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PLoS One. 2012; 7(6): e39813.
Published online 2012 Jun 29. doi:  10.1371/journal.pone.0039813
PMCID: PMC3387223

Methylation Markers of Early-Stage Non-Small Cell Lung Cancer

Alfons Navarro, Editor



Despite of intense research in early cancer detection, there is a lack of biomarkers for the reliable detection of malignant tumors, including non-small cell lung cancer (NSCLC). DNA methylation changes are common and relatively stable in various types of cancers, and may be used as diagnostic or prognostic biomarkers.


We performed DNA methylation profiling of samples from 48 patients with stage I NSCLC and 18 matching cancer-free lung samples using microarrays that cover the promoter regions of more than 14,500 genes. We correlated DNA methylation changes with gene expression levels and performed survival analysis.


We observed hypermethylation of 496 CpGs in 379 genes and hypomethylation of 373 CpGs in 335 genes in NSCLC. Compared to adenocarcinoma samples, squamous cell carcinoma samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes. 378 of 869 (43.5%) CpG sites discriminating the NSCLC and control samples showed an inverse correlation between CpG site methylation and gene expression levels. As a result of a survival analysis, we found 10 CpGs in 10 genes, in which the methylation level differs in different survival groups.


We have identified a set of genes with altered methylation in NSCLC and found that a minority of them showed an inverse correlation with gene expression levels. We also found a set of genes that associated with the survival of the patients. These newly-identified marker candidates for the molecular screening of NSCLC will need further analysis in order to determine their clinical utility.


Lung cancer is the leading cause of cancer-related deaths in the world. Epigenetic events are early and frequent in carcinogenesis [1], [2], [3], which makes DNA methylation an attractive biomarker for cancer. Epigenetic events could also provide a tractable link between the genome and the environment, with the epigenome serving as a biochemical record of relevant life events, e.g. cigarette smoking [4].

Lung cancer is morphologically divided into non-small cell and small cell lung cancer (NSCLC and SCLC). NSCLC accounts for about 80% of the lung cancers and is a heterogeneous clinical entity with major histological subtypes such as squamous cell carcinoma (SCC), adenocarcinoma (AC) and large cell carcinoma [5]. A common feature of the different subtypes of NSCLC is the somewhat slower growth and spread compared to SCLC, enabling surgical eradication in its early stages. Only a minor fraction of NSCLC cases are currently diagnosed in clinical stages I to IIb, where surgical removal is the therapy of choice.

The biomarker-driven approach at preinvasive phases could aid in diagnosing or ruling out lung cancer. Current markers, including squamous cell carcinoma antigen, carcinoembryonic antigen, cytokeratin 19 fragment antigen 21-1 and neuron-specific enolase were shown to lack satisfactory diagnostic power. In a recent study, only 37.3% of early-stage lung cancers could be diagnosed using the combination assays of the above-mentioned tumor markers [6].

Our study was aimed at the genome-wide identification of DNA methylation-based biomarker candidates in early-stage NSCLC. DNA methylation occurs vastly in the context of cytosine-guanine dinucleotides (CpGs) [7]. CpG-rich short stretches (CpG islands) are usually located in the promoter region of genes and are normally kept in the demethylated state [8]. In cancer, CpG islands located in the promoter area of tumor suppressor genes and “house-keeping” genes become hypermethylated, which can lead to decreased expression of these genes. At the same time, the genome is globally demethylated, which in turn can lead to the activation of oncogenes [9], [10]. Methylation of CpG island shores – regions with lower CpG density within approximately 2 kb of CpG islands - is also closely associated with transcriptional inactivation [11].

Recently, significant progress has been made in the genome-wide DNA methylation analysis. The methods include bisulfite conversion of DNA, immunoprecipitation or affinity purification of methylated DNA followed by microarray analysis or high-throughput sequencing [12]. It has been shown that in terms of accuracy, bisulfite-based methods perform slightly better than enrichment-based methods and do not require a statistical correction for CpG bias [13].

We have performed a genome-wide DNA methylation study of stage I NSCLC to obtain an insight into early-stage epigenetic alterations in lung cancer and identify potential diagnostic or prognostic biomarkers. In our study we used the HumanMethylation27 BeadChips (Illumina, Inc) that enable cost-effective quantitative comparisons across many samples.


CpG Methylation Analysis

Overall, we detected 496 CpGs in 379 genes hypermethylated and 373 CpGs in 336 genes hypomethylated in NSCLC (Figure S1, Table S1). A heatmap of the 100 most differently methylated genes in NSCLC compared to control samples is shown in Figure 1.

Figure 1
Differential DNA methylation between NSCLC and normal lung samples.

Compared to AC samples, SCC samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes (Figure S2, Table S2).

Two DNA samples (IDs 33 and 107) were processed in duplicate as an internal control of the assay’s reproducibility. Pearson’s correlation coefficient for both duplicates was 0.998. Methylation levels determined by the Infinium assay were validated using Sanger sequencing of bisulfite-converted DNA for 11 CpGs (six CpGs from NSCLC versus control samples analysis and five CpGs from survival analysis). The mean correlation between two methods was 83% (range 48.9%; 97.4%) (Figure S3).

The CpG sites analyzed by HumanMethylation27 assay are located within 1.5 kb upstream to 1 kb downstream of the TSS of their respective genes. We found a significant difference in the location of hypermethylated vs. hypomethylated CpGs relative to the closest TSS (p = 0.0001, Welch Two Sample t-test). Hypermethylated CpGs were preferentially located at TSSs, 3′ downstream of the TSS or in CpG islands. In contrast, hypomethylated CpGs were frequently found 5′ upstream of the respective TSSs or in CpG island shores [11] (Table 1, Figure 2).

Figure 2
CpGs’ distance from TSS.
Table 1
Distance of differentially methylated CpGs to the nearby transcription start sites (TSS, FDR corrected p<0.05, mean difference in methylation level in NSCLC vs tumor-free lung at least 13.6%).

Different CpGs of HSPA12B, PABPC5 and TP73 were either hyper- or hypomethylated in NSCLC. In the TP73 gene, the hypermethylated CpG site in the tumor sample was located upstream of the TSS of the full-length mRNA isoform. Hypomethylated TP73 CpGs were located close to the TSSs of the shorter isoforms. Hypermethylated CpGs of the HSPA12B and PABPC5 were located in their 5′ CpG islands, whereas hypomethylated CpGs were located upstream, outside the CpG islands.

Gene Expression Microarray Validation by qRT-PCR

Gene expression array setup and results are reported in the recent paper [14]. 10 genes and eight sample pairs were used to validate microarray results, using qRT-PCR technology. All the genes showed the same direction of over- or underexpression in the lung cancer samples, using both technologies (Figure 3). Seven genes showed a significant correlation between the expression fold-changes determined by qRT-PCR and microarray (p<0.05, R >0.7) (Figure S4).

Figure 3
The concordance between microarray and qRT-PCR measurements.

Methylation related to Gene Expression Changes

Using Pearson’s correlation analysis, we were able to determine the expected inverse correlation between the differential methylation levels and gene expression values for 378 (43.5%) of the 869 differentially methylated CpGs between NSCLC and control samples. In different histological groups we were able to find 337 of 780 CpGs (43.2%), the methylation levels of which were inversely correlated to the gene expression levels.

We performed a qPCR analysis of the different TP73 isoforms to test whether differential methylation next to the TSSs of different isoforms affects their expression level. The qPCR analysis did not reveal a statistically significant difference (p-value 0.36, paired t-test) between the expression levels of the two isoforms in our tumor samples.

Ingenuity Pathway Analysis

We performed in silico functional and interaction analyses of the differentially methylated genes using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA), and found 78 network eligible genes and 451 Functions/Pathways eligible genes. By including the known direct and indirect interactions, the most prominently represented gene network was related to tumor necrosis factor (TNF, Figure S5). Most of the genes (n = 22) in the network were hypomethylated, but some genes (n = 7) were also hypermethylated.

DNA Methylation related to Smoking Behavior

Based on tobacco smoking pack-years data, we asked whether smoking affects the DNA methylation patterns in a tumor. One patient who lacked smoking data was excluded. Linear regression analysis was performed using the Bioconductor Limma package. Analysis within tumor samples did not show any differentially methylated genes related to the extent of tobacco smoking. Comparing the limited number of non-smokers (n = 3, 6.4%) with smokers (n = 44, 93.6%) we found four differentially methylated CpG-sites in three genes (p<0.05, FDR adjusted), which are all hypomethylated in smokers group: CXorf38, MTHFD2 and TLL2.

Altered Methylation and Long-term Survival

We performed two types of survival analyses to find potential prognostic methylation markers. The patients with only up to one month survival after surgery (n = 2) were excluded to avoid any potential confounding influence of postoperative complications.

Firstly, we performed a Kaplan-Meier survival analysis on each of the CpG sites by dividing the Beta values into low, medium and high methylation groups. We only report results where all groups were larger than five patients. As a result, we found 10 CpGs in 10 genes, with methylation level differences in different survival groups (Figure 4). Patients with a medium methylation level of UGT1A7, GPR171, P2RY12, FLJ35784 and C20orf185 had better survival than patients with high-level methylation. Patients with a medium methylation level of CLEC11A and GRIK3 had better survival compared to low-level methylation. Patients with a low methylation level of CYP1A1 and INGX had better survival than those with medium-level methylation. Patients with a high methylation level for PIK3R5 had better survival than those with medium-level methylation.

Figure 4
Survival curves of 10 differentially methylated CpG sites.

Secondly, we performed the differential methylation analysis by combining Cox proportional hazard analysis and Wilcoxon rank-sum test. We found 18 CpGs in 15 genes in patients with 1 to 24 months survival (n = 12) vs. patients with 60 months and longer survival (n = 15), p<0.05, and the methylation difference cut-off applied. From the differentially methylated genes, DXS9879E (LAGE3), RTEL1 and MTM1 were hypermethylated, and SCUBE3, SYT2, KCNC3, KCNC4, GRIK3, CRB1, SOCS2, ACTA1, ZNF660, MDFI, ALDH1A3 and SRD5A2 were hypomethylated in the group with poor survival (Figure S6).


We have performed a genome-wide DNA methylation study in 48 stage I NSCLC patients and 18 macroscopically cancer-free control samples by using cluster analysis to search for genes that distinguish between the cancerous and normal lung tissue and compared these genes’ methylation levels with their expression levels. In addition, we performed in silico functional and interaction analysis of differently methylated genes using IPA software. Linear regression was used to find genes related to smoking, and Kaplan-Meier survival analysis was performed to identify the differential methylation of genes related to patient survival.

As a result, we detected 496 CpGs in 379 hypermethylated genes and 373 CpGs in 336 genes that were hypomethylated in NSCLC. A perfect separation of the control lung tissue samples from NSCLC samples was not obtained, as one normal lung sample clustered together with the cancer samples and six cancer samples (one in replicate) showed methylation patterns somewhat resembling the tumor-free lung tissue. Since we used non-dissected tumor samples, this finding may be at least partially caused by the confounding effect of non-neoplastic tissue present in these samples. Pathological examination of the NSCLC samples with DNA methylation profiles similar to the normal lung samples revealed either low tumor content (10 to 30%) or a very heterogeneous composition of tumor cells in the samples. These co-clustering cancer samples and cancer-free lung samples were therefore excluded from further analyses.

Our first goal was to identify the genes involved in early events in tumor-specific methylation. As a result, our screening uncovered some well-known methylation markers, some of which have tumor suppressor activity: CDKN2A, MGMT, GATA4, HOXA7, HOXA9, RUNX3, SFRP1. Most of the identified genes are novel methylation markers for NSCLC, although some of these have been described as methylated in other cancers. LGALS12 is a candidate tumor suppressor that is able to arrest the cell cycle and inhibit the proliferation of several cancer cell lines [15]. In breast cancer, LGALS12 has been found downregulated in the malignant tissue [16].

The second goal of our study was to correlate the methylation level changes with the gene expression data. Using Pearson’s analysis, we were able to find the expected inverse correlation between the methylation levels and gene expression values for 378 of 869 CpG sites (43.5%). The highest inverse correlation values between hypermethylated CpGs and expression values were found for the AGER (−0.78), EPOR (−0.65) and AQP1 (−0.63) genes. The mean distance of TSS of inversely correlated genes was −65 bp (range −1498; 917 bp) compared to −158 bp (range -1474; 818 bp) of positively correlated CpGs (p-value 0.02, students t-test). The range of CpGs is probably affected by the fact that Illumina’s Infinium HumanMethylation27 microarray covers CpGs that are located predominantly in the vicinity of promoter regions and not in the gene body or 3′UTR. Aquaporin 1 (AQP1) has been reported to be hypermethylated and downregulated in NSCLC [17]. AGER, which encodes a receptor of the immunoglobulin superfamily, has been reported to be downregulated in NSCLC [18]. The highest inverse correlation values for hypomethylated CpG sites and expression values were found for MB (−0.63), ADA (−0.60) and MAGEA6 (−0.60) genes. Myoglobin (MB) is associated with tumor progression and helps to overcome hypoxia in cancer cells [19]. Hypermethylation and/or the downregulation of some of the genes identified in our study (e.g. ALDH1A2, HOXA5, MT1E, SOX17) have been reported in other cancers [20], [21], [22], [23], but not yet in lung cancer. It is hypothesised that RASSF1 acts as a tumor suppressor in lung cancer progression [24]. Among the hypomethylated and upregulated genes, the most frequently reported gene was SERPINB5 (Maspin). Overexpression of SERPINB5 has been associated with cancer progression and a poor prognosis in lung cancer [25].

We can assume that genes with an inverse correlation have a higher likelihood of being regulated by methylation. However, many genes probably become methylated randomly during carcinogenesis, and this does not necessarily have a steady state effect on gene expression levels.

In the case of different histology groups, we were able to find an inverse correlation between the differentially methylated CpG sites and gene expression values for 337 of 780 CpG sites (43.2%). The highest inverse correlation values were for ACOX2 (−0.75), ARSE (−0.70) and SLC39A4 (−0.68), which have not been associated with NSCLC histological subtypes before. Of the inversely correlated genes, it has been reported that PIGR, MUC1 and FOLR1 are downregulated in SCC compared to AC [26], [27], [28], which is consistent with our results.

Analysis using IPA software revealed that the dominant functions of the differentially methylated genes were cell-to-cell signaling and interaction, DNA replication and repair, cellular growth and proliferation, cell death, cancer, inflammatory response, etc. A number of gene functions, such as cell growth, proliferation and cell death, are directly involved in cancer progression, but the immune system also plays an important role in fighting cancerous cells. It is also widely known that deficiencies in pathways of DNA repair and damage control are responsible for most or even all human cancers. After including the known indirect relations, the most prominent gene network revealed was related to TNF (Figure S5). TNF has a dual role in tumor biology. It is a cytokine with well-known anticancer properties, but may also promote cancer development and progression [29]. Hypomethylated genes in the TNF network were cytokines (CCL3, CCL4, CCL7, CCL8, CCL22, IL21, IL17A, EBI3) that can either stimulate or inhibit tumor growth and progression. The TNF network also includes the well-known potent antiapoptotic gene BCL2, which was also mostly hypomethylated in our NSCLC samples. ALDH1A3 (aldehyde dehydrogenase 1 family, member A3) indirectly regulated by TNF plays a role in the detoxification of aldehydes generated by alcohol metabolism and lipid peroxidation. Hypomethylation of this gene was related to a worse prognosis in our study cohort (Figure S6).

We found that TP73 had three differentially methylated CpGs, two of which were hypomethylated in the tumor tissues and located near the TSS of its shorter isoforms. The hypermethylated CpG was located upstream of the TSS of the full-length mRNA isoform. We measured the expression of different isoforms using qPCR analysis, but did not find any statistically significant differences (p-value 0.36, paired t-test) in the tumor samples, although the long isoform was expressed at a slightly lower level than the short isoform. We performed a Kaplan-Meier survival test for CpG sites, for which we divided their methylation values into 3 groups: low (0–0.25), medium (0.25–0.75) and high (0.75–1). As a result, we found 10 CpGs in 10 genes whose methylation level differs in different survival groups (Figure 4). UGT1A7 is an enzyme involved in the metabolism of (pre)carcinogens present in tobacco smoke. Precarcinogens and their metabolites are considered to play an important role in the carcinogenesis of the tobacco smoke-related cancers [30]. It has been shown that polymorphisms in the UGT1A7 gene are associated with lung cancer, suggesting that these polymorphisms reduce enzymatic activity [31]. A high methylation level could also affect UGT1A7 activity and cause a poor prognosis for NSCLC patients. CYP1A1 (belonging to cytochrome P450 superfamily) catalyzes many reactions involved in drug metabolism, also the conversion of polycyclic aromatic hydrocarbons into reactive metabolites and detoxifications of environmental carcinogens. It has been shown that CYP1A1 is hypermethylated in lung cancer samples compared to normal lung samples, and this was associated with reduced mRNA levels [32]. In our study, higher methylation levels were associated with poor survival of lung cancer patients that supports the hypothesis that CYP1A1 may have protective role in cancer progression. Other genes found in survival analysis have not been reporter to be involved in lung cancer patients’ survival.

Using a different type of survival analysis, where the differential methylation analysis was performed by combining Cox proportional hazard analysis and the Wilcoxon rank-sum test, we were able to analyze 12 patients with a survival of less than 24 months and 15 patients who survived 60 months or more after surgery. This analysis of the different survival groups revealed 15 differentially methylated genes. RTEL1, the regulator of telomere elongation helicase 1, seems to be the most functionally interesting one of these. This is an ATP-dependent DNA helicase required to suppress inappropriate homologous recombination, thereby playing a central role in DNA repair and in the maintenance of genomic stability. RTEL1 was found to be hypermethylated in our poor survival group.

Comparing non-smokers (n = 3, 6.4%) with smokers (n = 44, 93.6%), four differentially methylated CpG-sites related to three genes, hypermethylated in the non-smokers group were found: CXorf38, MTHFD2 and TLL2. Missing statistically significant difference in methylation levels between the different levels of tobacco smoking could indicate a poor reliability of the pack-years’ data obtained. MTHFD2 has been shown to have a higher expression in smokers, favouring rapid cell growth. [33]. TLL2 is a zinc-dependent metalloprotease. Expression of metalloproteinases is required for cell transformation, and this correlates with tumor progression [34].

By comparing genome-wide changes in the DNA methylation of normal and lung cancer cells, we were able to gain insight into the complexity of the methylation program required for cells to become fully malignant. As a result, we found a panel of genes that distinguish NSCLC cells from adjacent normal lung cells and also squamous cell carcinoma from adenocarcinoma. The analysis revealed a set of differentially methylated CpG sites that appear to regulate gene expression and another set that had affected the survival of the patients. These newly-identified methylation markers are candidates for the further molecular screening of NSCLC. In order to confirm these markers of NSCLC carcinogenesis, additional studies and validations are needed. From the results of our work and from previous findings, we can assume that altered DNA methylation is an early event in NSCLC.

Materials and Methods

Ethics Statement

The Ethics Committee of Human Studies, University of Tartu, has approved the study and a written consent was obtained from the study subjects.


We analyzed Union for International Cancer Control (UICC) stage I NSCLC samples [35] from 48 patients and macroscopically cancer-free “normal” lung control samples from 18 patients. All the specimens had been isolated during lung surgery at Tartu University Hospital, Estonia. The patients with adenocarcinoma (n = 6, 12.5%) and its subtype bronchioloalveolar carcinoma (n = 10, 20.8%) were analyzed as one group (n = 16, 33.3%). The remaining 32 (66.7%) of the analyzed patients had squamous cell carcinoma. The age range in the study group was 41–80 years (mean age in males n = 40, 66.2 years and in females n = 8, 65.5 years) (Table S3). The patients did not undergo any preoperative chemo- or radiotherapy.

At surgery, tissue specimens of appropriate size (1–2 cm3) were cut from tumorous and morphologically tumor-free lung tissue. One half of each sample was fixed in formalin and used for pathological examination. The other half of each specimen was snap frozen and stored at −80°C until use. Control samples were obtained at a site distant from the removed tumor and confirmed to be tumor-free by the same pathologist.

DNA Extraction and Bisulfite Modification

DNA was extracted from 50 mg of tumor and matching tumor-free lung tissue with the Dneasy® Blood & Tissue kit (Qiagen GmbH., Hilden, Germany) and with the Nucleospin® Tissue kit (Macherey-Nagel GmbH., Düren, Germany). DNA yield and purity were determined using the NanoDrop® ND1000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA). From each sample, 500 ng of genomic DNA was bisulfite modified using the EZ DNA Methylation™ Kit (Zymo Research, Orange, CA) according to the manufacturer’s recommendations.

Methylation Validation by Sanger Sequencing

For methylation chip validation 11 genes were chosen, five of these were genes from survival analysis and the remaining six were genes that distinguished between cancer and normal tissue (Primers used in study showed in Table S4). Primers for bisulfite-treated DNA PCR were designed using MethPrimer [36]. A 20 µl PCR was carried out in 80 mM Tris-HCl (pH 9,4–9,5), 20 mM (NH4)2SO4, 0,02% Tween-20 PCR buffer, 3 mM MgCl2, 1X Betaine, 0.25 mM dNTP mix, 2 units of Hot-start Taq polymerase, 50 pmol of the forward primer, 50 pmol of the reverse primer, and 20 ng of bisulfite-treated genomic DNA. PCR cycling conditions were 95°C 15 min for enzyme activation, 95°C 30 sec, 62°C 45 sec, 72°C 120 sec for 17 cycles, touchdown by −0.5°C for every cycle and 95°C 30 sec, 52°C 30 sec, 72°C 120 sec for 21 cycles. Sequencing was done as a service by the Core Facility of Estonian Biocenter. We analyzed sequencing traces with Mutation Surveyor software (Softgenetics, State College, PA, USA) and R statistical computing software (http://www.r-project.org/).

RNA Extraction and Gene Expression Analysis

Detailed description of the RNA extraction and gene expression analysis process is given in the recent paper [14].

10 genes and eight sample pairs (tumor and adjacent normal sample) were used to validate the microarray data. For quantitative RT-PCR (qPCR), cDNAs were synthesized from 700 ng of total RNA using the First Strand cDNA Synthesis kit (Fermentas, Vilnius, Lithuania) and oligo dT primers according to the manufacturer’s protocol. Triplicate qPCR reactions were performed in 384-well plates using SYBR Green ROX mix (ABGene, Epsom, UK or Fermentas, Vilnius, Lithuania) and ABI 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA). Data were analyzed using the SDS 2.2.2 (Applied Biosystems) and R statistical computing software (http://www.r-project.org/).

The geometric mean expression of two reference genes (ESD and S18RNA) was used as a reference. Expression fold change between normal and tumor sample were calculated using 2-ΔΔCt method. Pearson correlation analyses were used to assess the accordance between fold changes identified by qRT-PCR and array experiments.

In addition, qRT-PCR was used to determine the TP73 expression level. Primers used for the TP73 qPCR amplifications are listed in Table S5.

DNA Methylation Analysis

Methylation analysis was performed using Infinium® HumanMethylation27 RevB BeadChips (Illumina Inc.). The assay covers 27,578 CpGs in 14,495 genes located predominantly in CpG islands within proximal promoter regions, between 1.5 kb upstream and 1 kb downstream of the transcription start sites (TSS). A CpG island in this assay is defined as a nucleotide sequence of (1) 200 bp or greater in length, (2) 50% or greater in GC-percent, and (3) 0.60 or greater in the ratio of observed CpG sites over expected CpG sites in that region [10]. The HumanMethylation27 beadchips also cover CpG sites in the regulatory regions of 1,000 well-known cancer genes, 150 differentially methylated genes in various cancers and 110 miRNA genes. The chips were processed according to the manufacturer’s standard protocols.

We performed cluster analysis of the methylation profiles by using the Limma program of Bioconductor package in R statistical computing software (www.bioconductor.org). Prior to analysis we quantile normalized the methylation data to eliminate systematic differences between the chips. The analysis was performed using t-tests with an empirical Bayes’ correction from the Bioconductor Limma package [37]. The differentially methylated genes were clustered hierarchically and visualized using a heatmap.

All the methylation differences (differences between the Beta-values representing the calculated level of methylation from 0 to 1, alternatively 0% to 100% for each analyzed CpG) were identified using a false discovery rate (FDR) corrected p<0.05 and ≥0.136 mean methylation level difference that was previously shown to detect differences with at least 95% confidence [38]. For Kaplan-Meier survival analysis, we divided the Beta values into low (0–0.25), medium (0.25–0.75) and high (0.75–1) methylation group and performed a log-rank test to assess the difference in survival between the groups. We corrected these p-values using FDR and used 0.05 as the significance level.

Methylation Related to Gene Expression Analysis

Within the available data, we calculated Pearson’s correlations between the CpG site methylation levels and their respective gene expression levels (Table 2, Table S1 and S2).

Table 2
Statistically significant Pearson’s correlations between differentially methylated CpG sites and gene expression values across 48 lung cancer samples and the control samples with available gene expression data.

We computed the permutation-based p-values by permuting individuals and recalculating the median gene expression levels 10,000 times. All CpGs and genes represented by both the methylation and gene expression arrays were included in permutations. Three genes (AGER, NR0B2, PPP1R14D) yielded two significant correlations; only the strongest correlations are shown (Table 2).

Supporting Information

Figure S1

Differential DNA methylation between NSCLC and normal lung samples. DNA methylation profiles for 48 NSCLC samples and 18 normal lung samples are shown. We detected 496 CpGs hyper- and 373 CpGs hypomethylated in NSCLC. Methylation values (Beta-values) are represented as row Z-scores for each gene. Heatmap was generated with unsupervised 2D hierarchical cluster analysis. Red indicates high methylation and blue indicates low methylation relative to the row mean.


Figure S2

Differential DNA methylation between squamous cell carcinoma and adenocarcinoma. DNA methylation profiles for 16 adenocarcinoma (AC) samples and 32 squamous cell carcinoma (SCC) samples are shown. We detected 263 hypermethylated CpG sites and 513 hypomethylated CpG sites comparing SCC to AC. Methylation values are represented as row Z-scores for each gene. Red indicates high methylation and blue indicates low methylation relative to the row mean.


Figure S3

Scatterplots showing the correlation between beta values determined by Illumina methylation arrays and Sanger bisulfite sequencing. Shown are the best-fitting line, Pearson’s correlation coefficient and p-value of correlation tests. The mean correlation between two methods was 83% (range 48.9%; 97.4%).


Figure S4

Scatterplots showing the correlation between Illumina microarray and qRT-PCR measurements. On the x-axis is quantile-normalized and logarithmic fold change (log2TU-log2N) on the microarray for eight sample pairs. On the y-axis is ΔΔCt value for same sample pairs. Shown are the best-fitting line, Pearson’s correlation coefficient and correlation test p-value.


Figure S5

TNF network. The in silico functional and interaction analysis of differently methylated genes was performed using the Ingenuity Pathway Analysis (IPA) software. The most prominently represented gene network was related to tumor necrosis factor. The genes that were hypermethylated in NSCLC are shown with a red background, and the genes that were hypomethylated in NSCLC are shown with a green background. The depicted interactions are mostly indirect.


Figure S6

Boxplots of differentially methylated CpGs in different survival groups. The 18 CpGs in 15 genes had statistically different methylation (p-value <0.05, Beta-value ≤0.136) between patients with 1 to 24 months survival (n = 12) vs patients with 60 months and longer survival (n = 15). Methylation values are also shown for patients with 25–59 months survival and for normal lung tissue.


Table S1

Differentially methylated CpGs and genes in stage I NSCLC compared to cancer-free lung control samples. Mean Beta represents the methylated signals divided by the sum of methylated and unmethylated signals for each analyzed CpG. Beta-diff. was calculated by subtracting the Mean Beta value of a cancer-free lung from the Mean Beta value of stage I NSCLC samples.


Table S2

Differentially methylated CpGs and genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (AC) samples. Mean Beta represents the methylated signals divided by the sum of methylated and unmethylated signals for each analyzed CpG. Beta-diff. was calculated by subtracting the Mean Beta value of AC from the Mean Beta value of SCC.


Table S3

A detailed overview of the patient cohort involved in our study.


Table S4

PCR primers used in methylation validation with Sanger sequencing.


Table S5

Quantitative real-time PCR primers used for TP73 gene isoform analysis.



We express our sincere appreciation to all the patients participating in the study.


Competing Interests: The authors have declared that no competing interests exist.

Funding: The study was supported by the European Union through European Regional Development Fund, Centre of Excellence in Genomics and project 245536 OpenGene. Additional support was obtained from Estonian Ministry of Education and Science core grant SF0180142s08, Estonian Science Foundation grants 7473, 9293 and University of Tartu Development Fund Project of Translational Genomics. LM has been supported by the European Union through the European Social Fund, fellowship MJD71. NT has been supported by the fellowship from Alexander von Humboldt Foundation. KL, MK and NT have received travel stipends from Kristjan-Jaak Foundation. Website of OpenGene project: www.geenivaramu.ee/opengene/. Website of Estonian Science Foundation: www.etf.ee. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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