Abstract
Background
Due to the heterogeneity of cancer, identifying differentially
methylated (DM) CpG sites between a set of cancer samples and a set of
normal samples cannot tell us which patients have methylation
aberrations in a particular DM CpG site.
Methods
We firstly showed that the relative methylation-level orderings (RMOs)
of CpG sites within individual normal lung tissues are highly stable
but widely disrupted in lung adenocarcinoma tissues. This finding
provides the basis of using the RankComp algorithm, previously
developed for differential gene expression analysis at the individual
level, to identify DM CpG sites in each cancer tissue compared with its
own normal state. Briefly, through comparing with the highly stable
normal RMOs predetermined in a large collection of samples for normal
lung tissues, the algorithm finds those CpG sites whose hyper- or
hypo-methylations may lead to the disrupted RMOs of CpG site pairs
within a disease sample based on Fisher’s exact test.
Results
Evaluated in 59 lung adenocarcinoma tissues with paired adjacent normal
tissues, RankComp reached an average precision of 94.26% for
individual-level DM CpG sites. Then, after identifying DM CpG sites in
each of the 539 lung adenocarcinoma samples from TCGA, we found five
and 44 CpG sites hypermethylated and hypomethylated in above 90% of the
disease samples, respectively. These findings were validated in 140
publicly available and eight additionally measured paired cancer-normal
samples. Gene expression analysis revealed that four of the five genes,
HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five
frequently hypermethylated CpG sites within its promoters, were also
frequently down-regulated in lung adenocarcinoma.
Conclusions
The common DNA methylation aberrations in lung adenocarcinoma tissues
may be important for lung adenocarcinoma diagnosis and therapy.
Electronic supplementary material
The online version of this article (doi:10.1186/s12967-017-1122-y)
contains supplementary material, which is available to authorized
users.
Keywords: Lung adenocarcinoma, DNA methylation, Relative methylation
level orderings, Differentially methylated CpG sites
Background
The incidence of lung adenocarcinoma is increasing worldwide. It is
widely recognized that lung adenocarcinoma, like other cancers, has
different molecular subtypes with different prognoses [[43]1, [44]2].
In cancer genomes, the frequencies of somatic mutations and copy number
variations are usually very low [[45]3, [46]4], forming a major barrier
for cancer diagnosis and therapy. On the other hand, it is also well
known that cancers are commonly characterized with cancer hallmarks
[[47]5, [48]6], and tremendous efforts have been made to identify
common biomarkers at the level of pathways or gene modules. Especially,
mutually exclusive analyses of somatic mutation and/or copy number
variation have been tried to identify some driver genes that could
jointly explain a large fraction samples of a cancer type to provide
potential targets of medicine [[49]7, [50]8]. However, a set of
mutually exclusive genes or gene modules as a cancer diagnosis and
therapy maker might be difficult for clinical applications. In contrast
to genomic aberrations, DNA methylation aberrations are widespread in
cancer genomes. Therefore, it would be interesting to analyze whether
there are epigenomic aberrations appearing in almost all patients of a
cancer such as lung adenocarcinoma.
Currently, genome-wide DNA methylation profiles are widely applied to
identify population-level differentially methylated (DM) CpG sites in
cancer tissues compared with normal controls using various statistical
methods such as the Shannon entropy based method QDMR [[51]9],
empirical Bayes model DiffVar [[52]10] and T test [[53]11]. However,
the inter-individual heterogeneity of DM CpG sites was ignored in these
approaches. Thus a novel computational tool is needed to detect which
patients have methylation aberrations in a particular CpG site. To
tackle this difficulty, some previous works discretized DNA methylation
states of CpG sites in a cancer sample through comparing with the
average methylation level in a set of normal samples [[54]12, [55]13].
However, because the DNA methylation levels of CpG sites vary across
individuals in a healthy population, this approach may easily be
affected by biological variations.
Recently, we have reported an interesting biological phenomenon that
the within-sample relative expression orderings of genes within a
particular type of normal tissues are highly stable but widely
disrupted in the corresponding cancer tissues [[56]14]. Based on this
finding, we have developed an algorithm, named RankComp, to identify
genes differentially expressed in each individual cancer tissue sample
by finding those genes whose up- or down-regulations may lead to the
disrupted relative expression orderings of genes within this disease
sample in comparison with the stable normal background [[57]14].
Importantly, the stable normal background of the relative expression
orderings of genes within a particular type of normal tissues can be
predetermined in accumulated normal samples previously measured by
different laboratories [[58]14]. Thus, it would be of interest to
evaluate whether the within-sample relative methylation-level orderings
(RMOs) of CpG sites are also highly stable in a particular type of
normal tissues but widely disrupted in the corresponding cancer
tissues. If this biological phenomenon does exist, then it would be
possible to apply the RankComp algorithm to detect DM CpG sites for
each cancer tissue compared with its own previously (usually unknown)
normal status.
In this study, through the analysis of multiple methylation datasets
for normal lung tissues, we firstly revealed an interesting biological
phenomenon that the RMOs of CpG sites within different samples of
normal lung tissues are highly stable but widely reversed in the cancer
tissues. Based on this finding, we showed that the RankComp algorithm
can accurately detect DM CpG sites in individual cancer samples from
DNA methylation data for cancer samples alone. Then, RankComp was
applied to identify DM CpG sites for each of the 539 lung
adenocarcinoma samples from The Cancer Genome Atlas (TCGA). Many CpG
sites with methylation aberrations in above 90% of lung adenocarcinoma
tissues were found and validated in 140 publicly available and eight
additionally measured paired cancer-normal samples. Gene expression
analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG
and SPARCL1, each harboring one of the five frequently hypermethylated
CpG sites within its promoters, were also frequently down-regulated in
lung adenocarcinoma.
Methods
Data and preprocessing
DNA methylation profiles for lung tissues were collected from the Gene
Expression Omnibus (GEO) [[59]15] database and The Cancer Genome Atlas
data portal ([60]http://tcga-data.nci.nih.gov/tcga/). We used a dataset
([61]GSE32861) of DNA methylation profiles for paired cancer and
adjacent normal samples to evaluate the performance of RankComp
(Table [62]1). Except the paired cancer-normal datasets, the other DNA
methylation profiles described in Table [63]1 were used to evaluate the
RMOs of CpG sites in normal and cancer tissues. The DNA methylation
profiles of 539 samples of lung adenocarcinoma were selected from TCGA
for application analysis.
Table 1.
The DNA methylation profiles analyzed in this study
Dataset Normal Tumor Platform
[64]GSE62948 28 28 27K
[65]GSE32866 27 28 27K
[66]GSE52401 244 – 450K
TCGA 24 109 27K
TCGA 32 430 450K
[67]GSE32861^a 59 59 27K
[68]Open in a new tab
^aRepresents the paired cancer-normal samples used to evaluate the
performance of Rankcomp
The DNA methylation data was measured with Illumina Human Methylation
27 Beadchip (27K array) and Illumina Human Methylation 450 Beadchip
(450K array). We focused on analyzing the 25,978 CpG sites measured by
both 27 and 450K arrays. Using methylated signal intensity (M) and
unmethylated signal intensity (U), the DNA methylation level of each
probe was calculated by M/(U + M + 100) [[69]16]. The probes were
annotated to genes according to the annotation table of 27K platform.
KEGG pathways
Data of 234 pathways covering 5981 unique genes was downloaded from the
Kyoto Encyclopedia of Genes and Genomes (KEGG) (Release 58.0) [[70]17]
for pathway enrichment analysis.
Reproducibility analysis of the stable RMOs of CpG sites in normal tissues
The RMO of two CpG sites, A and B, was denoted as A > B or A < B if the
site A had a higher or lower methylation level than the site B. The RMO
of two CpG sites was defined as stable in normal tissues if the same
RMO existed in at least 99% of the samples, allowing 1% detection error
rate.
For a particular type of normal tissues, we respectively detected two
lists of stable CpG site pairs in two independent datasets to evaluate
the reproducibility of the stable CpG site pairs. The concordance score
of the two lists of stable CpG site pairs was calculated as s/k. k was
the number of the stable CpG site pairs shared by the two lists, among
which s pairs had the same RMO patterns (A > B or A < B) in the two
lists. The probability of a concordance score s/k observed by chance
was calculated according to the cumulative binomial distribution model
[[71]18].
[MATH: P=1-∑i=0
s-1k<
mrow>iPei1-Pek-1 :MATH]
1
where Pe (Pe = 0.5) is the probability of a pair of CpG sites having
the same RMO in two lists by chance.
Reproducibility analysis of the reversal RMOs of CpG sites in cancer tissues
For stable RMOs in normal samples, we applied Fisher’s exact test to
detect gene pairs with significantly higher frequencies of reversal
RMOs in cancer samples than what expected by random chance using two
independent datasets, respectively, defined as reversal CpG site pairs.
The p-values were adjusted using the Benjamini–Hochberg procedure
[[72]19]. The concordance of two lists of reversal CpG site pairs
identified from two independent datasets was also evaluated by the
cumulative binomial distribution model as described above.
The RankComp algorithm for individualized differential methylation analysis
Then, we used RankComp to identify differentially methylated CpG sites
in a given cancer sample in comparison with its previously normal
state, based on the highly stable CpG site pairs with consistent RMOs
in at least 99% of previously accumulated normal samples measured by
different laboratories. The detail of the RankComp algorithm was
described in Wang et al. [[73]14]. Briefly, for each cancer sample, CpG
site pairs with reversal ordering in comparison with their stable
ordering in normal samples were determined as reversal CpG site pairs
by RankComp. Then, the Fisher’s exact test was used to determine
whether a CpG site Ci was differentially methylated in a given cancer
sample by testing the null hypothesis that the proportion of reversal
CpG site pairs supporting the hypermethylation of Ci was equal to the
proportion of reversal CpG site pairs supporting the hypomethylation of
Ci. For a given CpG site Ci, if its ordering was stably lower (or
higher) than that of CpG site Cj in normal samples but this ordering
was reversed in a cancer sample, then this reversal CpG site pair could
support hypermethylation (or hypomethylation) of Ci in this sample. If
Ci itself is not changed in methylation level, the effect of the
methylation changes of other CpG sites on the upward or downward shift
in the rank of Ci is assumed to be a random event.
Performance evaluation of RankComp
Methylation profiles for paired cancer and adjacent normal tissues were
downloaded from TCGA and GEO to evaluate the performance of RankComp.
We used RankComp to detect DM CpG sites in individual cancer samples
using DNA methylation data on cancer samples alone. After identifying
DM CpG sites for each cancer sample, we evaluated the precision of DM
CpG sites identified for this cancer sample using the observed
methylation level differences (hyper- or hypomethylation) between the
cancer sample and its paired adjacent normal sample as the golden
standard. The underlying assumption of this evaluation is that the
previously normal state of a cancer tissue could be approximately
represented by the adjacent normal tissue of the cancer tissue. For a
cancer sample, if the aberration states of DM CpG sites identified by
RankComp are consistent with the golden standard, then they are defined
as true positives (TP); otherwise, false positives (FP). The precision
is calculated as positive predictive value: TP/(TP + FP).
To ensure the association between the individualized CpG sites and
cancer, the precision analysis for each cancer sample was restricted to
the CpG sites that were found to be differentially methylated at the
population-level. Thus, we applied T-test to detect population-level DM
CpG sites between cancer samples and normal samples using two
independent datasets for each cancer, respectively. The
Benjamini–Hochberg procedure was used to control the false discovery
rate (FDR). The concordance score between the two lists of DM CpG sites
and its statistical significance were calculated by the same method as
described above. We defined the DM CpG sites that were consistently
detected from the two independent datasets as the population-level DM
CpG sites for each cancer.
DNA methylation and gene expression profiling
Eight paired samples of lung adenocarcinoma tissues and adjacent normal
tissues were used for DNA methylation and gene expression detection.
Fresh frozen cancer tissue samples were obtained from surgically
removed lung specimens. Tumor samples were macro dissected to ensure
the purity of the tumor. From the same patient, adjacent normal tissue
samples were collected from resected, located approximately 3.5–5 cm
away from the tumor site. All samples were collected from the operating
room immediately after surgical resection. Samples were fresh frozen
and were shipped on ice for subsequent DNA extraction and RNA
extraction. This study was approved by the institutional review boards
of all participating institutions, and written consent forms were
obtained from all participants.
DNA was extracted from frozen tissue samples using the Qiagen^® Genomic
DNA Mini Kit, as described by the manufacturer. DNA quantification was
performed using a NanoDrop 2000 UV–Vis Spectrophotometer (Thermo). The
bisulfite conversion of DNA was conducted using the Zymo bisulfite gold
kit. The Infinium Methylation 450K assay was performed according to
Illumina’s standard protocol. Processed methylation chips were scanned
using an iScan reader (Illumina). All samples were processed at the
same time to avoid chip-to-chip variation. Infinium Methylation data
were processed using the Methylation Module of the GenomeStudio
software package (v. 2011.1). For quality control, methylation measures
with a detection p-value >0.05 were removed. The data was initially
normalized using internal controls in the GenomeStudio software and has
been submitted to GEO ([74]GSE85845).
Total RNA was extracted using Trizol reagent (Invitrogen) according to
the manufacture’s protocol. The purity and concentration of RNA was
determined by Nano Drop ND-1000 spectrophotometer according to
OD260/280 reading. Each time point has three replicates. Total RNAs
were hybridized using mRNA + lncRNA Human Gene Expression Microarray
V4.0 (4 × 180K format, CapitalBio Corp, Beijing, China), which contains
probes interrogating about 34,235 human mRNAs assembled from databases
such as UCSC [[75]20], RefSeq [[76]21] and Ensembl [[77]22]. The gene
expression profiles have been submitted to GEO ([78]GSE85841).
Results
Highly stable RMOs of CpG sites in normal lung tissues are widely disrupted
in cancer tissues
We collected a total of 355 samples for normal lung tissues, including
79 samples derived from three datasets assayed by the 27K array and 276
samples derived from two datasets assayed by the 450K array
(Table [79]1). Here, we only analyzed the CpG sites assayed by both the
27 and 450K arrays.
We defined two CpG sites as a stable CpG site pair if the two CpG sites
had identical RMO in at least 99% of the normal lung tissue samples.
Accordingly, a list of 229,037,151 stable CpG site pairs was identified
in the 79 normal lung tissue samples assayed by the 27K array, and a
shorter list of 173,949,484 stable CpG site pairs was identified in the
276 normal lung tissue samples assayed by the 450K array (Additional
file [80]1: Table S1). Notably, 90.62% of the stable CpG site pairs in
the shorter list were included in the longer list and 99.75% of the
overlapped CpG site pairs had the same RMOs in the two sets of the
normal lung tissue samples (binomial test, p < 2.2 × 10–16). The stable
CpG site pairs involved all the measured CpG sites. These results
suggest that the highly stable within-sample RMOs of CpG sites in
normal lung tissues can be reproducibly detected across samples
measured with different platforms. As exemplified in Fig. [81]1a,
although the DNA methylation levels of cg15778232, cg26521404 and
cg03606258 CpG sites varied greatly across different normal samples in
the [82]GSE32866 dataset, their RMOs (cg15778232 > cg26521404,
cg26521404 < cg03606258 and cg15778232 < cg03606258) were highly stable
across the normal samples.
Fig. 1.
Fig. 1
[83]Open in a new tab
An example of three CpG sites with pairwise stable RMOs in normal
samples (a) and reversal RMOs in cancer samples (b). Four paired
cancer-normal samples from [84]GSE32866 were used to show this
biological phenomenon. Red, blue and green points represent cg15778232,
cg26521404 and cg03606258 CpG sites, respectively
Among the CpG site pairs with stable RMOs in normal lung tissue, we
found 8,615,527 and 37,815,005 CpG site pairs with significantly
frequent reversal RMOs in the 165 and 430 lung cancer samples assayed
by the 27K array and 450K array (Table [85]1) (FDR < 0.05, Fisher’s
exact test), respectively. As exemplified in Fig. [86]1b, the RMO of
cg15778232 and cg26521404 was widely reversed in the lung cancer
samples ([87]GSE32866), caused by the methylation level increase of
cg26521404. The reversal CpG site pairs involved all the measured CpG
sites and averagely a CpG site participated in more than four thousands
of reversal CpG site pairs. These results suggest that the landscape of
stable CpG site pairs in normal lung tissues is widely disrupted in
lung cancer tissues.
Performance evaluation of RankComp
Here, we firstly determined DM CpG sites associated with lung cancer at
the population-level. From two independent datasets, [88]GSE62948 and
[89]GSE32866, 6,515 and 5,451 DM CpG sites in lung cancer were detected
(T-test, FDR < 0.05) (Additional file [90]2: Table S2). The two lists
of DM CpG sites had 4,159 overlaps and all of them had the same
hypermethylation or hypomethylation states in the two datasets with the
concordance score being 100% (binomial test, p < 2.2 × 10–16). These
4159 reproducible DM CpG sites were defined as the population-level DM
CpG sites for lung cancer.
Then, we used an independent dataset, [91]GSE32861 with 59 paired
cancer-normal lung samples, to evaluate the performance of RankComp in
individualizing the above defined population-level DM CpG sites. Here,
we detected DM CpG sites for each disease sample without using any of
the methylation data of its paired adjacent normal sample. The paired
adjacent normal sample of a disease sample was used for performance
evaluation only: the observed methylation level differences of the DM
CpG sites between the cancer sample and its adjacent normal sample were
taken as the golden standard. Based on the predetermined stable CpG
site pairs in the accumulated 355 normal lung tissue samples, with
FDR < 0.01, averagely 2777 DM CpG sites per disease sample were
identified and the average of the precisions of all samples was 94.26%.
However, as shown in Fig. [92]2, the precision for a sample
([93]GSM813269) was only 70.89%, which may be caused by the quality of
its paired normal tissue. In fact, the Spearman’s correlation
coefficient between the DNA methylation levels of this normal tissue
and any of the other 58 normal tissues was lower than 0.72, while the
Spearman’s correlation coefficient was larger than 0.88 between every
two of the other 58 normal tissues.
Fig. 2.
Fig. 2
[94]Open in a new tab
The precision and the number of DM CpG sites detected by RankComp for
each of the 59 lung cancer samples from [95]GSE32861
The above results suggested that RankComp can accurately find DM CpGs
in individual lung cancer tissues compared with their own previous
normal state approximately represented by their paired adjacent normal
tissues.
The CpG sites with methylation aberrations in above 90% lung adenocarcinoma
tissues
Then, we used RankComp to identify DM CpG sites for each of the 539
lung adenocarcinoma samples from TCGA. The results showed that 83.64%
of DNA methylation aberrations appeared in less than 80% of the cancer
samples, reflecting the heterogeneity nature of cancer. Interestingly,
we found five and 44 CpG sites that were hypermethylated and
hypomethylated, respectively, in more than 90% of the 539 cancer
samples (Additional file [96]3: Table S3). In the following text, we
focused on validating these CpG sites with extremely high frequencies
of methylation alternations in cancer tissues.
Firstly, we validated these CpG sites using 140 paired samples of
cancer-normal tissues collected from three datasets ([97]GSE32861, TCGA
and [98]GSE62948, Additional file [99]4: Table S4). As shown in
Table [100]2, in more than 95% of the 140 paired samples, the
methylation levels of the five CpG sites with high hypermethylation
frequencies were higher in the cancer tissues than in the corresponding
adjacent normal tissues. Similarly, in more than 90% of the 140 paired
samples, 43 of the 44 hypomethylated CpG sites had lower methylation
levels in the cancer tissues than in the corresponding adjacent normal
tissues (Additional file [101]5: Table S5).
Table 2.
The hypermethylation frequencies of five CpG sites in disease samples
CpG site Gene symble Frequency^1 (%) Frequency^2 (%)
cg05050341 ENG 97.86 100
cg12111714 ATP8A2 97.86 100
cg19466563 SPARCL1 98.57 87.50
cg19797376 TAL1 98.57 100
cg26521404 HOXA9 98.57 100
[102]Open in a new tab
For a gene corresponding to a CpG site, Frequency^1 and Frequency^2
represent the hypermethylation frequencies in the 140 publicly
available and eight additionally measured paired cancer-normal samples,
resepectively
To further validate these CpG sites with extremely high frequencies of
methylation alternations, we measured DNA methylation profiles for
eight paired cancer-normal lung tissues using Illumina Human
Methylation 450 Beadchip. Four of the five frequently hypermethylated
CpG sites were hypermethylated in all the eight paired cancer-normal
samples and the left one CpG site was hypermethylated in seven paired
cancer-normal samples (Table [103]2). For the 44 hypomethylated CpG
sites with high frequencies, 39 CpG sites were validated in at least
seven paired cancer-normal samples (Additional file [104]5: Table S5).
Genes deregulated by the frequently hypermethylated CpG sites
Because it is well known that DNA hypomethylation is weakly correlated
with up-regulation of gene expression [[105]23], we focused on
analyzing the five hypermethylated CpG sites with above 90% frequencies
in lung adenocarcinoma tissues. We defined a gene as a hypermethylated
gene in a disease sample if at least one CpG site within its promoter
region was identified as a hypermethylated and no CpG site was
identified as hypomethylated. Accordingly, we found five genes (HOXA9,
TAL1, ATP8A2, ENG and SPARCL1) corresponding to the five CpG sites had
above 90% hypermethylation frequencies in the 539 lung adenocarcinoma
samples from TCGA.
Then, we investigated the expressions of the five genes using 82 paired
cancer-normal samples with gene expression profiles ([106]GSE32867 and
TCGA, Additional file [107]6: Table S6). Here, a gene was defined as
down-regulated in the cancer sample if its expression level in the
cancer sample was lower than that in the corresponding adjacent normal
sample. We found that SPARCL1, ENG, TAL1, ATP8A2 and HOXA9 were
down-regulated in 99, 94, 89, 65 and 49% of the 82 cancer samples
(Table [108]3). Thus, at least three genes, SPARCL1, ENG and TAL1,
might be frequently and strongly silenced by the frequently
hypermethylated CpG sites within their promoter regions. To further
validate the results for these three genes, we measured gene expression
profiles for eight paired cancer-normal lung tissues using microarray.
The gene expression levels of SPARCL1 and TAL1 were down-regulated in
all the eight cancer tissues compared with their paired adjacent normal
tissues, while ENG was down-regulated in six of the eight cancer
tissues (Table [109]3).
Table 3.
Down-deregulation frequencies of the five genes regulated by the five
frequently hypermethylated CpG sites
CpG site Gene symble Frequency^1 (%) Frequency^2 (%)
cg05050341 ENG 93.90 75.00
cg12111714 ATP8A2 64.63 62.50
cg19466563 SPARCL1 98.78 100
cg19797376 TAL1 89.02 100
cg26521404 HOXA9 48.78 50.00
[110]Open in a new tab
For a gene corresponding to a CpG site, Frequency^1 and Frequency^2
represent the down-regulation frequencies of the gene in the 82
publicly available and eight additionally measured paired cancer-normal
samples, resepectively
The above results suggested that SPARCL1, ENG and TAL1 could be
potential tumor suppressors of lung adenocarcinoma and thus could be
drug targets or diagnostic biomarkers for lung adenocarcinoma. ENG is a
candidate tumor-suppressor gene of esophageal squamous cell carcinoma
[[111]24]. It has been found that the silencing of ENG by
hypermethylation in the promoter region could be reactivated by
demethylation [[112]25] that can reduce colony formation efficiency and
suppress invasion efficiency and tumorigenicity of cancer cells
[[113]24]. Thus, the drug target potential of ENG for lung
adenocarcinoma deserves further studies. SPARCL1, a known tumor
suppressor of colon cancer [[114]26], is an anti-adhesive extracellular
matrix gene with anti-proliferative effects mediated through cell–cell
adhesion [[115]27, [116]28]. TAL1 is a member of the basic
helix-loop-helix family of transcription factors and its downregulation
could suppress the activity of the tumor suppressor TGF-β in the early
phase of tumor through downregulating KDR expression [[117]29]. Thus,
SPARCL1 and TAL1 might also be important tumor suppressors of lung
adenocarcinoma and it would be interesting to study whether their
silencing could be reactivated by demethylation and whether the
reactivation could suppress cancer cells.
Discussion
The inter-individual heterogeneity of cancer forms a major barrier for
cancer diagnosis and therapy. Thus, it would be interesting to find
common molecular aberrations appearing in almost all patients of a
cancer such as lung adenocarcinoma. In this work, we have revealed an
interesting biological phenomenon that the within-sample RMOs of CpG
sites remain highly stable in normal lung tissues. This might be an
epigenetic mechanism to keep genes functioning concertedly and robustly
in the normal lung tissues in the presence of various perturbations.
Another interesting biological phenomenon revealed in this study is
that the stable RMOs in normal tissues are widely disrupted in the
corresponding cancer tissues, reflecting the nature of cancer as a
systems disease. These intrinsic biological phenomena provide the basis
for identifying DM CpG sites for an individual cancer sample by
exploiting the widely disrupted epigenetic landscape within this
individual disease sample. In fact, our analyses showed that the
rank-based RankComp algorithm can accurately detect DM CpG sites at the
individual-level, providing us a novel tool to dissect the
inter-individual heterogeneity of cancer patients. Then, through the
individual-level analysis of DM CpG sites in 539 lung adenocarcinoma
samples, we found five and 44 CpG sites hypermethylated and
hypomethylated in above 90% of the disease samples, respectively. These
findings were further validated in 140 publicly available and eight
additionally measured paired cancer-normal samples. These common DNA
methylation aberrations found in lung adenocarcinoma tissues may be
important for lung adenocarcinoma diagnosis and therapy. Especially, we
found three genes (SPARCL1, ENG and TAL1) harboring frequently
hypermethylated CpG sites within their promoters were also frequently
down-regulated in lung adenocarcinoma. The results suggested that these
genes might function as tumor suppressors and thus might be drug
targets or diagnostic biomarkers of lung adenocarcinoma.
In addition, after identifying DM genes for a disease sample, we could
detect pathways significantly enriched with hypermethylated and
hypomethylated genes respectively for this disease sample. The
application of the individual-level pathway analysis to 539 samples of
lung adenocarcinoma revealed that the neuroactive ligand-receptor
interaction pathway, known to be involved in cancer development
[[118]30, [119]31], was significant in 96.48% of the 539 samples, while
the calcium signaling pathway, also known to be involved in cancer
[[120]32–[121]34], was significant in 88.87% of the 539 samples
(FDR < 0.05, Additional file [122]7: Figure S1). Therefore, these
pathways significantly enriched with DM genes in almost all
adenocarcinoma tissues deserve our future investigation.
Notably, the majority of the DNA methylation aberrations are not
universally appearing in patients of lung adenocarcinoma. For example,
83.64% of the CpG sites with DNA methylation aberrations appeared in
less than 80% of all the 539 lung adenocarcinoma tissues from TCGA,
reflecting the heterogeneity nature of lung adenocarcinoma. Some of
these methylation aberrations might be cancer subtype specific and
associated with distinct clinical outcomes. As a case study, we
identified potential methylation aberration signatures correlated with
the prognosis of stage I lung adenocarcinoma patients after surgical
resection using all the 120 TCGA samples of stage I lung adenocarcinoma
patients with surgical resection. For each CpG site, we classified the
patients into two groups according to whether they had or had not the
methylation aberration of this CpG site and then compared their
relapse-free survival times after surgical resection (FDR < 0.1,
univariate Cox model). Finally, we found seven genes (ALX4, PDLIM1,
FST, EDIL3, ITPKB, APC and UCN) that were significantly associated with
the prognosis of patients. For instance, we found that APC was
hypermethylated in 50% of the 120 patients and these patients had
significantly shorter relapse-free survival time (p < 0.0014,
univariate Cox model) than the other patients without APC
hypermethylation. This result supported a previous report that
hypermethylation of APC could be associated with poor prognosis of
NSCLC including lung adenocarcinoma [[123]35]. Moreover, ALX4
[[124]36], PDLIM1 [[125]37], CAP2 [[126]38] and EDIL3 [[127]39] have
also been found to be correlated with the prognosis of cancer. Thus,
the ability of these methylation aberrations for predicting the
prognosis of stage I lung adenocarcinoma patients after surgical
resection deserves future studies.
In this paper, we only analyzed the CpG sites within gene promoter
regions, assayed by both the 27K and 450K arrays, but were unable to
analyze the huge number of CpG sites located within gene bodies or
intergenic regions due to the extensive computational burden. Thus, an
optimized algorithm needs to be developed so that the genome-wide CpG
sites assayed by 450K or whole-genome bisulfite sequencing could be
taken into account. In addition, in order to extend the application
scope of the RankComp algorithm, it is necessary to further evaluate
the cross-platform properties of RMOs in samples measured by other
platforms such as the whole-genome bisulfite sequencing platform.
In summary, the individual-level analysis of DM CpG sites can help us
identifying universal or subtype-specific DNA methylation biomarker for
cancer prevention, treatment and diagnosis [[128]40, [129]41].
Conclusions
Using multiple DNA methylation datasets for normal lung tissues, we
firstly revealed that the RMOs of CpG sites within different samples of
normal lung tissues are highly stable but widely reversed in the
corresponding cancer tissues. This biological phenomenon allows us to
exploit the within-sample RMOs of CpG sites to accurately detect
individual-level DM CpG sites using RankComp. Additionally, we used
RankComp to identify DM CpG sites for each of the 539 lung
adenocarcinoma samples from TCGA and identified many common DNA
methylation aberrations in lung adenocarcinoma tissues, which were
validated by paired cancer-normal samples. Using gene expression
analysis, we further identified abnormal expression genes among genes
with common DNA methylation aberrations. These common genes might be
used as candidate biomarkers for lung adenocarcinoma diagnosis and
therapy.
Authors’ contributions
ZG, FH and HDY conceived and designed the overall study. QZG
participated in study design and revised the manuscript. HDY, JH and
YQL performed the computational experiments. JZ, HDL, HPL, and YYG
participated in study design. HDY, YYG and ZG wrote the manuscript. All
authors read and approved the final manuscript.
Acknowledgements