughes Carvalho et al.; licensee BioMed Central Ltd. 2012
Received: 3 April 2012
Accepted: 22 June 2012
Published: 22 June 2012
Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis of NSCLC samples and paired lung tissues, where we combine MethylCap and next generation sequencing (MethylCap-seq) to provide comprehensive DNA methylation maps of the tumor and paired lung samples. The MethylCap-seq data were validated by bisulfite sequencing and methyl-specific polymerase chain reaction of selected regions.
Analysis of the MethylCap-seq data revealed a strong positive correlation between replicate experiments and between paired tumor/lung samples. We identified 57 differentially methylated regions (DMRs) present in all NSCLC tumors analyzed by MethylCap-seq. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. We also identified DMRs that were specific to two of the major subtypes of NSCLC, adenocarcinomas and squamous cell carcinomas.
Collectively, we provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC.
KeywordsDNA Methylation Epigenetics MethylCap Next generation sequencing Non-small cell lung Cancer
Non-small cell lung carcinoma (NSCLC) is a common malignancy characterized by a worldwide high incidence and low survival rate . NSCLC is a heterogenic disease which is broadly classified into three major histopathological subtypes: adenocarcinoma (ADC), squamous cell carcinoma (SCC) and large cell carcinoma (LCC). This heterogeneity poses challenges for diagnosis and treatment, since each subtype presents with a distinctive prognosis  and the choice of therapeutic regimen is predominantly based on tumor subtype and staging parameters . The development of personalized diagnostics and therapy is leading the way to a new era that may see us overcome some of the difficulties in treating complex diseases such as NSCLC.
In the past decade, comparative gene expression profiles of tumors have been extensively studied [4–6], yielding useful insights into the molecular hallmarks of carcinogenesis [7, 8]. With the advent of next generation sequencing, genome-wide screening has become an attractive tool for profiling tumors versus lung tissues [7, 9]. DNA methylation is a very stable epigenetic mark and next generation sequencing studies have recently shown that many genes are aberrantly methylated in cancer [10, 11]. Tissue specific DNA methylation patterns are stabilized during embryonic development, and faithfully maintained through cell divisions [12–14]. Nevertheless, established methylation patterns can be reprogrammed, with tumor cells undergoing DNA demethylation and de novo methylation through mechanisms not yet completely understood. CDKN2A and RASSF1 are examples of genes found to be aberrantly methylated in a wide variety of tumors [15–18], and epigenetic silencing of these genes has also been reported in NSCLC [19–22]. The great majority of DNA methylation studies are concentrated on the analysis of CpG islands located in the promoter areas of pre-selected genes. However, differentially methylated areas may be located within genes and at large distances from the nearest neighboring genes [23, 24]. Although data on methylated genes in NSCLC are rapidly accumulating, unbiased data concerning specificity of the genome-wide distribution of methylated loci are still scarce.
In this study, we used Methyl-DNA Capture (MethylCap) and high-throughput sequencing (MethylCap-seq, ) to perform a genome-wide DNA methylation screening of NSCLC tumors and paired adjacent lung tissues. With this approach, we sought to identify genome-wide aberrant methylation patterns of NSCLC. Specific differentially methylated regions would be promising candidate molecular markers for non-invasive diagnostics using circulating tumor DNA, and increase the number of possible targets for epigenetic therapy.
Methylation profiles in NSCLC-study outline
Data from patients used for MethylCap-seq and bisulfite sequencing validation
Overall survival (month)
Age at diagnosis
Global analysis of genome-wide methylation patterns of NSCLC
The DMRs were analyzed according to general hallmarks of their genomic localization (Figure 2E). Compared with the general distribution of randomly sampled regions across the genome (label “Random regions”), we found that promoter areas were particularly enriched among the DMRs, both hypo- (P=5.7 × 10-3) and hypermethylated (p=5.3 × 10-4). Although we removed most signals from repetitive regions by keeping only uniquely mapped reads, the signals from their uniquely mappable reads and flanking regions were still above background and allowed us to test for differential methylation. This revealed that some specific repeat classes such as LINE and satellites were also differentially represented in the DMRs, with satellite repeats being particularly hypomethylated in tumors when compared with lung tissues (P=1.3 × 10-5). By contrast, LINE repeats are relatively underrepresented in the DMRs (hypo and hypermethylation), indicating that the methylation status of the bulk of the LINE elements was similar between the tumors and paired lung counterparts.
Differentially methylated regions in NSCLC
We found 57 DMRs present in all tumor samples, of which 50 were hypermethylated and 7 were hypomethylated. An example of two DMRs in all samples, including the replicate experiment can be found in Additional file 10. We also analyzed DMRs in relation to NSCLC subtypes. We found 287 DMRs unique for SCC and 26 DMRs unique for ADC (Additioonal File 11). These DMRs were classified by hyper- and hypomethylation and their genomic locations relative to genes (Figure 3B). We found that the ADC-specific DMRs had a genomic distribution similar to that observed for the DMRs present in all seven tumors, while for SCCs, DMRs within genes were concentrated in the gene body. SCC also presented considerably more subgroup-specific DMRs than ADC and ‘All-sample’; this could be explained by the notion that SCC is a relatively homogeneous tumor type.
For the DMRs that fall in gene areas, we analyzed which types of genes were present in each of the three groups (Figure 3C and Additional file 11). We found that for the DMRs present in all tumor samples, more than 75% of the associated genes belonged to the class of transcriptional regulators. For SCC-specific DMRs, we observed a considerable heterogeneity in the functional categories of associated genes. Nevertheless, there was an overrepresentation of genes involved in transcriptional regulation, organization of the cytoskeleton and cell cycle regulation. We note that while some of the DMRs were associated with genes that had been previously reported to be differentially methylated in lung tumors, such as APC CDH13 CDKN2A DAPK hMLH1 HOX genes, OTX1 HOX2 and many others [22, 29–32], we also found DMRs which, to the best of our knowledge, have not been previously reported to be associated with methylation in NSCLC or other types of cancer.
Validation of differentially methylated regions in NSCLC by bisulfite sequencing
To assure sufficient depth of coverage for quantitative analysis, we sequenced 36 individual clones from each fragment and each bisulfite-converted DNA sample. Figure 4B shows an example of the methylation status for one fragment of a paired sample. The methylation status of the five fragments in all seven paired NSCLC/lung tissue samples is summarized in Figure 4C; full data for all samples is available in Additional files 12 and 13. We also examined potential differences in methylation according to tumor histology. To perform this analysis we grouped the samples in four categories: all clones from the lung tissues of patients (ADC_N and SCC_N) and clones from tumors (ADC_T and SCC_T), (Figure 4D and Additional file 14). We observed a statistically significant difference (P≤ 0.0001), not only between tumors and lung tissues, but also between ADC and SCC for all fragments analyzed by bisulfite sequencing. This identifies these fragments as candidate markers for differentiating tumors versus paired lung tissues, and also between tumor subtypes. Moreover this finding validates the MethylCap-seq data, since the methylation status determined by bisulfite sequencing correlated quantitatively with the MethylCap-seq data (Figure 4E).
Screening by methylation-specific PCR verifies methylation differences between tumor and paired lung tissues, and between tumor subtypes
Frequency of methylation by methylation-specific polymerase chain reaction in the 96 samples (48 tumor and matching lung tissue) in five fragments
Squamous cell carcinoma
MSP is a more qualitative approach than bisulfite sequencing. In MSP, selective amplification depends on the methylation status of only the CpGs present in the designed primers, which were two or three CpGs depending on the fragment. For each methylated (M) primer set, amplification of the fragment is dependent on methylation of the CpGs that are present in the sequence targeted by the “M” primer; while each unmethylated (U) primer set requires unmethylated CpGs in the referred sequence for amplification. In the 48 paired samples analyzed, CDKN2A showed frequent amplification with the M primer set in lung samples (24 out of 48), although the amplified bands were often of low intensity. Tumors had a much larger number displaying amplification with both M and U primer sets. Of the 47 tumors, 36 displayed amplification with the M primer set, and two of the tumors displayed amplification only with the M primer set. For RASSF1 and ZIC4, 41 paired samples were available. Both fragments differentiated between lung and tumor samples, whereas no methylation was detected in lung samples. ZIC4 showed methylation in more than 70% of the tumors, which is much higher than the 41% found for RASSF1. The findings for RASSF1 are in accordance with other studies . Also, Frag_01 presented no amplification with the M primer set in the lung samples, in the Frag_02 M primer set only 5 out of 48 were amplified. In tumors, we observed fragment amplification in 29out of 48 samples for Frag_01 and 36out of 48 samples for Frag_02 with the M primer sets. Differences in methylation status were significant in tumors versus lung samples for all 5 fragments, with a p-value < 0.02 for CDKN2A and P < 0.0001 for the other four fragments (Figure 5B). Frag_01, Frag_02 and ZIC4 present a much lower P-value, indicating they may be better tumor markers than CDKN2A and RASSF1. We also observed that significant differences between SCC and ADC were only found for Frag_01 (Figure 5C). In conclusion, the MSP results validate the MethylCap-seq data, since the results obtained for all five fragments were consistent with the significant DMRs in all tumors, and Frag_01 appeared as part of the SCC-specific DMRs in the MethylCap-seq data.
To date, genome-wide methylation studies in NSCLC have concentrated on specific areas of the genome , promoter regions [34–36] or sets of pre-selected candidate genes [19, 37]. Recently Kwon et al.  published a report on genome-wide analysis of DNA methylation in NSCLC. However, they also concentrate the analysis on a selection of candidate genes. In our study, we performed an unbiased genome-wide DNA methylation analysis of seven individual patients with NSCLC and their adjacent lung tissues. We combined MethylCap with high-throughput sequencing (MethylCap-seq) to draw detailed methylome maps of NSCLC tumors and paired lung tissues. The replicate experiments showed that the depth of coverage generated by MethylCap-seq was sufficient to capture the methylation status of the entire genome. Moreover, there was a very high degree of correlation between the replicate experiments, demonstrating the robustness of the MethylCap-seq approach. As controls for normalization and validation purposes we made use of fully methylated and fully unmethylated DNA samples. The MethylCap-seq data of these controls provide a useful resource for benchmarking of the fully methylated versus fully unmethylated status of any particular fragment in the genome. Using the MEDIPS computational tool , we generated methylation profiles of the seven paired NSCLC/lung cases which can be viewed in the genome browsers. This broad view of DNA methylation in NSCLC will provide new opportunities for the identification of specific epigenetic markers of NSCLC that could, for instance, be used for early detection of the disease.
The occurrence of methylated CpGs in the fragments selected for bisulfite sequencing showed a high quantitative correlation with the reads found by MethylCap-seq in the same area. In conjunction with the MethylCap-seq data on the fully methylated and fully unmethylated controls, this demonstrates that MethylCap-seq yields reliable quantitative information on the methylation status of the fragments in a particular DNA sample. This is important considering that the tumor cell content of samples to be analyzed will be variable. Provided that sufficient sequencing depth is achieved, it should be possible to use MethylCap-seq for the identification of tumor-specific hypermethylated DNA regions in samples containing only a minority of tumor cells, and possibly in circulating tumor DNA isolated from serum samples. The results obtained with MSP are qualitative in nature, but this PCR-based approach is very sensitive and may therefore be used to detect tumor-specific hypermethylated DNA fragments in serum DNA. As a first step in this direction, we used MSP to investigate the presence of five DMRs in a larger set of paired samples. The results confirmed the MethylCap-seq data and demonstrated that these DMRs are frequently hypermethylated in NSCLC. In particular, the Frag_01 fragment was found to be hypermethylated in 13 out of 16 SCC cases and in none of the ADC, suggesting that this might be a useful marker for this type of NSCLC, and ZIC4 showed 100% specificity for tumors and higher sensitivity (>70%) than RASSF1. In addition, CDKN2A amplification with the M primer was often observed in lung tissue samples, although at much lower amplification levels when compared with tumor samples. This could reflect an early event in the methylation status of CDKN2A in tumorigenesis.
The mapped reads point to differentially methylated areas across the genome, with some marked preferences for hypermethylation or hypomethylation in certain genomic regions. There is a strong correlation between CpG islands and hypermethylation in tumors, with the most significantly hypermethylated regions associated with promoter regions of genes. As previously reported by many groups [40–43], gene promoters are a target of methylation as an epigenetic regulatory mechanism. However, our data also reveal a high degree of hypermethylation outside promoter areas at intergenic regions, and at long distances from genes. This indicates that patterns of DNA methylation may play a role not only by silencing promoters of key tumor suppressor genes, but also by regulating gene expression in a more complex manner through distant regulatory elements such as insulators and enhancers.
We found that satellite regions were particularly hypomethylated in the NSCLC tumors when compared with lung tissues. Recently, Ting et al.  have shown that overexpression of satellite repeats is associated with different types of cancer. The observed hypomethylation of these regions in NSCLC would be compatible with their results. We also found that hypomethylated regions are often present in subtelomeric regions, in agreement with the observation that hypomethylation is more frequently located near chromosome ends in lung cancer . While CpG islands and genes are present in the subtelomeric regions of chromosomes, the impact of subtelomeric hypomethylation on the regulation of these genes is currently unclear.
In an alternative approach, we selected the most significant DMRs in the individual NSCLC cases (14,742) and performed unsupervised cluster analysis of the hyper- and hypomethylated regions in the tumor samples. We observed that the samples clustered according to their histological classification. This indicates that there are DMRs that could not only be used as NSCLC markers but also as markers for histological classification of NSCLC tumors. Subsequently we searched for the specific areas that were differentially methylated in all tumors when compared with the paired lung tissues, and specific for the subtypes. We observed that SCCs displayed more DMRs than did ADCs did. This observation could be explained by the heterogeneity of ADCs, which would result in fewer DMRs shared by all three ADC samples. The contrary is seen in SCC, which is a more homogeneous type of NSCLC. This characteristic has been observed previously in gene expression profiling studies [4, 46].
Remarkably, the majority (>75%) of the hypermethylated genes present in all seven samples encoded for transcription factors, while the shared hypomethylated genes were spread among many different functional categories. Transcription factors play a central role in maintaining or modifying cell fate, either in normal processes such as development and cell differentiation, or in cancer. Mechanisms of epigenetic regulation via DNA methylation are not yet completely understood; several factors such as DNA methyltransferases, chromatin remodelling proteins and DNA-binding transcription factors are involved [47, 48]. Therefore, any perturbation that leads to a decrease in the expression status of those factors may disrupt important pathways for maintenance of the differentiated cellular state. Aberrant methylation can change chromatin structure, rendering DNA binding sites accessible or inaccessible to transcription factors leading to activation or silencing of genes important in cell differentiation and maintenance. It has been recently shown that hypermethylated genes found in lung tumors are associated with cellular morphogenetic differentiation . These cellular mechanisms are orchestrated by transcriptional regulators; therefore changes in the methylation status of their binding sites may disrupt those processes thus contributing to oncogenesis.
List of most differentially methylated genes in non-small cell carcinoma
Gene class/type/ biological function
Common in all tumors
BARX homeobox 1
paired box 9
orthodenticle homeobox 1
G-protein coupled receptor
natriuretic peptide receptor C/guanylate cyclase C (atrionatriuretic peptide receptor C)
fibroblast growth factor 12
one cut homeobox 2
PR domain containing 14
retina and anterior neural fold homeobox
short stature homeobox 2
DMRT-like family A2
fer-1-like 4 (Caenorhabditis. elegans) pseudogene
SIX homeobox 6
GATA binding protein 3
SKI familytranscriptional corepressor 1
sal-like 1 (Drosophila)
iroquois homeobox 2
glutamate receptor, ionotropic, kainate 2
SATB homeobox 2
Meis homeobox 1
ventral anterior homeobox 1
cyclin N-terminal domain containing 2
zinc finger, MYND-type containing 10
v-myc myelocytomatosis viral oncogene homolog (avian)
neuron navigator 1
cytoplasmic polyadenylation element binding protein 3
Unique for squamous cell carcinomas
trafficking protein particle complex 9
abhydrolase domain containing 2
catenin (cadherin-associated protein), delta 1
histone cluster 1, H2bb
epithelial membrane protein 1
transducin (beta)-like 1 X-linked receptor 1
Zic family member 4
acyloxyacyl hydrolase (neutrophil)
actinin, alpha 4
chromosome 1 open reading frame 21
protein kinase C and casein kinase substrate in neurons 2
DOT1-like, histone H3 methyltransferase (Sacchormyces. cerevisiae)
e3 ubiquitin-protein ligase
WW domain containing E3 ubiquitin protein ligase 2
generaltranscription factor IIIC, polypeptide 1, alpha 220 kDa
MDN1, midasin homolog (yeast)
death inducer-obliterator 1
histone cluster 1, H3c
ankyrin repeat domain 13B
calcitonin-related polypeptide beta
protein tyrosine phosphatase, receptor type, A
signal transducer and activator of transcription 5A
LIM domain kinase 1
solute carrier family 23 (nucleobase transporters), member 2
BARX homeobox 1
Nance-Horan syndrome (congenital cataracts and dental anomalies)
forkhead box K1
protein-L-isoaspartate (D-aspartate) O-methyltransferase
SET domain containing 1A
centromere protein P
enhances neuronal dendrite outgrowth
SLIT and NTRK-like family, member 1
RAR-related orphan receptor B
postsynaptic scaffold in neuronal cells
discs, large (Drosophila) homolog-associated protein 1
chromosome 3 open reading frame 21
ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N-acetylgalactosaminide alpha-2,6-sialyltransferase 1
Zic family member 3 (odd-paired homolog, Drosophila)
transmembrane protein 132D
microtubule-associated protein 1 light chain 3 beta 2
chibby homolog 3 (Drosophila)
Ras interacting protein 1
protein kinase, cGMP-dependent, type I
WD repeat domain 72
potassium voltage-gated channel, KQT-like subfamily, member 2
G-protein coupled receptor
BAI1-associated protein 3
mitogen-activated protein kinase kinase kinase 10
Unique for Adenocarcinomas
family with sequence similarity 78, member B
growth arrest-specific 1
protein tyrosine phosphatase, receptor type, N polypeptide 2
Wnt receptor signaling pathway
R-spondin 2 homolog (Xenopus laevis)
POU class 3 homeobox 3
transient receptor potential cation channel, subfamily A, member 1
solute carrier family 6 (neurotransmitter transporter, noradrenalin), member 2
LIM homeobox 1
small GTPase mediated signal transduction
Rap guanine nucleotide exchange factor (GEF) 5
growth differentiation factor 10
chromosome 3 open reading frame 45
slit homolog 2 (Drosophila)
zinc finger protein 423
actin filament organization
ras homolog gene family, member F (in filopodia)
Samples were obtained from the patients with NSCLC (n = 48) who had undergone surgical lung resection at Erasmus University Medical Center Rotterdam. Specimens were collected from the tumor and adjacent non-cancerous lung tissue and studied under an anonymous tissue protocol approved by the medical ethical committee of Erasmus University Medical Center Rotterdam. Tissues were snap-frozen within two hours after surgical resection in liquid nitrogen pre-cooled isopentane, and stored at 196 °C or 80 °C until DNA extraction.
Patient samples were independently reviewed by two pathologists. The cohort included 17 with ADC; 16 with SCC, seven with LCC , eight unclassified samples, and paired lung tissues for each tumor sample.
The MRC-5 lung fibroblast-like cell line was used as control. Cells were cultured under standard conditions using minimum essential medium supplemented with 10% heat-inactivated fetal bovine serum, 2 mM L-glutamine, 1% non-essential amino acids and penicillin/streptomycin. Cells were harvested when they reached 90% confluence.
Genomic DNA from patient tissues and cultured cells were extracted by overnight treatment incubation with lysis buffer and proteinase K, followed by phenol-chloroform extraction, ethanol precipitation and RNase digestion.
Artificial demethylation and methylation of genomic DNA
We used DNA extracted from the MRC-5 cell line and commercially available Universal unmethylated DNA (UUD; Millipore(Billerica, MA, USA). Fully unmethylated DNA was obtained by whole-genome amplification using the REPLI-g kit (QiagenQiagen – Germantown, MD, USA) according to manufacturer protocol, followed by phenol-chloroform extraction. Fully methylated DNA was prepared by treating MRC-5 DNA and UUD with M.SssI enzyme (New England Biolabs - Ipswich, MA, USA) according to the manufacturer’s protocol. In short, 10 μg of DNA was incubated for 2 hours at 37°C with 40 U of M.SssI and 640 μM of S-adenosylmethionine. DNA was then treated with phenol-chloroform and recovered by ethanol precipitation.
Enrichment of methylated DNA was carried out using the MethylCap technique. DNA samples were sheared by sonication to obtain fragments between 200 and 800 bp. Methylated DNA capture was carried out using an adapted protocol from the MethylMiner Methylated DNA Enrichment kit (Invitrogen – Carlsbad, CA, USA). Two adaptations were made. Firstly, 20 μL of beads and 14 μL (7 μg) of MBD-biotin protein were used for 5 μg of sheared genomic DNA. Secondly, a single elution fraction was obtained by resuspending the beads in 200 μL of 1X binding/washing buffer containing 2 μL of proteinase K (20 mg/ml). The samples were then incubated for 90 minutes at 57 °C with 800 rpm agitation. Remaining steps were performed following the manufacturer’s protocol scaled up to 5 μg of DNA. Before the samples were used for high-throughput sequencing, methyl-enrichment was tested by quantitative (q) PCR (data not shown). qPCR was carried out using SYBR r green, Phire Hot Start DNA polymerase (Finnzymes - Vantaa, Finland)), and 1 μL of the enriched samples. Primer sequences are available in Additional file 16. qPCR was performed in triplicate and enrichment levels were calculated as previously described .
High-throughput sequencing (HTS) was carried out on 18 samples: seven tumors (three ADC, three SCC and one LCC), seven paired lung tissues and four controls (two artificially fully methylated DNA and two fully unmethylated DNA). For each sample, DNA recovered from two independent Methyl Capture experiments was sequenced. The Illumina Genome Analyzer IIx platform (San Diego, CA, USA was used for both replicates, one sample per lane, according to the manufacturer’s protocol. In short: after MethylCap, fragments of the methyl-enriched fraction were end-repaired and ligated to single-read adaptors. Samples were size-selected to an average of 320 bp, PCR-amplified and 36 bp sequenced.
The Illumina Casava pipeline was used for base calling, alignment and quality control. Results were mapped against the reference Human_UCSChg18_AllChromosomes using eland_extended by Illumina pipeline 1.6.0.
Detailed information about the evaluation and selection of normalization parameters, and identification, characterization and selection of DMRs can be found in Additional file 3.
Bisulfite sequencing was carried out on the same 16 samples used for MethylCap HTS. Detailed information is provided in Additional file 3. In short: bisulfite conversion was carried out using the Epitect kit from Qiagen following the manufacturer’s protocol. Samples were amplified using primers specific for the fragments of interest ( Additional file 16). Amplified fragments were loaded on 2% agarose gels and extracted from the gel using a NucleoSpin Extract II kit (Machery-Nagel – Düren, Germany). Fragments were then ligated to pGEM-t easy vector (Promega - Madison, WI, USA) and cloned into DH12-β competent cells. Colony PCR was performed on 36 colonies and PCR fragments sequenced. Analysis of methylated and unmethylated CpGs was executed using two online programs: BISMA (http://biochem.jacobs-university.de/BDPC/BISMA) and QUMA (http://quma.cdb.riken.jp).
To determining the methylation status of the selected regions, we used MSP  in 96 samples (48 tumors and matching lung tissues). Samples were bisulfite converted as described for bisulfite sequencing, and amplified using two different set of primers designed for the methylated and unmethylated sequences ( Additional file 16). The fully methylated and fully unmethylated DNA samples were used as controls, and a water blank reaction used as control for contamination. After amplification, products were resolved on 2% agarose gels containing ethidium bromide and visualized under UV transillumination.
Statistical analysis for bisulfite sequencing was provided by the BISMA program. In short, Fisher’s exact test was used for the statistical significance between two bisulfite sequence groups at each CpG, and a Mann-Whitney U-test for the statistical significance between two groups of the entire set of CpG sites. Two-tailed P-value of Fisher’s exact test was calculated from the 2 × 2 tables at each CpG site. A Mann-Whitney U-test was used for the statistical significance of the entire set of CpG sites between the tumor and lung groups. For determination of significance in the MSP, we used chi-square distribution. Two-tailed P-values were determined by the counts of the amplified bands for each primer in the group of samples lung/tumor or SCC/ADC. Groups that showed a P-value < 0.05 were considered significantly different.
differentially methylated region
large cell carcinoma
long interspersed element
non-small cell lung carcinoma
polymerase chain reaction
squamous cell carcinoma
Universal unmethylated DNA
We thank Peter Riegman and Monique Oomen for providing patient samples stored in the Erasmus MC tissue bank, and Ilya Grigorev for the MRC-5 cell line. We thank Nynke Gillemans for technical assistance and Thamar van Dijk, Cíntia Bombardieri, Ricardo Leite, Vojka Gouka and Marco Gouka for helpful discussions during the preparation of this manuscript. We also thank Alex Pindyurin for this help with the R script. This work was supported by the Netherlands Genomics Initiative.
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